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Review

Spatial Validation of Spectral Unmixing Results: A Systematic Review

by
Rosa Maria Cavalli
Research Institute for Geo-Hydrological Protection (IRPI), National Research Council (CNR), 06128 Perugia, Italy
Remote Sens. 2023, 15(11), 2822; https://doi.org/10.3390/rs15112822
Submission received: 30 March 2023 / Revised: 22 May 2023 / Accepted: 24 May 2023 / Published: 29 May 2023
(This article belongs to the Special Issue New Tools or Trends for Large-Scale Mapping and 3D Modelling)

Abstract

:
The pixels of remote images often contain more than one distinct material (mixed pixels), and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared effort has enabled the development of techniques for retrieving information from mixed pixels. The most analyzed, implemented, and employed procedure is spectral unmixing. Among the extensive literature on the spectral unmixing, nineteen reviews were identified, and each highlighted the many shortcomings of spatial validation. Although an overview of the approaches used to spatially validate could be very helpful in overcoming its shortcomings, a review of them was never provided. Therefore, this systematic review provides an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing the papers that were published not only in 2011 and 2010, but also in 1996 and 1995. The key criterion is that the results of the spectral unmixing were spatially validated. The Web of Science and Scopus databases were searched, using all the names that were assigned to spectral unmixing as keywords. A total of 454 eligible papers were included in this systematic review. Their analysis revealed that six key issues in spatial validation were considered and differently addressed: the number of validated endmembers; sample sizes and sampling designs of the reference data; sources of the reference data; the creation of reference fractional abundance maps; the validation of the reference data with other reference data; the minimization and evaluation of the errors in co-localization and spatial resampling. Since addressing these key issues enabled the authors to overcome some of the shortcomings of spatial validation, it is recommended that all these key issues be addressed together. However, few authors addressed all the key issues together, and many authors did not specify the spatial validation approach used or did not adequately explain the methods employed.

1. Introduction

1.1. Background

A pixel that contains more than one “land-cover type” is defined as a mixed pixel, and its spectrum is formed by combining the spectral signatures of these “land-cover types” [1]. The presence of mixed pixels in the image constrains the techniques that can be carried out to analyze, characterize, and classify the remote sensing images [2,3]. To retrieve mixed-pixel information from remote sensing images, a shared research effort allowed developing several methods (e.g., spectral unmixing, probabilistic, geometric-optical, stochastic geometric, and fuzzy models [1]). However, the literature shows that, for over 40 years, spectral unmixing has been the most commonly used method for discrimination, detection, and classification of superficial materials [4,5,6].
The spectral unmixing was defined as the “procedure by which the measured spectrum of a mixed pixel is decomposed into a collection of constituent spectra, or endmembers, and a set of corresponding fractions, or abundances, that indicate the proportion of each endmember present in the pixel” [6]. It is important to point out that many names were given to the spectral unmixing procedure: hyperspectral unmixing [7,8], linear mixing [9], nonlinear spectral mixing models [10,11], semi-empirical mixing model [12], spectral mixing models [13,14,15], spectral mixture analysis [16,17,18,19,20,21,22], spectral mixture modeling [23,24], and spectral unmixing [19,25,26]. In this paper, the term spectral unmixing was chosen.
The first studies that introduced the spectral unmixing procedure were carried out about 40 years ago (Table 1). In order to study Moon minerals, Adams & McCord [27] observed nonlinear behavior of the spectra of Apollo 11 and 12 samples that were measured in the laboratory. In order to analyze the spectra of Mars, Singer & McCord [28] assumed that the spectrum of the mixed pixel was a bilinear combination of the spectra of its two constituent materials, and it was weighted by their abundances in the mixed pixel; their model required two constraints: the sum of the weighing factors must be one, and their values must not be negative. Hapke [29] proposed a nonlinear mixing model that was called “isotropic multiple scattering approximation” by Heylen et al. [8]. Johnson et al. [12] and Smith et al. [13] combined “spectral mixing model” with the modified Kubelka–Munk model and principal component analysis, respectively. In order to analyze the spectra of Mars, Adams & Smith [23] improved the “bilinear model”, which was proposed by Singer & McCord [28], considering more than two constituent materials of the mixed pixel and adding the residual error.
Adams et al. [16] decomposed the “spectral mixture analysis” in two consecutive steps: the first step decomposes the spectrum of each mixed pixel into a collection of constituent spectra (called endmembers), and the second step determines the proportion of every endmember present in the pixel. The literature highlighted two main models for performing the first step: linear and nonlinear mixture models. To estimate the proportion of every endmember (called fractional abundances), many solutions were proposed (e.g., Gram–Schmidt Orthogonalization [30], Least Square Methods [31], Minimum Variance Methods [6], Singular Value Decomposition [32], Variable Endmember Methods [6]).

1.2. Reviews on the Spectral Unmixing Procedure

In order to more effectively understand the importance of spectral unmixing, a quantification of the works that have studied, implemented, and applied this procedure since 1971 were provided. For this purpose, all names that were given to the spectral unmixing procedure were exploited as terms in the search strategy. A total of 5768 and 5852 papers were identified using Web of Science and Scopus search engines, respectively (accessed on 19 May 2023). Among these papers, 19 reviews offered the status of spectral unmixing (Table 2).
An interesting overview of the “linear models” developed up to 1996 was offered by Ichoku & Karneili [1], who compared this method with four other unmixing models: probabilistic, geometric-optical, stochastic geometric, and fuzzy models. The authors summarized that evaluated spatial accuracies were not representative of the real accuracies at the level of individual pixels because the spatial validation was performed for a few test pixels.
Heinz & Chein-I-Chang [33] focused on the second constraint of linear spectral mixture analysis (i.e., the fractional abundances of each mixed pixel must be positive), which is very difficult to implement in practice. Reviewing the literature, the authors pointed out that because most research did not know in detail the spectra present in the image scene, their results did not necessarily reflect the true abundance fractions of the materials [33].
Keshava [42] exploited the hierarchical taxonomies to facilitate comparison of the wide variety of methods used for spectral unmixing and revealed their similarities and differences. Furthermore, the author restated that most of the methods developed to solve problems were due to lack of detailed knowledge of ground truth. In their extensive description of spectral unmixing methodology, Keshava and Mustard [6] focused on the processing chain of linear unmixing methods applied to hyperspectral data. The authors highlighted that the shortcomings in spatial validation were due to the lack of detailed ground-truth knowledge; for this reason, the main focus of the research was on determining endmembers, rather than recovering fractional abundance maps [6].
Bioucas-Dias et al. [36] aimed to update the previous review, which was proposed by Keshava and Mustard [6] 10 years earlier. Therefore, the authors extensively described the methods that were proposed from 2002 to 2012 to improve the mathematical validity of the spectral unmixing. Bioucas-Dias & Plaza [7,38], Parente & Palza [37], Veganzones & Grana [40], and Martinez et al. [41] provided brief, but comprehensive reviews of methods for statistical and geometric extraction of endmembers. Somers et al. [39] provided a comprehensive and extensive review of the methods to address the temporal and spatial variability of the endmembers in the spectral unmixing.
An introduction to nonlinear unmixing methods and an overview of the most commonly used approaches were provided by Heylen et al. [8]. These authors also pointed out the lack of detailed ground truths for accurate validation of the spectral unmixing procedures [8]. After performing a general review of spectral unmixing, Quintano et al. [41] provided an interesting summary of its applications. Moreover, the authors pointed out the difficulty in spatially validating the results of spectral unmixing results and identified two main reasons: “(1) it is difficult to collect ground truth as scale directly corresponding to remotely sensed data resolution; (2) traditional classification accuracy analysis measurement tools may not be suitable for mixed pixel analysis” [41].
Wei & Wang [5] presented an overview of four aspects of the spectral unmixing (i.e., geometric method, nonnegative matrix factorization (NMF), Bayesian method, and sparse unmixing), whereas an overview of the methods that estimated the number of endmembers was provided by Ismail & Bchir [39]. Shi & Wang [43] provided a comprehensive review of the methods that combined spatial and spectral information for the spectral unmixing; the authors called them “spatial spectral unmixing” [43]. To extract endmembers, select endmember combinations, and estimate endmember fraction abundances, these methods exploited the correlation between neighboring pixels [43]. Wang et al. [45] provided an overview of the methods that incorporated the spatial information not only in spectral unmixing, but also in the all image classifiers. The authors underlined that most of the spatial accuracy was based on “the idea of area-weighted accuracy” because it was derived from some validation samples.
The most recent review was offered by Borsoi et al. [4], who provided a comprehensive review of the methods to solve the spectral variability problem in hyperspectral data. The spectral variability is mainly due to atmospheric, illumination, and environmental conditions [46,47]. Starting from the availability or non-availability of spectral libraries, the authors organized the “Spectral Unmixing algorithms” “according to a practitioner’s point of view, based on the necessary amount of supervision and the computational cost” and highlighted that the algorithms with less supervision (i.e., Fuzzy Unmixing, MESMA—Multiple Endmember Spectral Mixture Analysis—and variants, Bayesian models) are the methods with high computational cost [4]. Moreover, the authors pointed out the difficulty of assessing the accuracy of these methods due to the lack of detailed ground truths [4]. A review of four of these methods, which address the spectral variability problem, was also provided by Drumetz et al. [44].
It is important to mention that the spatial accuracy of spectral unmixing results can be evaluated using images and/or in situ data and/or maps, and the spectral accuracy of spectral unmixing results can be evaluated using spectral signatures that were acquired in situ and/or in the laboratory and/or obtained from images [4,6,8,33,45]. However, an independent validation dataset is required (i.e., the spectral library and/or the reference maps) [48].

1.3. Objectvives

In conclusion, since 1971 many methods have been introduced to improve the mathematical validity of the spectral unmixing procedure, but the validation of the results still needs much improvement, especially the spatial validation. In particular, the lack of detailed ground-truth knowledge is the main reason of the many shortcomings in the spatial validation of the spectral unmixing results. However, no author provided an overview focusing on the spatial validation of the spectral unmixing results.
Therefore, this systematic review aims to provide readers with (a) an overview of how the previous authors approached spatial validation of spectral unmixing results and (b) recommendations for overcoming the many shortcomings of spatial validation and minimizing its errors. The systematic review was carried out in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) statement [49,50]. The methodological approach employed in this systematic literature review is explained in Section 2, whereas the results, discussion, and conclusions are presented in Section 3 and Section 4.

2. Materials and Methods

2.1. Identification Criteria

This systematic literature review aims to provide readers with an overview of the approaches applied for spatial validation of spectral unmixing results and does not claim to be exhaustive since too many works have studied, implemented, and applied this technique since 1971. Therefore, the papers published in 2022, 2021, and 2020 were chosen to analyze the current status, whereas those published not only in 2011 and 2010, but also in 1996 and 1995 were selected to assess the progress over time. The year 1995 was chosen as the initial time for the systematic review, because in this year, spectral unmixing and other “mixture modeling techniques” were well implemented and, thus, commonly employed [1,6,51,52,53,54]. The Web of Science (WoS) and Scopus search engines were used to identify the papers that spatially validated the spectral unmixing results and were published in 2022, 2021, 2020, 2011, 2010, 1996, and 1995.
Initially, the papers that named the spectral unmixing in the titles, abstracts, and keywords were identified. For this purpose, all the names assigned to spectral unmixing (i.e., hyperspectral unmixing, linear mixing, nonlinear spectral mixing models, semi-empirical mixing model, spectral mixing models, spectral mixture analysis, spectral mixture modeling, spectral unmixing) were employed as unique query strings (first yellow box in Figure 1).
The total records identified from these databases was 2999. The subject areas of the search engines were checked to refine the identification of the papers. Therefore, “4.169 Remote Sensing”, “4.174 Digital Signal Processing”, “4.17 Computer Vision & Graphics”, “5.250 Imaging &Tomography”, “5.20 Astronomy & Astrophysics”, “5.191 Space Sciences”, “8.8 Geochemistry, Geophysics & Geology”, “8.93 Archaelogy”, “8.19 Oceanography, Meteorology & Atmospheric”, “8.140 Water Resources”, “8.124 Environmental Sciences”, “3.40 Forestry”, and “3.45 Soil Science” were “Citation Topics” selected in the WoS database, whereas “Earth and Planetary Sciences”, “Physics and Astronomy”, and “Environmental Science” were the subject areas selected in the Scopus database. After refining the subject areas, the identified papers became 2034 (second yellow box in Figure 1): 1396 were the papers published in 2022, 2021, and 2020; 538 were the papers published in 2011 and 2010; 100 were the papers published in 1996 and 1995.

2.2. Screening and Eligible Criteria

Reading the abstracts of the identified papers was conducted to select only those that applied spectral unmixing to remote images. Excluding the duplicates, 760 papers were selected with the first screening (orange box in Figure 1): 535 were the papers published in 2022, 2021, and 2020; 186 were the papers published in 2011 and 2010; 100 were the papers published in 1996 and 1995.
Reading the full text of the screened papers was conducted to identify only those that spatially validated the spectral unmixing results (bright red box in Figure 1). The last analysis identified the eligible papers: 326 were the papers published in 2022, 2021, and 2020; 112 were the papers published in 2011 and 2010; 16 were the papers published in 1996 and 1995.
In conclusion, 454 eligible papers were included in this systematic review. In Appendix A, the Table A1, Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7 summarize the characteristics of the eligible papers that were published in 2022, 2021, 2020, 2011, 2010, 1996, and 1995, respectively.

3. Results

3.1. Spatial Validation of Spectral Unmixing Results

The screening carried out showed that the number of studies that spatially validated the results of spectral unmixing has significantly increased over the selected years (bright red box in Figure 1): about 100 research papers per year were published in the past 3 years; about 50 research papers per year were published in 2011 and 2010; about 10 research papers per year were published in 1996 and 1995. The screening carried out showed also that the number of studies that applied spectral unmixing has significantly increased over the selected years (orange box in Figure 1): about 180 research papers per year were published in the past 3 years; about 90 research papers per year were published in 2011 and 2010; about 20 research papers per year were published in 1996 and 1995. In order to assess the importance of spatial validation in the spectral unmixing procedure, the papers that applied spectral unmixing to remote imaging were analyzed (orange box in Figure 1). Figure 2 shows the percentage of these papers that were not validated (the percentage in grey wedges), spectrally validated (the percentage in yellow wedges), spatially validated (the percentage in blue wedges), and spatially and spectrally validated (the percentage in green wedges) the spectral unmixing results. Therefore, spatial validation was carried out alone (blue wedges in Figure 2) or together with spectral validation (green wedges in Figure 2).
Considering all papers that performed spatial validation (blue and green wedges in Figure 2), the percentage of these research published in 2022, 2021, and 2020 (61% of a total of 326 papers) was comparable to that of the papers that were published in 2011 and 2010 (60% of a total of 112 papers), whereas these percentages were greater than those of the papers that were published in 1996 and 1995 (41% of a total of 16 papers). Moreover, the percentage of the research published in 2022, 2021, and 2020 that did not validate the results (23%) was smaller than those of the papers that were published in the other 2 groups of years (31%). In conclusion, these values highlighted not only the increasing application of spectral unmixing over these years, but also the high priority given to the spatial validation.

3.2. Remote Images

The eligible papers published in 2022, 2021, 2020, 2011, 2010, 1996, and 1995 are summarized in Table 3, Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9, according to the remote images to which spectral unmixing was applied. Authors who applied only spatial validation were cited in the fourth columns of Table 3, Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9, whereas those who applied both spatial and spectral validation were cited in the fifth columns.
Table 3. Eligible papers published in 2022.
Table 3. Eligible papers published in 2022.
Remote Image AnalyzedTime SeriesStudy Area ScaleSpatial Validation Carried OutSpatial and Spectral Validation Carried Out
AMMIS * (0.5 m) [55]NoLocal[56,57]
Apex * (2.5 m) [58]NoLocal[59]
ASTER (15–30–90 m) [60]NoRegional 1 [61]
ASTER (15–30 m)Yes 2Local [62]
AVHRR (1–5 km) [63]Yes 1Regional 1[64][65,66]
AVIRIS * (10/20 m) [67]NoLocal[57,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87][88,89,90,91,92,93,94,95,96,97,98]
AVIRIS-NG * (5 m) [99]NoLocal[100]
CASI * (2.5 m) [101]NoLocal[59,78]
DESIS * (30 m) [102]Yes 1Regional 1 [103]
DESIS * (30 m)NoLocal [104]
EnMap * (30 m) [105]NoLocal[69]
GaoFen-6 (2–8–16 m) [106]NoRegional 1 [107]
GaoFen-2 (3.2 m)Yes 1Regional 1[108]
GaoFen-1 (2–8–16 m)NoLocal[109]
HYDICE * (10 m) [110]NoLocal[59,68,76,77,79,81,82,85,86,90,111][89,96,97]
Hyperion * (30 m) [112]Yes 1Local[75]
Hyperion * (30 m)NoLocal[113][114,115,116]
HySpex * (0.6–1.2 m) [104]NoLocal[104,117]
Landsat (15–30 m) [118]Yes 1Continental 1[119]
Landsat (15–30 m)Yes 1Regional 1[108,120,121,122,123,124,125,126,127,128,129,130,131,132,133][134,135]
Landsat (15–30 m)NoRegional 1 [107,136,137]
Landsat (15–30 m)Yes 1Local[138,139][62]
Landsat (15–30 m)NoLocal 2[140,141]
Landsat (15–30 m)NoLocal[109,142]
M3 hyperspectral image * [143]NoMoon [143]
MIVIS * (8 m) [144]NoLocal [145]
MERIS (300 m) [146]Yes 1Local [147]
MODIS (0.5–1 km) [148]Yes 1Continental 1 [149]
MODIS (0.5 km)Yes 1Regional 1[108,150,151,152][137]
MODIS (0.5 km)NoLocal[153]
NEON * (1 m) [154]NoLocal[154]
PRISMA * (30 m) [155]NoLocal [114,156,157,158]
ROSIS * (4 m) [159]NoLocal[56,57,78,81,85]
Samson * (3.2 m) [59]NoLocal[59,72][89,97]
Sentinel-2 (10–20–60 m) [160]Yes 1Regional 1[108,133,161,162,163]
Sentinel-2 (10–20–60 m)NoRegional 1[136][107,164,165]
Sentinel-2 (10–20–60 m)Yes 1Local[166,167][168]
Sentinel-2 (10–20–60 m)NoLocal 2 [104]
Sentinel-2 (10–20–60 m)NoLocal [169]
Specim IQ * [170]Yes 1Laboratory[170]
SPOT (10–20 m) [171]NoLocal 2[140]
WorldView-2 (0.46–1.8 m) [172]NoLocal[166]
WorldView-3 (0.31–1.24–3.7 m)NoLocal[166]
* Hyperspectral sensor; 1 Multiple images acquired from same sensor; 2 Multiple images acquired from different sensors.
Table 4. Eligible papers published in 2021.
Table 4. Eligible papers published in 2021.
Remote Image AnalyzedTime SeriesStudy Area ScaleSpatial Validation Carried OutSpatial and Spectral Validation Carried Out
ASTER (15–30–90 m)NoRegional 1 [173]
AVIRIS *NoLocal[174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201][202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225]
AVIRIS-NG * (5 m)NoLocal [226]
CASI *NoLocal[174,227]
Simulated EnMAP *Yes 1Regional 1[228]
GaoFen-5 * (30 m)NoLocal [229]
HYDICE * (10 m)NoLocal[192,230,231,232][204,212,214,216,218]
HyMap * (4.5 m)YesLocal[233]
Hyperion * (30 m)NoLocal [212,234,235]
Hyperion * (30 m)Yes 1Local[236,237]
HySpexNoLocal [238]
Landsat (30 m)Yes 1Regional 1[239,240,241,242,243,244]
Landsat (30 m)Yes 1Local 2[245,246,247,248,249,250,251,252,253]
Landsat (30 m)NoLocal[227,254,255,256,257,258,259]
Landsat (30 m)NoRegional 1[260]
MODIS (0.5–1 km)NoLocal[254,261]
MODIS (0.5–1 km)Yes 1Regional 1[262,263,264]
PRISMA * (30 m)NoLocal [265]
ROSIS * (4 m)NoLocal[191,200,266][217,267]
Samson * (3.2 m)NoLocal[188,232,268][207,210,211,214,224,225,267]
Sentinel-2 (10–20–60 m)NoLocal[255,258][226,269]
Sentinel-2 (10–20–60 m)Yes 1Local[243,253,270][229,271,272]
Sentinel-2 (10–20–60 m)NoRegional 1 [273]
Sentinel-2 (10–20–60 m)Yes 1Regional 1[244]
UAV multispectral image [274]NoLocal[274]
WorldView-2 (0.46–1.8 m)Yes 1Local [275]
WorldView-3 (0.31–1.24–3.7 m)NoLocal 2[276]
ZY-1-02D * (30 m) [228] NoLocal [228]
* Hyperspectral sensor; 1 Multiple images acquired from same sensor; 2 Multiple images acquired from different sensors.
Table 5. Eligible papers published in 2020.
Table 5. Eligible papers published in 2020.
Remote Image AnalyzedTime SeriesStudy Area ScaleSpatial Validation Carried OutSpatial and Spectral Validation Carried Out
AISA Eagle II airborne hyperspectral scanner * [277]NoLocal[277]
ASTER (15–30–90 m)NoRegional 1[278]
ASTER (15–30–90 m)Yes 1Local 2 [279,280]
AVIRIS *NoLocal[281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298][299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327]
AVIRIS NG *NoLocal[291]
AWiFS [328]Yes 1Local 2[328]
CASI *NoLocal[329]
Simulated EnMAP * (30 m)NoRegional 1[330]
GaoFen-1 WFVYes 1Local[331]
GaoFen-1 WFVYes 1Local 2[332][333]
GaoFen-2NoLocal 2[332]
HYDICE * (10 m)NoLocal[292,293,298,334,335][299,307,309,310,316,318,321,322,324]
HyMAP *NoLocal 2 [280]
HyMAP *NoLocal[336]
HySpex * (0.7 m)NoLocal[337]
Hyperion * (30 m)NoLocal[336][338]
Landsat (30 m)Yes 1Local 2[332][280,339]
Landsat (30 m)Yes 1Local[252,340,341,342,343,344,345,346,347]
Landsat (30 m)Yes 1Continental 1[348]
Landsat (30 m)Yes 1Regional 1[349,350,351,352,353,354,355][356]
Landsat (30 m)NoRegional 1[357]
MODIS (0.5–1 km)Yes 1Local[340,358,359,360,361][333]
MODIS (0.5–1 km)Yes 1Regional 1[362,363]
MODIS (0.5–1 km)Yes 1Local 2[364,365][279]
PlanetScope (3 m) [366]Yes 1Local 2[366]
PROBA-V (100 m) [367]Yes 1Regional 1[353,368,369,370,371]
ROSIS * (4 m)NoLocal[285,372][373]
Samson * (3.2 m)NoLocal[284,374,375][301,303,305,315,320,323,324]
Sentinel-2 (10–20–60 m)NoLocal 2[332,376][280,339]
Sentinel-2 (10–20–60 m)Yes 1Local[328,340,377,378,379,380,381,382][333,383]
Suomi NPP-VIIRS [354]Yes 1Regional 1[353]
UAV hyperspectral data * [384]Yes 1Local [384]
WorldView-2Yes 1Local[342]
WorldView-2Yes 1Local 2 [385]
WorldView-3Yes 1Local 2 [385]
* Hyperspectral sensor; 1 Multiple images acquired from same sensor; 2 Multiple images acquired from different sensors.
Table 6. Eligible papers published in 2011.
Table 6. Eligible papers published in 2011.
Remote Image AnalyzedTime SeriesStudy Area ScaleSpatial Validation Carried OutSpatial and Spectral Validation Carried Out
AHS * [386]NoLocal [386]
ASTERNoLocal [387,388,389]
ASTERYes 1 Local[390,391]
AVIRIS *NoLocal[307,392,393,394,395,396,397,398,399,400,401,402,403][387,404,405,406,407,408,409,410,411,412,413,414,415,416,417]
CASI *NoLocal [418]
MERIS (300 m)NoLocal[419]
MODIS (0.5–1 km)Yes 1Local[420,421,422,423]
HYDICE *NoLocal[392,424][414,415,425]
HyMAP *NoLocal[392,426][427]
Hyperion * (30 m)NoLocal [387,428]
HJ-1 * (30 m) [429]NoLocal[429,430]
Landsat (30 m)Yes 1Local[431,432,433][387]
Landsat (30 m)NoLocal[434,435]
Landsat (30 m)Yes 1Local 2[436,437,438]
Landsat (30 m)NoLocal 2[423,439]
QuickBird (0.6–2.4 m) [440]NoLocal[441,442]
SPOT (10–20 m)NoLocal 2[439,441]
* Hyperspectral sensor; 1 Multiple images acquired from same sensor; 2 Multiple images acquired from different sensors.
Table 7. Eligible papers published in 2010.
Table 7. Eligible papers published in 2010.
Remote Image AnalyzedTime SeriesStudy Area ScaleSpatial Validation Carried OutSpatial and Spectral Validation Carried Out
Airborne hyper-spectral image * (about 1.5 m) [443]NoRegional 1 [443]
AHS * (2.4 m)NoLocal [444]
ASTER (15–30–90 m)Yes 1Local[445,446]
ASTER (15–30–90 m)Yes 1Regional 1[447]
ATM (2 m) [101]NoLocal 2 [101]
AVHRR (1 km)Yes 1Regional 1[448]
AVIRIS * (20 m)NoLocal[449,450,451,452,453,454,455,456,457][458,459,460,461,462,463]
CASI * (2 m)NoLocal [101]
CASI *NoLaboratory [464,465]
CHRIS * (17 m) [466]NoLocal[467]
DAIS * (6 m) [464]NoLocal[465]
DESIS *NoLocal [468,469]
HYDICE * NoLocal[455,470,471][458,463]
HyMAP *NoLocal[471]
Hyperion * (30 m)NoLocal[472,473,474]
HJ-1 * (30 m)NoLocal[475,476]
Landsat (30 m)Yes 1Regional 1[477,478,479,480,481,482,483]
Landsat (30 m)NoRegional 1[484,485,486,487,488,489][490]
Landsat (30 m)NoLocal 2[491,492]
Landsat (30 m)NoLocal[493]
MIVIS * (3 m)NoRegional 1[494]
MODIS (0.5–1 km)Yes 1Regional 1[495]
MODIS (0.5–1 km)Yes 1Continental 1[496]
QuickBird (2.4 m)NoLocal 2[491]
QuickBird (2.4 m)NoLocal[497,498]
SPOT (10–20 m)Yes 1Regional 1[480]
SPOT (2.5–10–20 m)NoLocal 2[486,491,492]
SPOT (2.5–10–20 m)NoLocal[499][500]
* Hyperspectral sensor; 1 Multiple images acquired from same sensor; 2 Multiple images acquired from different sensors.
Table 8. Eligible papers published in 1996.
Table 8. Eligible papers published in 1996.
Remote Image AnalyzedTime SeriesStudy Area ScaleSpatial Validation Carried OutSpatial and Spectral Validation Carried Out
AVIRIS *NoLocal[501,502][503]
GERIS * [504]NoLocal[504]
Landsat (30 m)NoLocal[14,505][506]
SPOT (2.5–10–20 m)NoLocal[507]
* Hyperspectral sensor.
Table 9. Eligible papers published in 1995.
Table 9. Eligible papers published in 1995.
Remote Image AnalyzedTime SeriesStudy Area ScaleSpatial Validation Carried OutSpatial and Spectral Validation Carried Out
AVHRR (1–5 km)Yes 1Regional 1 [508]
AVIRIS * (20 m)NoLocal [509][510,511]
Landsat (30 m)NoLocal[512][513]
MIVIS * (4 m)NoLocal [514]
MMR * [515]Yes 1Local [515]
* Hyperspectral sensor; 1 Multiple images acquired from same sensor.
The first columns of Table 3, Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9 and the second columns of Table A1, Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7 show the sensor name and the spatial resolution of the images. Considering all eligible papers, 27 hyperspectral sensors and 16 multispectral sensors were employed. Hyperspectral sensors were highlighted in the first columns of Table 3, Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9 with an asterisk. The literature often combined spectral unmixing with hyperspectral data because the number of bands must be greater than the number of endmembers [4,5,42,44]. However, the percentage of papers that employed hyperspectral data (57% of a total of 458 papers) is slightly higher than the percentage of papers that employed multispectral data (43% of a total of 458 papers). The second columns of Table 3, Table 4, Table 5, Table 6, Table 7, Table 8 and Table 9 show the papers that performed the time series studies, whereas the third columns of these tables show the papers that performed the local, regional, or continental studies.
The analysis of these data showed that most studies that analyzed hyperspectral images were performed at the local scale and did not carry out the multitemporal studies, whereas most studies that analyzed multispectral images were performed at the regional or continental scale and carried out the multitemporal studies (more than one image was analyzed). Therefore, the spectral unmixing is widely applied to multispectral images, despite their smaller number of bands than hyperspectral images, because these data are characterized by greater spatial and temporal availability than those of the hyperspectral data.
Moreover, the spectral unmixing was also applied to some hyperspectral and multispectral images that were characterized with high spatial resolutions (e.g., AMMIS image with spatial resolution equal to 0.5 m [56] and WorldView-3 image with spatial resolution of 0.31 m [166]). These papers confirm that, no matter how high the spatial resolution might be, no image pixel results were completely homogeneous in spectral characteristics [9,516,517].

3.3. Accuracy Metrics

Accuracy, which is defined as “the degree of correctness of the map”, is usually assessed by comparing the “ground truth” with the map retrieved from remote images [518,519]. Because no map can fully and completely map the territory [520], ground truth is more correctly called reference data [521]. To assess the differences between the reference data and results of the spectral unmixing, the eligible papers exploited different metrics. Figure 3 shows the pie chart of the distribution of the metrics that were adopted by eligible papers.
The other 14 metrics were average accuracy [522], correct labeling percentage for the unchanged pixels [141], correlation coefficient [150], Kling–Gupta efficiency [523], mean abundance error [117], mean error [169], mean relative error [169], normalized average of spectral similarity measures [524], producer’s accuracy [153], Receiver Operating characteristic Curves (ROC) method [525], relative mean bias [165], separability spectral index [526], signal-to-reconstruction error [56], and systematic error [109].
In conclusion, the authors of 454 eligible papers employed 22 different metrics, and most authors employed more than 1 metric. Overall, 25% of the eligible papers did not specify the accuracy metrics used. It is very important to note that some standard accuracy assessments, such as the kappa coefficient, “assume implicitly that each of the testing samples is pure”; therefore, some of these metrics were inappropriate for evaluating the accuracy of the fractional abundance maps [41,518].

3.4. Key Issues in the Spatial Validation

Since the literature highlighted many sources of error in accuracy assessment of retrieved maps [518,519,521], the authors identified and carried out several “key issues” to address and minimize these errors. Figure 4 and Table A1, Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7 summarize the key issues that were identified.

3.4.1. Validated Endmembers

Before analyzing the endmembers that were validated, it is necessary to remember that the number of endmembers that were determined with the images must be less than the number of sensor bands; therefore, the number of endmembers that were determined with the multispectral data is less than the number of endmembers that were determined with the hyperspectral data [6,23,527]. Therefore, the authors who elaborated the multispectral images employed smaller levels of model complexity than authors who elaborated the hyperspectral images [528,529]. For example, the VIS model was used to map only three endmembers (Vegetation, Impervious surfaces, and Soil) in many urban areas that were retrieved from multispectral data (e.g., [109,152,477,493]).
The third columns of Table A1, Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7 list the endmembers that were determined using spectral unmixing; the fourth columns of these showed the number of these endmembers that were validated. It is interesting to note that some authors validated smaller number of endmembers than the number of the endmembers that were determined (i.e., 40 eligible papers). Dividing the works that analyzed hyperspectral images from those that analyzed multispectral data, Figure 5 shows the percentage of studies that validated the total or partial number of endmembers. It is important to highlight that, since 4 eligible papers analyzed both hyperspectral and multispectral data [104,227,231,281], the sum of papers that analyzed hyperspectral data and papers that analyzed multispectral data (i.e., 458) is greater than the number of eligible papers (i.e., 454).
Therefore, only 2% of the studies that elaborated hyperspectral images partially validated the determined endmembers, whereas 18% of the studies that elaborated multispectral images partially validated the determined endmembers. As mentioned above, hyperspectral images were used to carry out non-repeated surveys over time and at local-scale studies (252 papers of a total of 262), whereas most multispectral images were used to carry out regional- or continental-scale studies that were or were not repeated over time (180 papers of a total of 196). Therefore, some of these authors, who analyzed more than one image, chose to spatially validate only the materials or groups of materials on which they focused their study. For example, Hu et al. [149] spatially validated only blue ice fractional abundance maps that were retrieved from MODIS images covering the period 2000–2021 in order to present a FABIAN (Fractional Austral-summer Blue Ice over Antarctica) product. It should be noted that 5 and 12% of the papers that analyzed hyperspectral or multispectral data, respectively, did not specify which endmembers were validated.

3.4.2. Sampling Designs for the Reference Data

The literature demonstrated that a possible source of error in spatial validation is due to the choice of the sampling design for the reference data [518,519,521,530]. The sampling design mainly includes the definition of the sample size and the sampling design of the reference data [518]. Authors of eligible papers chose three kinds of sample sizes: the whole study area; the representative area; small sample sizes (pixels, plots, and polygons samples). The eighth columns of Table A1, Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7 show the different sample sizes that were adopted by every eligible paper, and Table 10 shows the number of papers that adopted the different sample sizes.
Most authors of the eligible papers chose to validate the whole study areas, followed, in descending order, by the choice to employ the different number of small sample sizes and then the representative areas. It is also important to note the high percentages of the papers that did not specify the sample size of the reference data: 18, 11, and 31%, respectively.
The literature also pointed out that the sampling designs for spatially validating maps at local scale cannot be the same as the designs for spatially validating maps at regional or continental scale [518,530]. As mentioned above, most of the studies that analyzed the hyperspectral data were performed at local scale (252 papers of a total of 262), whereas the studies that analyzed the multispectral images performed at regional or continental scale (180 papers of a total of 196). Therefore, the eligible papers that analyzed hyperspectral images were analyzed separately from those that analyzed multispectral images (Figure 6 on the right and left, respectively), not only to analyze the different sampling designs adopted from the hyperspectral and multispectral data, but also to highlight the different sampling designs chosen for local or regional/continental scale studies. Figure 6 shows the percentage of the eligible papers that employed the different sample sizes and the percentage of the eligible papers that employed a different number of small sample sizes.
Most papers that processed hyperspectral images validated the whole study area (212 papers), whereas most papers that processed multispectral images employed small sample sizes (94 papers).
The authors of eligible papers that employed small sample sizes adopted three different sampling designs of reference data: partial, random, and uniform. The ninth columns of Table A1, Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7 show the sampling designs of every eligible paper. Most authors who published in 2022, 2021, and 2020 and published in 2011 and 2010 chose the random distribution of reference data (78% for a total of 326 papers and 76% for a total of 110 papers, respectively), whereas the authors who published in 1996 and 1995 did not specify the sampling designs employed. Stehman and Foody [519] highlighted that “the most commonly used designs” that were chosen to assess the land cover products were “simple random, stratified random, systematic, and cluster” designs. Therefore, these results confirmed that random designs were the most commonly used approaches.

3.4.3. Sources of the Reference Data

Eligible papers employed four different sources of reference data to spatially validate spectral unmixing results: images, in situ data, maps, and previous reference maps. Table 11 shows the number of the eligible papers that employed these reference data sources, whereas the fifth columns of Table A1, Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7 detail the sources of the reference data.
The number of authors who chose to utilize geological, land use, or land cover maps as reference maps is the smallest (5% of the total eligible papers), followed, in ascending order, by the number who chose to create the reference maps using in situ data (20% of the total eligible papers), and then by the number of authors who chose to create the reference maps using other images (31% of the total eligible papers). Firstly, the number of authors who chose to use the previous reference maps is the largest (44% of the total eligible papers).
As regards the authors who chose to create the reference maps using other images, most of them employed images at higher spatial resolutions than those of the remote images analyzed (95% of a total of 143 papers). To create the reference maps from the images, 47% of the eligible papers did not specify the method used to map the endmembers, 29% employed the photo-interpretation, 21% classified the images, 2% used the vegetation indexes, and 2% used the mixed approach by classifying and/or photo-interpreting and/or applying vegetation indexes (e.g., [114,145,531]). As regards the classification methods, there are four works that applied the same classification procedure to analyze the remote images and to create the reference maps [65,66,149,261]. Among these, the authors of 3 papers compared the fractional abundance maps that were retrieved from the multispectral images at moderate spatial resolutions (10, 30, and 60 m) with the fractional abundance maps that were retrieved from the multispectral data at coarse spatial resolutions (0.5 and 1 km) [65,66,149].
Moreover, the reference data sources that were chosen to validate the results of the hyperspectral images were analyzed separately from those that were chosen to validate the results of the multispectral images. Figure 7 shows the percentage of the papers that adopted the different sources of the reference data to validate the results of hyperspectral (right) and multispectral data (left).
As regards the papers that analyzed the multispectral data, most of the authors chose to create the reference maps from the other images, whereas most of the authors that analyzed the hyperspectral data chose to employ the previous reference maps. It is important to emphasize that 97% of these reference maps are available online together with hyperspectral images and/or reference spectral libraries (e.g., [532,533,534,535] Figure 8). Therefore, these images were well known: Cuprite (NV, USA, e.g., [70,458]), Indian Pines (IN, USA, e.g., [78,458]), Jasper Ridge (CA, USA, e.g., [68,97]), Salinas Valley (CA, USA, e.g., [75,78]) datasets that were acquired with AVIRIS sensors; Pavia (Italy, e.g., [81,85]) datasets that were acquired with the ROSIS sensor; Samson (FL, USA, e.g., [59,89]) dataset that was acquired with the Samson sensor; University of Houston (TX, USA, e.g., [59,78]) dataset that was acquired with the CASI-1500 sensor; Urban (TX, USA, e.g., [59,68]) and Washington DC Mall (Washington, DC, USA, e.g., [81,90]) datasets that were acquired with the HYDICE sensor.
Moreover, 93% of these papers proposed a method and tested it not only on these “real” hyperspectral data, but also on created synthetic images. Borsoi et al. [4] highlighted that in order to overcome “the difficulty in collecting ground truth data”, some authors generated synthetic images. However, the authors complained because “there is not a clearly agreed-upon protocol to generate realistic synthetic data” [4].

3.4.4. Reference Fractional Abundance Maps

“Misclassifications” of the reference data or “misallocations of the reference data” are another possible source of error in spatial validation, defined as “imperfect reference data” by [519] or “error magnitude” by [518]. The authors highlighted that these errors can be caused also by the use of “standard” reference maps to validate the spectral unmixing results (i.e., the fractional abundance maps) [41,518,519]. The difference between standard reference maps and reference fractional abundance maps is that each pixel of the standard reference map is assigned to a corresponding land cover class, whereas each pixel of the reference fractional abundance map is labeled with the fractional abundances of each endmember that is present in that pixel. Therefore, the values of the standard reference map are equal to 0 or 1, whereas the values of the reference fractional abundance map are greater than 2 and vary between 0 and 1 (100 values are able to fully validate the fractional abundance of endmembers [114]).
The reference fractional abundance maps were employed by 133 eligible papers that were published in 2022, 2021, and 2020; by 62 eligible papers that were published in 2011 and 2010; and by 13 eligible papers that were published in 1996 and 1995 (45% of the total eligible papers). Moreover, among these works, 87, 47, and 8 papers estimated the full range of abundances using 100 values (31% of the total eligible papers), whereas 41, 10, and 5 works partially estimated the fractional abundances using less than 100 values (12% of the total eligible papers). It is important to note that 7% of the total eligible papers did not specify if they used the standard reference maps or the reference fractional abundance maps.
The eligible papers were separately analyzed according to reference data sources that were adopted in order to find out how fractional abundances were estimated. In the four parts of Figure 9, the eligible papers that were clustered according to the reference data sources are shown, and each part of Figure 9 shows the percentage of the papers that did not specify the reference maps used and the number of the papers that fully or partially estimated the reference fractional abundance maps.
High-spatial-resolution images were the most widely employed to make the reference fractional abundance maps (81% of the total papers that employed the images), followed by in situ data (68% of the total papers that employed in situ data), and then the maps (50% of the total papers that employed maps). Moreover, in situ data were the most widely employed to estimate the full range of fractional abundances (62% of the total papers that employed in situ data), followed by high-spatial-resolution images (52% of the total papers that employed the images), and then the maps (21% of the total papers that employed the images). The previous reference maps were not employed to make the reference fractional abundance maps.
Many authors highlighted that it is not easy to create the reference fractional abundances maps (e.g., [4,6,518,519]). Cavalli [145] implemented a method that was proposed by [537] in order to create the reference fractional abundance maps. This method is able to create the reference fractional abundance maps by varying the spatial resolution of the high-resolution reference maps several times, and the range of fractional abundances can be fully estimated according to the spatial resolution of the reference maps [114].

3.4.5. Validation of the Reference Data with Other Reference Data

In order to further minimize the errors due to “misclassifications” or “misallocations of the reference data” [518,519], some authors validated the reference data using other reference data: 61 eligible papers published in 2022, 2021, and 2020; 21 eligible papers published in 2011 and 2010; 4 eligible papers published in 1996 and 1995. Therefore, 81% of the total eligible papers did not take into consideration that the reference map may not be “ground truth” and may be “imperfect” [519,520].
It is very important to point out that some authors took advantage of the online availability of reference data to validate reference data (e.g., [114,123,127,140,145,152,231,448,496]). Many efforts are being made to create the networks of accurate validation data [48,538,539,540]. For example, Zhao et al. [140] exploited in situ measurements of the Leaf Area Index (LAI) that were provided by the VALERI project [540], whereas Halbgewachs et al. [123], Lu et al. [423], Shimabukuro et al. [353], and Tarazona Coronel [127] utilized validation data that were provided by the Program for Monitoring Deforestation in the Brazilian Amazon (PRODES) [541].

3.4.6. Error in Co-Localization and Spatial Resampling

The key issues described above addressed only the errors in the thematic accuracy of the spectral unmixing results [518,519], whereas this key issue aimed to address the geometric errors due to the comparison of remote images with reference data [542]. The impact of co-localization and spatial resampling errors was minimized and/or evaluated by 6% of the eligible papers: 20 eligible papers published in 2022, 2021, and 2020; 8 eligible papers published in 2011 and 2010; 1 eligible paper published in 1996. In order to minimized the errors, Arai et al. [368], Cao et al. [164], Li et al. [107], Soenen et al. [500], and Zurita-Milla et al. [419] carefully chose the size of the reference maps; Bair et al. [254], Cavalli [114,145], Ding et al. [152], Fernandez-Garcia et al. [256], Hamada et al. [441], Hajnal et al. [169], Lu et al. [435], Ma & Chan [78], Rittger et al. [262], Sun et al. [263], Yang et al. [488], and Yin et al. [151] spatially resampled the reference fractional abundance maps; Estes et al. [447] compared different windows of pixels (i.e., 3 × 3, 7 × 7, 11 × 11, 15 × 15, and 21 × 21); Pacheco & McNairn [480] selected the size and the spatial resolution of the reference maps; Ben-dor et al. [507], Fernandez-Guisuraga et al. [342], Kompella et al. [328], Laamarani et al. [343], and Plaza & Plaza [465] carefully co-localized the reference fractional abundance maps on the reference maps; Wang et al. [366] expanded the windows of the field sample size; Zhu et al. [64] resampled at “four kinds of grids” (i.e., 1100 × 1100 m, 2200 × 2200 m, 4400 × 4400 m, and 8800 × 8800 m) the reference fractional abundance map and compared the results. Bair et al. [254], Binh et al. [341], Cavalli [114,145], Cheng et al. [543], and Ruescas et al. [448] evaluated the errors in co-localization and spatial-resampling due to the comparison of different data at different spatial resolutions. Moreover, Cavalli [145] proposed a method to minimize the errors: the comparison of the histograms of the reference fractional abundance values with the histograms of the retrieved fractional abundance values.
It is important to point out that 94% of the total papers did not address the geometric errors due to the comparison of remote images with reference data.

4. Conclusions

The term validation is defined as “the process of assessing, by independent means, the quality of the data products derived from the system outputs” by the Working Group on Calibration and Validation (WGCV) of the Committee on Earth Observing Satellites (CEOS) [48]. Since 1969, research has been involved to establish shared key issues to validate the land cover products that were retrieved from the remote images [518,519,539,544]. These products can be obtained by applying classifications called “hard”, because they extract information only from “pure pixels,” and classifications called “soft”, because they also extract information from “mixed pixels” [519,544]. However, not only the literature related to the spatial validation, but also every review on the spectral unmixing procedure (i.e., a soft classification) highlighted that the key issues in the spatial validation of soft classification results have yet to be clearly established and shared (e.g., [4,6,518,519]).
Since no review was performed on this fundamental topic, this systematic review aims (a) to identify and analyze how the authors addressed the spatial validation of spectral unmixing results and (b) to provide readers with recommendations for overcoming the many shortcomings of spatial validation and minimizing its errors. The papers published in 2022, 2021, and 2020 were considered to analyze the current status of spatial validation, and the papers published not only in 2011 and 2010, but also in 1996 and 1995, were considered to analyze its progress over time. Since the literature on spectral unmixing is extensive, only papers published in these seven years were considered. A total of 454 eligible papers were included in this systematic review and showed that the authors addressed 6 key issues in the spatial validation. In this text, the order in which the key issues were presented is not an order of importance.
  • The first key issue concerned the number of the endmembers validated. Some authors chose to focus on only one or two endmembers, and only these were spatially validated. This key issue was designed to facilitate the conduct of regional- or continental-scale studies and/or multitemporal analysis. It is important to note that 8% of the eligible papers did not specify which endmembers were validated.
  • The second key issue concerned the sampling designs for the reference data. The authors who analyzed hyperspectral images preferred to validate the whole study area, whereas those who analyzed multispectral images preferred to validate small sample sizes that were randomly distributed. It is important to point out that 16% of the eligible papers did not specify the sampling designs for the reference data.
  • The third key issue concerned the reference data sources. The authors who analyzed hyperspectral images primarily used the previously referenced maps and secondarily created reference maps using in situ data, whereas the authors who analyzed multispectral images chose to create reference maps primarily using high-spatial-resolution images and secondarily using in situ data.
  • The fourth key issue was, perhaps, the one most closely related to the spectral unmixing procedure; it concerned the creation of the reference fractional abundance maps. Only 45% of the eligible papers created the reference fractional abundance maps to spatially validate the fractional abundance maps retrieved. These mainly employed high-resolution images and secondarily in situ data. Therefore, 55% of the eligible papers did not specify the employment of the reference fractional abundance maps.
  • The fifth key issue concerned the validation of the reference data with other reference data; it was addressed only by 19% of the eligible papers. Therefore, 81% of the eligible papers did not validate the reference data.
  • The sixth key issue concerned the error in co-localization and spatial resampling data, which was minimized and/or evaluated only by 6% of the eligible papers. Therefore, 94% of the eligible papers did not address the error in co-localization and spatial resampling data.
In conclusion, to spatially validate the spectral unmixing results and minimize and/or evaluate its errors, six key issues were considered not only from the eligible papers published in 2022, 2021 and 2020, but also from those published in 2010, 2011, 1996, and 1995. In addition, the results obtained from both hyperspectral and multispectral data were spatially validated considering all key issues, but these were addressed in different ways. All six key issues addressed together enabled rigorous spatial validation to be performed. Therefore, this systematic review provided readers with the most suitable tool to rigorously address spatial validation of the spectral unmixing results and minimize its errors.
The key difference between reference data suitable for hard and soft classifications is that the latter reference maps must have higher spatial resolution than the resolutions of the image pixels [6,114,518]. The optimal scale would be that 100 times larger than the image pixel resolution [114]. However, many hyperspectral data were validated using the previous reference maps at the same spatial resolution as the remote image, so these standard reference maps can only create reference fractional abundance maps with the help of other reference data. The employment of the standard reference maps instead of the reference fractional abundance maps was also evidenced by the employment of metrics to assess spatial accuracy that “assume implicitly that each of the testing samples is pure” [37,217].
However, only 4% of eligible papers addressed every key issue, and many authors did not specify which approach they employed to spatially validate the spectral unmixing results. Moreover, most of the authors who specified the approach employed did not adequately explain the methods used and the reasons for their choices. Six “good practice criteria to guide accuracy assessment methods and reporting” were identified by [519]. Therefore, these papers did not fully meet three good practice criteria: “reliable”, “transparent”, and “reproducible” [519].

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

In accordance with the PRISMA statement [49,50], 454 eligible papers were identified, screened, and included in this systematic review: 326 eligible papers were published in 2022, 2021, and 2020; 112 eligible papers were published in 2011 and 2010; 16 eligible papers were published in 1996 and 1995. The eligible criterion was that the results of the spectral unmixing were spatially validated. Analyzing these papers, six key issues were identified that were differently addressed to spatially validate the spectral unmixing results. The different ways in which the key issues were addressed by the eligible papers published in 2022, 2021, 2020, 2011, 2010, 1996, and 1995 are summarized in Table A1, Table A2, Table A3, Table A4, Table A5, Table A6 and Table A7, respectively.
Table A1. Main characteristics of the eligible papers that were published in 2022.
Table A1. Main characteristics of the eligible papers that were published in 2022.
PaperRemote ImageDetermined EndmembersValidated EndmembersSources of Reference DataMethod for Mapping the Endmembers Validation of Reference Data with Other Reference DataSample Sizes and Number of Small Sample Sizes Sampling DesignsReference DataEstimation of Fractional AbundancesError in Co-Localization and Spatial Resampling
Abay et al. [62]ASTER (15–30 m)
Landsat OLI (30 m)
Goethite, hematiteAllGeological map-In situ observations--Reference map--
Ambarwulan et al. [147]MERIS (300 m)Several total suspended matter concentrationsAllIn situ data--171 samples----
Benhalouche et al. [156]PRISMA (30 m)Hematite, magnetite, limonite, goethite, apatiteAllIn situ data-------
Bera et al. [120]Landsat TM, ETM+, OLI (30 m)Vegetation, impervious surface, soilAllGoogle Earth imagesPhotointerpretationSoil map101 polygonsUniformReference fractional abundance mapsPartial-
Brice et al. [121]Landsat TM, OLI (30 m)Water,
wetland vegetation, trees, grassland
1Planet images (4 m)PhotointerpretationIn situ observations427 wetlands-Reference fractional abundance mapPartial-
Cao et al. [164]Sentinel-2 (10–20–60 m)Vegetation, high albedo impervious surface, low albedo impervious surface, soilAllGaoFen-2(0.8–3.8 m)PhotointerpretationIn situ observations300 squares (100 × 100 m)Stratified randomReference fractional abundance mapsPartialPolygon size
Cavalli [114]Hyperion (30 m)
PRISMA (30 m)
Lateritic tiles, lead plates, asphalt, limestone, trachyte rock, grass, trees, lagoon waterAll
All
Panchromatic IKONOS image (1 m)
Synthetic Hyperion and PRISMA images (0.30 m)
Photointerpretation
The same spectral unmixing procedure performed to real images
In situ observations and shape files provided by the city and lagoon portal of Venice (Italy)The whole study areaThe whole study areaReference fractional abundance mapsFullSpatial resampling the reference maps and evaluation of the errors
Evaluation of the errors in co-localization and spatial-resampling
Cavalli [145]MIVIS (8m)Lateritic tiles, lead plates,
vegetation, asphalt,
limestone, trachyte rock
All
All
Panchromatic IKONOS image (1 m)
Synthetic MIVIS image (0.30 m)
Photointerpretation
The same spectral unmixing procedure performed to real image
In situ observations and shape files provided by the city and lagoon portal of Venice (Italy)The whole study areaThe whole study areaReference fractional abundance mapsPartialSpatial resampling the reference maps and evaluation of the errors
Evaluation of the errors in co-localization and spatial-resampling
Cerra et al. [104]DESIS (30 m)
HySpex (0.6–1.2 m)
Sentinel-2 (10–20–60 m)
PV panels,
2 grass, 2 forest, 2 soil,
2impervious surfaces
1Reference map--The whole study areaThe whole study area---
Cipta et al. [137]Landsat OLI (30 m)
MODIS (500 m)
Rice, non-riceAllIn situ data--10 samples----
Compains Iso et al. [134]Landsat TM, OLI (30 m)Forest, shrubland, grassland, water, rock, bare soilAllOrthophoto (≤ 0.5 m)Photointerpretation-50 squares (30 × 30 m)RandomReference fractional abundance mapsPartial-
Damarjati et al. [157]PRISMA (30 m)A. obtusifolia, sand, wetland vegetationsAllIn situ data----Reference maps--
Dhaini et al. [70]AVIRIS (20 m)Andradite, chalcedony, kaolinite, jarosite, montmorillonite, nontronite
Road, trees, water, soil
Asphalt, dirt, tree, roof
AllReference map--The whole study areaThe whole study areaReference maps--
Ding et al. [122]Landsat TM, OLI (30 m)Vegetation, impervious surface, soilAllGoogle satellite images
(1 m)
Photointerpretation-100 pointsRandomReference maps--
Ding et al. [152] MODIS (250–500 m)Vegetation, non-vegetationAllLandsat (30 m)K-means unsupervised
classified method
Google map5 Landsat imagesRepresentative areasReference fractional abundance mapsPartialSpatial resampling the reference maps
Fang et al. [71]AVIRIS (20 m)Road, 2building, trees, grass, soil
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference maps--
Fernández-Guisuraga et al. [161]Sentinel-2 (10–20 m)Soil, green vegetation, non-photosynthetic vegetation1Photos--60 situ plots (20 × 20 m)Stratified randomReference fractional abundance mapFull-
Gu et al. [98]AVIRIS (20 m)Vegetation, soil, road, river
soil, water, vegetation
AllReference map --The whole study areaThe whole study areaReference maps--
Guan et al. [86]AVIRIS (20 m)Trees, water, dirt, roadAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Asphalt, grass, trees, roofsAllReference map--The whole study areaThe whole study areaReference maps--
Hadi et al. [68]AVIRIS (20 m)Trees, water, dirt, roadAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Asphalt, grass, trees, roofsAllReference map--The whole study areaThe whole study areaReference maps--
Hajnal et al. [169]Sentinel-2 (10–20–60 m)Vegetation, impervious surface, soilAllAPEX image (2 m),
High-resolution land cover map
Support vector classification-APEX imageRepresentative areasReference fractional abundance mapsFullSpatial resampling the reference maps
Halbgewachs et al. [123]Landsat TM, OLI (30 m), TIRS (60 m)Forest, non-Forest (non-photosynthetic vegetation, soil, shade)2Annual classifications of the Program for Monitoring Deforestation in the Brazilian Amazon (PRODES)-Official truth-terrain data from deforested and non-deforested areas prepared by PRODES494 samplesStratified randomReference maps--
He et al. [56]AMMIS (0.5 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
ROSIS (4 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
Hong et al. [69]AVIRIS (20 m)Trees, water, dirt, road, roofsAllReference map--The whole study areaThe whole study areaReference maps--
EnMAP (30 m)Asphalt, soil, water, vegetationAllReference map--The whole study areaThe whole study areaReference maps--
Hu et al. [149]MODIS (0.5–1 km)Blue ice,
coarse-grained snow, fresh snow, bare rock, deep water, slush, wet snow
1Sentinel-2 imagesThe same spectral unmixing procedure performed to MODIS imagesFive auxiliary datasetsSix test areas identified as blue ice areas in the Landsat-based LIMA productRepresentative areasReference fractional abundance mapsFull-
Hua et al. [72]AVIRIS (10 m)
Samson (3.2 m)
Dirt, road
-
All
All
Reference map
Reference map
--The whole study area
The whole study area
The whole study area
The whole study area
Reference maps
Reference maps
--
Jamshid Moghadam et al. [115]Hyperion (30 m)Kaolinite/smeetite, sepiolite, lizardite, choriteAllGeological map--The whole study areaThe whole study areaReference maps--
Jin et al. [143]M3 hyperspectral imageLunar surface materialsAllLunar Soil Characterization Consortium dataset----Reference fractional abundance mapsFull-
Jin et al. [73]AVIRIS (10 m)
Samson (3.2 m)
Road, soil, tree, water
Water, tree, soil
All
All
Reference map
Reference map
--The whole study area
The whole study area
The whole study area
The whole study area
Reference maps
Reference maps
--
Kremezi et al. [166]Sentinel-2 (10–20–60 m)
WorldView-2 (0.46–1.8 m)
WorldView-3 (0.31–1.24–3.7 m)
PET-1.5 l bottles, LDPE bags, fishing netsAllIn situ data--3 squares (10 × 10 m)-Reference map--
Kuester et al. [111]HYDICE (10 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
Kumar et al. [113]Hyperion (30 m)Sal-forest, teak-plantation, scrub, grassland, water, cropland, mixed forest, urban, dry riverbedAllGoogle Earth images--Same squares (30 × 30 m)-Reference fractional abundance mapsPartial-
Lathrop et al. [124]Landsat 8 OLI (15–30 m)Mud, sandy mud, muddy sand, sandAllIn situ data--805 circles (250 m radius)UniformReference fractional abundance mapPartial-
Legleiter et al. [103]DESIS (30 m)12 cyanobacteria genera,
water
AllIn situ data-------
Li et al. [75]AVIRIS (10 m)Vegetation, bare soil, vineyard, etc.AllField reference data--The whole study areaThe whole study areaReference maps--
Hyperion (30 m)-AllHyperion (30 m) image--The whole study areaThe whole study areaReference map--
Li et al. [74]AVIRIS (10 m)Tree, water, dirt, roadAllReference map--The whole study areaThe whole study areaReference maps--
Li et al. [107]GaoFen-6 (2–8–16 m)
Landsat 8 OLI (15–30 m)
Sentinel-2 (10–20–60 m)
Green vegetation, bare rock, bare soil, non-photosynthetic vegetationAllPhoto acquired with dronesClassificationIn situ measurements of fractional vegetation cover and bare rock285 polygonsRandomReference fractional abundance mapsFullPolygon size
Li et al. [76]AVIRIS (10 m)Andradite, chalcedony, kaolinite, jarosite, montmorillonite, nontroniteAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Asphalt, grass, trees, roofsAllReference map--The whole study areaThe whole study areaReference maps--
Luo et al. [77]AVIRIS (10 m)Andradite, chalcedony, kaolinite, jarosite, montmorillonite, nontroniteAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Asphalt, grass, trees, roofsAllReference map--The whole study areaThe whole study areaReference maps--
Lyngdoh et al. [100]AVIRIS (20 m)
AVIRIS-NG (5 m)
Trees, water, dirt, road
Red soil, black soil, crop residue, built-up areas, bituminous roads, water
AllReference map--The whole study areaThe whole study areaReference maps--
Ma & Chang [78]AVIRIS (10 m)-AllReference map--The whole study areaThe whole study areaReference maps-Spatial resampling the reference maps
CASI (2.5 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
ROSIS (4 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
Matabishi et al. [469]DESIS (30 m)Roof materialsAllVHR images-Field validation data1053 ground reference points-Reference fractional abundance mapsFull-
Meng et al. [163]Sentinel-2 (10–20–60 m)Vegetation, non-vegetation1Google Earth Pro image
(1 m)
--10535 squares (10 × 10 m)Stratified randomReference fractional abundance mapsPartial-
Nill et al. [125]Landsat TM, OLI (30 m)Shrubs, coniferous trees, herbaceous plants, lichens, water, barren surfacesAllRGB camera (0.4–8 cm)
Orthophotos (10–15 cm)
-Field validation data216 validation pixelsStratified randomReference fractional abundance mapsFull-
Ouyang et al. [126]Landsat-8 OLI (30 m)Impervious surface,
evergreen vegetation, seasonally exposed soil
1Land use and land cover maps (0.5 m)--264 circles (1 km radius)RandomReference fractional abundance mapPartial-
Ozer & Leloglu [167]Sentinel-2 (10–20–60 m)Soil, vegetation, waterAllAerial images (30 cm)----Reference fractional abundance mapPartial-
P et al. [61]ASTER (90 m)Iron Oxide1In situ data --13 samples----
Palsson et al. [59]Apex Asphalt, vegetation, water, roofAllReference map--The whole study areaThe whole study areaReference maps--
AVIRIS (10 m)Road, soil, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
CASI (2.5)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Asphalt, grass, trees, roofsAllReference map--The whole study areaThe whole study areaReference maps--
Samson (3.2 m)Soil, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
Pan & Jiang [65]AVHRR (1–5 km)Snow, bare land, grass, forest, shadowAllLandsat7 TM+ image (30 m)The same procedure performed to AVHRR image-Landsat imageRepresentative areaReference fractional abundance mapsFull-
Pan et al. [66]AVHRR (1–5 km)Snow, bare land, grass, forest, shadowAllLandsat5 TM image (30 m)The same procedure performed to AVHRR imageThe land use/land coverLandsat imageRepresentative areaReference fractional abundance mapsFull-
Paul et al. [470] DESIS (30 m)PV panel, vegetation, sandAllVHR image---RandomReference fractional abundance mapsFull-
Pervin et al. [154]NEON (1 m)Tall woody plants, herbaceous and low stature vegetation, bare soilAllNEON AOP image (0.1 m)Supervised classificationDrone imagery (0.01 m)13 sets of 10 pixelsRandomReference fractional abundance mapsPartial-
Qi et al. [89]AVIRIS (10 m)Road, soil, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Asphalt, grass, trees, roofsAllReference map--The whole study areaThe whole study areaReference maps--
Samson (3.2 m)Soil, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
Rajendran et al. [116]Hyperion (30 m)Chlorophyll-a1WorldView-3 image (0.31–1.24–3.7 m) Field validation data--Reference fractional abundance mapsFull-
Ronay et al. [170]Specim IQWeed speciesAllIn situ data--The whole study areaThe whole study areaReference fractional abundance mapsFull-
Santos et al. [131]Landsat MSS, TM, OLI (30 m)Natural vegetation, anthropized area, burned, waterAllIn situ data--samplesRandomReference maps--
Shaik et al. [158]PRISMA (30 m)Broadleaved forest, Coniferous forest, Mixed forest, Natural
grasslands, Sclerophyllous vegetation
AllLand use and land cover map-Field validation data--Reference maps--
Shao et al. [109]Landsat-8 OLI (15–30 m)
GaoFen-1 (2–8–16 m)
Vegetation, soil
impervious surfaces (high albedo; low albedo), water
1GaoFen-1 image (2 m)Object-based classification
and photointerpretation of the results.
Ground-based measurements300 pixelsUniformReference fractional abundance mapPartial-
Shi et al. [90]AVIRIS (10 m)Road, soil, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Road, roof, soil, grass, trail, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
Shi et al. [79]AVIRIS (10 m)Road, soil, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Road, roof, soil, grass, trail, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
Shimabukuro et al. [132]Landsat TM, OLI (30 m)Forest plantationAllMapBiomas annual LULC map collection 6.0--20000 samplesStratified randomReference mapsPartial-
Silvan-Cardenas et al. [139]Landsat (30 m)--In situ data--samples-Reference maps--
Sofan et al. [135]Landsat-8 OLI (15–30 m) Vegetation, smoldering, burnt areaAllPlanetScope images (3 m)Photointerpretation--Random---
Song et al. [153]MODIS (0.5 km)Water, urban, tree, grassAllGlobalLand30 maps (GLC30) produced based on Landsat (30 m)----Reference fractional abundance mapsFull-
AVIRIS (10 m)--Reference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Road, roof, soil, grass, trail, tree, water-Reference map--The whole study areaThe whole study areaReference maps--
Sun et al. [80]AVIRIS (10 m)Andradite, chalcedony, kaolinite, jarosite, montmorillonite, nontroniteAllReference map--The whole study areaThe whole study areaReference fractional abundance mapsFull-
Sun et al. [165]Sentinel-2 (10–20–60 m)Rice residues,
soil, green moss, white moss
1Photos (1.5 m)PhotointerpretationIn situ observations30 samplesRandomReference fractional abundance mapsPartial-
Sutton et al. [119]Landsat TM, OLI (30 m)Drylands, semi-arid zone, arid zoneAllIn situ data--4207 samplesNo-uniform---
Tao et al. [91]AVIRIS (10 m) Andradite, chalcedony, kaolinite, jarosite, montmorillonite, nontroniteAllReference map--The whole study areaThe whole study areaReference maps--
Tarazona Coronel [127] Landsat TM, OLI (30 m)Vegetation1Landsat (15–30 m) and
Sentinel-2 (10–20–60 m) images
PhotointerpretationOfficial truth-terrain data from deforested and non-deforested areas prepared by PRODES300 samplesStratified randomReference fractional abundance mapsPartial-
van Kuik et al. [133]Landsat TM, OLI (30 m)
Sentinel-2 (10–20–60 m)
Blowouts to sand, water, vegetation1Unoccupied Aerial Vehicle (UAV) orthomosaics (1 m)Photointerpretation---Reference fractional abundance mapsPartial-
Viana-Soto et al. [138]Landsat TM, OLI (30 m)Tree, shrub, background (herbaceous, soil, rock)1OrthophotosPhotointerpretationValidation samples-UniformReference fractional abundance mapsFull-
Wang et al. [87] AVIRIS (10 m)--Reference map--The whole study areaThe whole study areaReference maps--
Wang et al. [142]Landsat-8 OLI
(30 m)
Impervious surfaces (high albedo, low albedo),
forest, grassland, soil
1QuickBird image (0.6 m)Spectral angle mapping classificationIn situ observations13,080 pointsRandomReference fractional abundance mapsPartial-
Wang et al. [150]MODIS (0.5 km)Vegetation, non-vegetationAllLandsat image (30 m)K-means-based unsupervised classification-Landsat imageRepresentative areaReference fractional abundance mapsPartial-
Wang et al. [92]AVIRIS (10 m)Andradite, chalcedony, kaolinite, jarosite, montmorillonite, nontroniteAllReference map--The whole study areaThe whole study areaReference map--
Wu & Wang [85]AVIRIS (10 m) Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Road, roof, soil, grass, trail, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
ROSIS (4 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
Xia et al. [128]Landsat ETM+, OLI (30 m)High albedo, vegetation
low albedo, shadow
2Google Earth imagesPhotointerpretation-100 polygons (30 × 30 m)RandomReference fractional abundance mapsPartial-
Xu et al. [162]Sentinel-2 (10–20–60 m)Impervious surface, water body, vegetation, bare landAllGoogle Earth imagesPhotointerpretationIn situ observations--Reference fractional abundance mapsPartial-
Yang et al. [57] AMMIS (0.5 m)
AVIRIS
ROSIS
-AllReference map--The whole study areaThe whole study areaReference maps--
Yang [81]AVIRIS (20 m) Vegetation, water, soilAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Road, roof, soil, grass, trail, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
ROSIS (4 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference maps--
Yang et al. [141] Landsat-8 OLI
(30 m)
Water, non-waterAll Google Earth images--The whole study areaThe whole study areaReference fractional abundance mapsPartial-
Yi et al. [82]AVIRIS (20 m) Vegetation, water, soilAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Road, roof, soil, grass, trail, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
Yin et al. [82]MODIS (0.250 km) Water, soil1Landsat OLI image (30 m)Modified normalized difference water index (MNDWI)-Landsat imageRepresentative areaReference fractional abundance mapsPartialSpatial resampling the reference maps
Zhang & Jiang [108]Landsat (30 m)
Sentinel-2 (20 m)
MODIS (0.5 km)
Snow1GaoFen-2 image (3.2 m)Supervised classification---Reference fractional abundance mapPartial-
Zhang et al. [117]HySpec (0.7 m)Bitumen, red-painted metal sheets, blue fabric, red fabric, green fabric, grassAll Reference map----Reference mapsPartial-
Zhang et al. [83]AVIRIS (20 m)Andradite, chalcedony, kaolinite, jarosite, montmorillonite, nontroniteAllReference map--The whole study areaThe whole study areaReference maps--
Zhang et al. [93]AVIRIS (10/20 m)Dumortierite, muscovite, Alunite+muscovite, kaolinite, alunite, montmorillonite
Tree, water, road, soil
AllReference map--The whole study areaThe whole study areaReference maps--
Zhang et al. [129]Landsat-8 OLI (30 m)Vegetation, impervious surfacesAll GaoFen-1 image (2–8 m)Photointerpretation-101 samplesUniformReference fractional abundance mapsPartial-
Zhang et al. [130]Landsat-8 OLI (30 m)VegetationAllGaoFen-1 image (2–8 m)Object-based classification-101 samplesUniformReference fractional abundance mapPartial-
Zhang et al. [88] AVIRIS (10/20 m)Cuprite, road, trees, water, soil Asphalt, dirt, tree, roofAllReference map
Reference map
--The whole study areaThe whole study areaReference maps--
Zhao et al. [84] AVIRIS (10 m)Road, trees, water, soil
Asphalt, grass, tree, roof, metal, dirt
AllReference map
Reference map
--The whole study areaThe whole study areaReference maps--
Zhao et al. [96] AVIRIS (10 m)Road, trees, water, soilAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Road, roof, soil, grass, trail, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
Zhao et al. [94] AVIRIS (10 m)Road, trees, water, soilAllReference map--The whole study areaThe whole study areaReference maps--
Zhao et al. [95] AVIRIS (20 m)Andradite, chalcedony, kaolinite, jarosite, montmorillonite, nontroniteAllReference map--The whole study areaThe whole study areaReference maps--
Zhao et al. [136]Landsat-8 OLI (30 m)
Sentinel-2 (10–20–60 m)
Impervious surfaces, vegetation,
soil, water
2WorldView-2 image (0.50–2 m)--172 polygons (480 × 480 m)RandomReference fractional abundance mapsFull-
Zhao et al. [140]Landsat (30 m)
Spot (30 m)
Vegetation1Fractional vegetation cover reference maps (provided by VALERI project and the ImagineS)-In situ measurements of LAI (provided by VALERI project and the ImagineS)445 squares (20 × 20 m
or 30 × 30 m)
-Reference fractional abundance mapFull-
Zhao & Qin [168]Sentinel-2 (10–20–60 m)Vegetation, mineral areaAllIn situ data----Reference fractional abundance mapsPartial-
Zhu et al. [64]AVHRR (1–5 km)Snow,
non-snow (bare land, vegetation, and water)
1Landsat TM image (30 m)Normalized difference snow index-Landsat imageRepresentative areaReference fractional abundance mapFullSpatial resolution variation
Zhu et al. [97]AVIRIS (10 m)Road, trees, water, soilAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (10 m)Road, roof, soil, grass, trail, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
Samson (3.2 m)Soil, tree, waterAllReference map--The whole study areaThe whole study areaReference maps--
Table A2. Main characteristics of the eligible papers that were published in 2021.
Table A2. Main characteristics of the eligible papers that were published in 2021.
PaperRemote ImageDetermined EndmembersValidated EndmembersSources of Reference DataMethod for Mapping the Endmembers Validation of Reference Data with Other Reference DataSample Sizes and Number of Small Sample Sizes Sampling DesignsReference DataEstimation of Fractional AbundancesError in Co-Localization and Spatial Resampling
Azar et al. [174] AVIRIS
CASI
Trees, Mostly Grass Ground Surface, Mixed Ground Surface, Dirt/Sand, RoadAll
All
Reference map
CASI image
-
Photointerpretation
-The whole study areaThe whole study areaReference map
Reference map
--
Badola et al. [226]AVIRIS-NG (5 m) Sentinel-2 (10–20–60 m)Black Spruce Birch Alder GravelAllIn situ dataPhotointerpretationIn situ observations29 plotsRandomReference map--
Bai et al. [175]AVIRISAsphalt, Grass, Tree, Roof, Metal, DirtAllReference map--The whole study areaThe whole study areaReference map--
Bair et al. [254]Landsat
MODIS
Snow, canopy1WorldView-2–3 images (0.34–0.55 m)PhotointerpretationAirborne Snow Observatory (ASO) (3 m)--Reference fractional abundance mapFullSpatial resampling the reference maps
Evaluation of the errors in co-localization and spatial-resampling
Benhalouche et al. [230] HYDICE (10 m)
Samson (3.2)
Asphalt, grass, tree, roof
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Benhalouche et al. [265]PRISMA (30 m)MineralAllGeological map--The whole study areaThe whole study areaReference map--
Borsoi et al. [176]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Cerra et al. [238]HySpexTargetAllIn situ data-Reference targets and Aeronet data--Reference fractional abundance maps--
Chang et al. [229] GF-5 (30 m)
Sentinel 2 (10–20–60 m)
ZY-1-02D (30 m)
-AllIn situ data----Reference fractional abundance maps--
Chen et al. [239] Landsat-AllUAV images-Ground survey data--Reference fractional abundance maps--
Chen et al. [245]LandsatVegetation, impervious surface, bare soil, and waterAllGoogle Earth images----Reference fractional abundance maps--
Chen et al. [246]Landsat-AllGoogle Earth images-Field surveys300 plotsRandomReference fractional abundance maps--
Converse et al. [247]LandsatGreen vegetation, non-photosynthetic vegetation, soilAllUAS images-Field surveysPlots-Reference fractional abundance mapsFull-
Di et al. [177]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Dong & Yuan [178]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Dong et al. [179]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Dong et al. [180]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Dutta et al. [248]LandsatVegetation, impervious surface, bare soil,1In situ data-Built-up density,
urban expansion and population density of the area
--Reference fractional abundance mapsFull-
Ekanayake et al. [181] AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Elrewainy & Sherif [182]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Feng & Fan [255]Landsat (30 m)
Sentinel 2 (10–20–60 m)
Vegetation, high-albedo impervious surface, low-albedo impervious surface soilAllIn situ data--18000 testing areasrandomReference fractional abundance mapsFull-
Fernández-García et al. [256]Landsat (30 m)Arboreal vegetation, shrubby vegetation, herbaceous vegetation, rock and bare soil, waterAllOrthophotographs (0.25 m)--250 plots (30 × 30 m)randomReference fractional abundance mapsFullSpatial resolution variation
Finger et al. [249]Landsat (30 m)-AllCalifornia Department of Fish and Wildlife (CDFW) aerial survey canopy area product----Reference fractional abundance mapsFull-
Gu et al. [183]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Guo et al. [184]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Gu et al. [185]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Han et al. [186]AVIRISAsphalt, grass, tree, roofAllReference map--The whole study areaThe whole study areaReference map--
Han et al. [268]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Haq et al. [234]Hyperion (30 m)Clean snow,
blue ice, refreezing ice dirty snow, dirty glacier ice, firn, moraine, and glacier ice
AllIn situ data-Sentinel-2 images--Reference fractional abundance mapsFull-
He et al. [231]HYDICE (10 m)
MODIS (0.5–1 km)
-All
All
Reference map
Finer Resolution Observation and
Monitoring of Global Land Cov (30 m)
---
61 scenes
-Reference fractional abundance mapsFull-
He et al. [56]ROSIS (4m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference map--
Hua et al. [187]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Hua et al. [188]AVIRIS
Samson (3.2)
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Huang et al. [189]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Jia et al. [190]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Ji et al. [235]Hyperion (30 m)Photosynthetic vegetation, non-photosynthetic vegetation, bore soilAllReference map--The whole study areaThe whole study areaReference map--
Jiji [250]Landsat (30 m)Heavy metalsAllIn situ data--17 samplesRandomReference fractional abundance mapsFull-
Jin et al. [267]ROSIS (4 m)
Samson (3.2 m)
Urban surface materials
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Kneib et al. [271]Sentinel 2 (10–20–60 m)-allPleiades images (2 m)Photointerpretation---Reference fractional abundance mapsFull-
Kucuk & Yuksel [202]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Kumar & Chakravortty [191]AVIRIS
ROSIS (4 m)
-
Urban surface materials
AllReference map--The whole study areaThe whole study areaReference map--
Li et al. [203]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Li et al. [192]AVIRIS
HYDICE (10 m)
Cuprite
-
AllReference map--The whole study areaThe whole study areaReference map--
Li et al. [193] AVIRIS AllReference map--The whole study areaThe whole study areaReference map--
Li [194]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Li et al. [195]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Li et al. [196]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Li et al. [197]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Li et al. [251]Landsat (30 m)Impervious, vegetation, bare land, waterAllGoogle Earth images-Field surveys4296 sampled pointsRandomReference fractional abundance mapsFull-
Li [257]Landsat (30 m)Impervious, soil, vegetationAllImages--200 sample pointsRandomReference fractional abundance mapsFull-
Li et al. [204]AVIRIS
HYDICE (10 m)
-AllReference map--The whole study areaThe whole study areaReference map--
Li et al. [205]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Liu et al. [206]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Lui & Zhu [207]AVIRIS
Samson (3.2 m)
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Lombard & Andrieu [240]Landsat-3Google Earth imagesPhointerpretation-8490 sample pointsRandomReference fractional abundance mapsFull-
Luo & Chen [260]LandsatVegetation, impervious, soil1Gaofen-2 and WorldView-2 images----Reference fractional abundance mapsFullSpatial resolution variation
Ma et al. [276]WorldView-3VegetationAllDigital cover photography-Vegetation spectra30 sample points-Reference fractional abundance mapFull-
Mudereri et al. [273]Sentinel 2 (10–20–60 m)-AllGoogle Earth images-Field surveys1370 pixelsRandomReference fractional abundance mapsFull-
Muhuri et al. [258]Landsat
Sentinel 2 (10–20–60 m)
Snow coverAllIn situ data-Airborne Snow Observatory (ASO) (2 m)--Reference fractional abundance mapsFull-
Okujeni et al. [228]Simulated EnMAP-AllGoogle Earth images-Landsat images3183 sitesRandomReference fractional abundance mapsFull-
Ou et al. [233]HyMap (4.5 m)Soil organic matter, soil heavy metaAllIn situ data--95 soil samplesRandomReference fractional abundance mapsFull-
Pan et al. [261]MODIS (0.5–1 km)SnowAllLandsat imagesMESMA-The whole study areaThe whole study areaReference fractional abundance mapsFull-
Patel et al. [208]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Peng et al. [209]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Qin et al. [210]AVIRIS
Samson (3.2 m)
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Racoviteanu et al. [241]LandsatDebris-covered glaciersAllPléiades 1A image (2 m)
RapidEye image (5 m) PlanetScope (3 m)
PhointerpretationDEM151 test pixelsRandomReference fractional abundance mapsFull-
Rittger et al. [262]MODIS (0.5–1 km)SnowAllLandsat images---RandomReference fractional abundance mapsFullSpatial resolution variation
Sall et al. [252]Landsat (30 m)WaterbodiesAllDigitalGlobe WorldView-2 (0.46 m)-National AgricultureImagery Program (NAIP)--Reference fractional abundance mapsFull-
Sarkar & Sur [173]ASTER (15–30–90 m)Bauxite mineralsAllIn situ data-Petrological, EPMA, SEM-EDS studies
DEM
--Reference fractional abundance mapsFull-
Seydi & Hasanlou [236] Hyperion (30 m)-AllIn situ data--73505 samplesRandomReference fractional abundance mapsFull-
Seydi & Hasanlou [237]Hyperion (30 m)-AllIn situ data----Reference fractional abundance mapsFull-
Shahid & Schizas [211]AVIRIS
Samson (3.2 m)
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Shen et al. [242] Landsat (30 m)Impervious, non-impervious surfaceAllLand use map by the National Basic Geographic
Information Center
----Reference map--
Shen et al. [270]Sentinel 2 (10–20–60 m)-AllGoogle Earth imagesPhointerpretation-467 polygonsRandomReference fractional abundance mapsFull-
Shumack et al. [243]Landsat (30 m) Sentinel 2 (10–20–60 m)Bare soil, photosynthetic vegetation, non-photosynthetic vegetationAllOrthorectified mosaic images (0.02 m)Object based image analysesSLATS dataset of fractional ground cover surveys400 point per imagesRandomReference fractional abundance mapsFull-
Song et al. [232]HYDICE (10 m)
Samson (3.2 m)
Road, trees, water, soil
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Soydan et al. [272]Sentinel 2 (10–20–60 m)-AllLaboratory analysis of field collected samples through Inductive Coupled Plasma-Laboratory analysis of field collected samples through X-Ray Diffraction, and ASD spectral analysis--Reference fractional abundance mapsFull-
Su et al. [212]AVIRIS
HYDICE (10 m)
Hyperion (30 m)
-
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Sun et al. [263]MODIS (0.5–1 km)Green vegetation, sand, saline, and dark surfaceAllGoogle Earth images
Field observations
--89 samples
10 plots (1 × 1 km)
RandomReference fractional abundance mapsFullSpatial resolution variation
Sun et al. [275]WorldView-2Mosses, lichens, rock,
water, snow
In situ data -Photos and spectra32 plots (2 × 2 m)RandomReference fractional abundance maps--
Tan et al. [198]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Vibhute et al. [213]AVIRISTree, soil, water, roadAllReference map--The whole study areaThe whole study areaReference map--
Wan et al. [214]AVIRIS
HYDICE (10 m)
Samson (3.2 m)
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Wang et al. [215]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Vermeulen et al. [244]Landasat
Sentinel 2 (10–20–60 m)
Soil, Photosynthetic Vegetation, Non-Photosynthetic
Vegetation
AllImages, field data--(10 × 10 m) plots-Reference fractional abundance maps--
Wang et al. [199]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Wang et al. [216]AVIRIS
HYDICE (10 m)
-AllReference map--The whole study areaThe whole study areaReference map--
Wang [217]AVIRIS
ROSIS (4 m)
-
Urban surface materials
AllReference map--The whole study areaThe whole study areaReference map--
Wang et al. [200]AVIRIS
ROSIS (4 m)
-
Urban surface materials
AllReference map--The whole study areaThe whole study areaReference map--
Wu et al. [253]Landsat
Sentinel 2 (10–20–60 m)
Bare soil, agricultural crop
Water, vegetation, urban
AllGoogle MapsPhointerpretation---Reference fractional abundance mapsFull-
Xiong et al. [201]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Xiong et al. [218] AVIRIS
HYDICE (10 m)
-
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Xu et al. [219]AVIRIS CupriteAllReference map--The whole study areaThe whole study areaReference map--
Xu & Somers [269]Sentinel 2 (10–20–60 m)Vegetation, soil, impervious surfaceAllGoogle Earth imagesObject-oriented classification---Reference fractional abundance mapsFull-
Yang et al. [264] MODIS (0.5–1 km)Vegetation, soilAllGF-1, Google Earth images--2044 samples (0.5 × 0.5 km)RandomReference fractional abundance mapsFull-
Ye et al. [220]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Yu et al. [227]Landasat (30 m)
CASI
-AllGF-1 image (2 m)
GeoEye image (2 m)
Reference map
Classification-The whole study areaThe whole study areaReference fractional abundance mapsPartial-
Yuan et al. [274]UAV multispectral image-AllIn situ data--67 samples-Reference fractional abundance mapsFull-
Yuan & Dong [221]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Yuan et al. [222]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Zang et al. [259]LandsatVegetation, soil, impervious surfaceAllGoogle Earth Pro image Night light data, population data at township scale, administrative data120 samplesRandomReference fractional abundance mapsFull-
Zhang & Pezeril [223]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Zhao et al. [266]ROSIS (4 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference map--
Zheng et al. [224]AVIRIS
Samson (3.2 m)
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Zhu et al. [225]AVIRIS
Samson (3.2 m)
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Table A3. Main characteristics of the eligible papers that were published in 2020.
Table A3. Main characteristics of the eligible papers that were published in 2020.
PaperRemote ImageDetermined EndmembersValidated EndmembersSources of Reference DataMethod for Mapping the Endmembers Validation of Reference Data with Other Reference DataSample Sizes and Number of Small Sample Sizes Sampling DesignsReference DataEstimation of Fractional AbundancesError in Co-Localization and Spatial Resampling
Aalstad et al. [340] Landsat
MODIS
Sentinel2
Shadow, cloudy, snow,
snow-free
All305 terrestrial imagesClassificationDEM--Reference fractional abundance mapsFull-
Aldeghlawi et al. [334]HYDICEUrban surface materialsAllReference maps--The whole study areaThe whole study areaReference map--
Arai et al. [368]PROBA-V Vegetation, soil, shadeAllLandsat images (30 m)Calculate Geometry
function
Land use and land cover map produced by the MapBiomas
Project and the Agricultural Census
298 sampling unitsUniformReference fractional abundance mapsFullSpatial resampling the reference maps
Bai et al. [281]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Benhalouche et al. [278]ASTER-AllIn situ data--2 samples-Reference fractional abundance mapsFull-
Binh et al. [341]Landsat-AllGoogle Earth imagesPhointerpretationField surveys--Reference fractional abundance mapsFullEvaluation of the errors in co-localization and spatial-resampling
Borsoi et al. [283]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Borsoi et al. [282]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Borsoi et al. [176]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Bullock et al. [349]Landsat-AllIn situ data--500 samplesRandomReference fractional abundance mapsFull-
Carlson et al. [377]Sentinel (10–20–60 m)-AllIn situ data-Aerial photograhs-RandomReference fractional abundance mapsFull-
Chen et al. [299]AVIRIS
HYDICE
-
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Cheng et al. [543]Hyperspectral-AllIn situ data---RandomReference fractional abundance mapsFullEvaluation of the errors in co-localization and spatial-resampling
Cooper et al. [330]Simulated EnMAP (30 m)-AllGoogle Earth imagesPhointerpretation-260 polygons (90 × 90 m)RandomReference fractional abundance mapsFull-
Czekajlo et al. [350]Landsat-AllGoogle Earth imagesPhointerpretation-1085 grids (6 × 6 m)RandomReference fractional abundance mapsFull-
Dai et al. [351]Landsat-AllIn situ data DEM2223 samples sitesRandom
Das et al. [300]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Dou et al. [301]AVIRIS
Samson
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Drumetz et al. [329] CASI -AllReference map--The whole study areaThe whole study areaReference map--
Elkholy et al. [284]AVIRIS
Samson
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Fang et al. [285]AVIRIS
ROSIS
-
Urban surface materials
AllReference map--The whole study areaThe whole study areaReference map--
Fathy et al. [286]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Fernández-Guisuraga et al. [342]Landsat
WorldView-2
Photosynthetic vegetation, non-photosynthetic vegetation, soil and shadeAllIn situ data--85 (30 × 30 m) field plots 360 (2 × 2 m) field plotsRandomReference fractional abundance mapsFullCo-localization the maps
Firozjaei et al. [364]MODIS-AllLandsat images-Annual primary energy consumption, Global gridded population density, Population size data, Normalized difference vegetation index (NDVI)
Data, CO and NOx emissions
The whole study areaThe whole study areaReference fractional abundance mapsFull-
Fraga et al. [378]Sentinel-2 (10–20–60 m)-AllIn situ data- 15 sampling pointsRandomReference fractional abundance mapsFull-
Gharbi et al. [545]Hyperspectral-AllReference map--The whole study areaThe whole study areaReference map--
Girolamo-Neto et al. [379]Sentinel-2 (10–20–60 m)-AllIn situ data 461 field observationsRandomReference fractional abundance mapsFull-
Godinho Cassol et al. [369]PROBA-V Vegetation, soil, shadeAllLandsat images (30 m)--622 sampling unitsUniformReference fractional abundance mapsFull-
Han et al. [287]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
He et al. [356]Landsat-AllIn situ data-Photos118 field sitesRandomReference fractional abundance mapsFull-
Holland & Du [288]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Hua et al. [289]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Huang et al. [302]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Huechacona-Ruiz et al. [380]Sentinel-2 (10–20–60 m)-AllIn situ data-GPS288 sampling unitsRandomReference fractional abundance mapsFull-
Imbiriba et al. [303] AVIRIS
Samson
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Jarchow et al. [358]Landsat -AllWorldView-2 (0.5 m)-National Agriculture
Imagery Program (NAIP) scene
154 podsRandomReference fractional abundance mapsFull-
Ji et al. [333]GF1
Landsat
Sentinel-2 (10–20–60 m)
-AllIn situ data-GPS111 surveyed fractional-cover sitesRandomReference fractional abundance mapsFull-
Jiang et al. [304]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Karoui et al. [290]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Khan et al. [352]Landsat-AllIn situ data-GPS, “Land Use, Land Use Change and Forestry Projects”108 circular sample plotsRandomReference fractional abundance mapsFull-
Kompella et al. [328] AWiFS
Sentienl-2 (10–20–60 m)
-AllIn situ data-GPS2 sampling areas-Reference fractional abundance mapsPartialCo-localization the maps
Laamrani et al. [343]Landsat -AllPhotographs-Field surveys, GPS 70 (30 × 30 m) sampling area-Reference fractional abundance mapsFullCo-localization the maps
Lewińska et al. [359]MODISSoil, green vegetation, non-photosynthetic vegetation shade Land cover classifications (30 m), Map of the Natural Vegetation of Europe--The whole study areaThe whole study areaReference fractional abundance mapsFull-
Li et al. [305]AVIRIS
Samson
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Li [360]LandsatVegetation, high albedo, low albedo, soilAllOrthophotography images, Google Earth images--The whole study areaThe whole study areaReference fractional abundance mapsFull-
Ling et al. [365]MODISwater and landAllRadar altimetry water levels--The whole study areaThe whole study areaReference fractional abundance mapsFull-
Liu et al. [332]GF1
GF2
Landsat
Sentinel-2 (10–20–60 m)
Water, vegetation, soilAllGoogle Earth images Meteorological data129 sample points Reference fractional abundance mapsFull-
Lu et al. [306]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Lymburner et al. [348]Landsat-AllLIDAR survey--100 (10 × 10 km) tilesRandomReference fractional abundance mapsFull-
Lyu et al. [338]Hyperion (30 m)-AllIn situ data-Land use data36 plotsRandomReference fractional abundance mapsFull-
Markiet & Mõttus [277]AISA Eagle II airborne hyperspectral scanner--In situ data-Site fertility class, tree species composition, diameter at breast height, median tree height, effective leaf area index calculated from canopy gap fraction250 plotsRandomReference fractional abundance mapsFull-
Mei et al. [307]AVIRIS
HYDICE
-
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Moghadam et al. [336]HyMap
Hyperion (30 m)
-AllGeological map--The whole study areaThe whole study areaReference fractional abundance mapsPartial-
Montorio et al. [339]Landsat
Sentinel-2 (10–20–60 m)
-AllPléiades-1A orthoimage--275/280 plotsRandomReference fractional abundance mapsFull-
Park et al. [546]Hyperspectral -AllIn situ data----Reference fractional abundance mapsFull-
Patel et al. [372]ROSIS Urban surface materialsAllReference map--The whole study areaThe whole study areaReference map--
Peng et al. [297]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Peroni Venancio et al. [347]Landsatphotosynthetic vegetation, soil/non-photosynthetic vegetationAllIn situ data---RandomReference fractional abundance mapsFull-
Qi et al. [312]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map-
Qi et al. [308]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Qian et al. [309]AVIRIS
HYDICE
-
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Qu & Bao [321]AVIRIS
HYDICE
-AllReference map--The whole study areaThe whole study areaReference map--
Quintano et al. [381] Sentinel-2 (10–20–60 m)Char, green vegetation, non-photosynthetic vegetation, soil, shadeAllOfficial
burn severity (three severity levels) and fire perimeter maps provided by Portuguese Study Center of Forest Fires
--The whole study areaThe whole study areaReference map--
Rasti et al. [320]AVIRIS
Samson
-
Trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Redowan et al. [371]Landsat -AllGoogle Earth images--Representative areasRepresentative areasReference fractional abundance mapsFull-
Rathnayake et al. [293]AVIRIS
HYDICE
-
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Salvatore et al. [385]WorldView-2 WorldView-3-AllIn situ data----Reference fractional abundance mapsFull-
Sall et al. [252]Landsat-AllWorldView-2 (0.46 m) National Agriculture Imagery Program (NAIP89 waterbodiesThe whole study areaReference fractional abundance mapsFull-
Salehi et al. [280]HyMap
ASTER
Landsat
Sentinel-2
-AllIn situ data-Geological map, X-ray fluorescence analysis--Reference fractional abundance mapsFull-
Senf et al. [345]Landsat -AllAerial images--360 sample areasRandomReference fractional abundance mapsFull-
Shah et al. [313]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Shih et al. [354]Landsat Vegetation, Impervious, SoilAllGoogle Earth VHR images--107 (90 × 90 m) samplesRandomReference fractional abundance mapsPartial
Shimabukuro et al. [370] PROBA-V-AllSentinel-2--Representative areasRepresentative areasReference fractional abundance mapsFull-
Shimabukuro et al. [353]Landsat
Suomi NPP-VIIRS
ROBA-V
-AllSentinel-2
MODIS
-Annual classifications of the Program for Monitoring Deforestation in the Brazilian Amazon (PRODES),
Global Burned Area Products (Fire CCI, MCD45A1,MCD64A1)
--Reference fractional abundance mapsPartial-
Siebels et al. [319]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Sing & Gray [363]Landsat-AllIn situ data--346 field plotsRandomReference fractional abundance mapsFull-
Sun et al. [331]GF-1-AllGoogle Earth
images
--4500 pixelsRandomReference fractional abundance mapsFull-
Takodjou Wambo et al. [279]ASTER
Landsat
-AllIn situ data-Geological map, X-ray diffraction analysis7 outcrops, 53 rock samples-Reference fractional abundance mapsFull-
Tao et al. [315]AVIRIS
Samson
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Thayn et al. [357]Landsat-AllLow-altitude aerial imagery collected from a DJI Mavic Pro drone--Representative areasRepresentative areasReference fractional abundance mapsFull-
Tong et al. [311]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Topouzelis et al. [382] Sentinel-2 (10–20–60 m)-AllUnmanned Aerial System images--Representative areasRepresentative areasReference fractional abundance mapsFull-
Topouzelis et al. [383]Sentinel-2 (10–20–60 m)-AllUnmanned Aerial System images--Representative areasRepresentative areasReference fractional abundance mapsFull-
Trinder & Liu [344]Landsat-AllZiyuan-3 image, Gaofen-1 satellite image,----Reference fractional abundance mapsFull-
Uezato et al. [325]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Vijayashekhar et al. [292]AVIRIS
HYDICE
-
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Wang et al. [375]SamsonSoil, tree, waterAllReference map--The whole study areaThe whole study areaReference map--
Wang et al. [366]PlanetScope (3 m)Green vegetation Non-photosynthetic vegetationAllIn situ data Field measurements of LAI, phenocam-based leafless tree-crown fraction, phenocam-based leafy tree-crown fractionnonoReference fractional abundance mapsFullExpansion of the windows of field sample size
Wang et al. [346]LandsatWater, urban, agriculture, forestAllReference map--The whole study areaThe whole study areaReference map--
Wang et al. [373]ROSIS (4 m)Urban surface materialsAllReference map--The whole study areaThe whole study areaReference map--
Wang et al. [322]AVIRIS
HYDICE
-AllReference map--The whole study areaThe whole study areaReference map--
Wright & Polashenski [362]MODIS (0.5 m) -AllWorldView-2 (0.46 m)
WorldView-3 (0.31 m)
- Representative areasRepresentative areasReference fractional abundance mapsFull-
Xiong et al. [323]AVIRIS
Samson
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Xu et al. [295]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Xu et al. [296]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Xu et al. [316]AVIRIS
HYDICE
-
Road, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Xu et al. [318]AVIRIS
HYDICE
-
Asphalt, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Yang & Chen [294]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Yang et al. [327]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Yang et al. [298]AVIRIS
HYDICE
-
Asphalt, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Yang et al. [374]Samson Soil, tree, waterAllReference map--The whole study areaThe whole study areaReference map--
Yin et al. [355]Landsat-AllGoogle Earth images--500 samplesRandomReference fractional abundance mapsFull-
Yuan et al. [314] AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Yue et al. [376]Sentinel-2 (10–20–60 m)-AllDigital photos--The whole study areaThe whole study areaReference fractional abundance mapsFull-
Zeng et al. [317]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Zhang et al. [337]HySpex (0.7 m) Google Earth images----Reference fractional abundance mapsFull-
Zhang et al. [384]UAV hyperspectral data-AllIn situ data-Laboratory analysis35 samples-Reference fractional abundance mapsFull-
Zhang et al. [326]AVIRIS CupriteAllReference map--The whole study areaThe whole study areaReference map--
Zhou et al. [310]AVIRIS
HYDICE
-
Asphalt, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Zhou et al. [324]AVIRIS
HYDICE
Samson
-
Soil, tree, water
AllReference map--The whole study areaThe whole study areaReference map--
Zhou et al. [291]AVIRIS (16 m)
AVIRIS NG (4 m)
Turfgrass, non-photosynthetic vegetation (NPV), paved, roof, soil, and treeAllNAIP high-resolution images (1 m)--64 regions of interest (180 × 180 m)RandomReference fractional abundance mapsPartial-
Zhu et al. [335]HYDICEAsphalt, trees, water, soil AllReference map--The whole study areaThe whole study areaReference map--
Table A4. Main characteristics of the eligible papers that were published in 2011.
Table A4. Main characteristics of the eligible papers that were published in 2011.
PaperRemote ImageDetermined EndmembersValidated EndmembersSources of Reference DataMethod for Mapping the Endmembers Validation of Reference Data with Other Reference DataSample Sizes and Number of Small Sample Sizes Sampling DesignsReference DataEstimation of Fractional AbundancesError in Co-Localization and Spatial Resampling
Altmann et al. [404]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Ambikapathi et al. [405] AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Bartholomeus et al. [386]AHSMaizeAllIn situ data--14 samplesRandomReference fractional abundance mapsPartial-
Bouaziz et al. [420]MODIS-AllIn situ data--102 samplesRandomReference fractional abundance mapsPartial-
Canham et al. [406]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Cao et al. [429]HJ-1 (30 m)-AllIn situ data--13 sample plotsRandomReference fractional abundance maps--
Castrodad et al. [392]AVIRIS
HYDICE
HyMAP
-
Asphalt, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Chen et al. [430]HJ-1 (30 m)-AllIn situ data--13 sample plotsRandomReference fractional abundance maps--
Chudnovsky et al. [428] Hyperion (30 m)-AllIn situ data-Bulk mineral, geo-chemical composition8 samples-Reference fractional abundance maps--
Cui et al. [421]MODIS (0.5–1 km)-AllLandsat image--Landsat imageRepresentative areaReference fractional abundance maps Partial-
de Jong et al. [427]HyMAP (5 m)-AllIn situ data-Physical characterization, infiltration measurements107 plotsRandomReference fractional abundance maps--
Dopido et al. [393]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Eches et al. [407]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Ghrefat & Goodell [387]ASTER
AVIRIS
Hyperion
Landsat
-AllIn situ data----Reference fractional abundance maps--
Gilichinsky et al. [439]Landsat
SPOT
--In situ data --229 validation areasRandomReference fractional abundance maps--
Gillis & Plemmons [424]HYDICEAsphalt, trees, water, soil AllReference map--The whole study areaThe whole study areaReference map--
Griffin et al. [431]Landsat-AllIn situ data --304 samplesRandomReference fractional abundance mapsFull-
Halimi et al. [394]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Halimi et al. [408]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Hamada et al. [441] QuickBird (0.6–2.4 m)
SPOT (10 m)
-AllInfrared aerial photography (0.15 m)Phointerpretation-30 samplesRandomReference fractional abundance mapsFullSpatial resolution variation
Heylen et al. [395]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Heylen et al. [396]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Heylen & Scheunders [397]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Hosseinjani & Tangestani [388]ASTER-AllIn situ data -Geological map, X-ray diffraction analysis8 samplesRandomReference fractional abundance mapsFull-
Hu & Weng [390]ASTER-AllImages--Representative areaRepresentative areaReference fractional abundance mapsFull-
Iordache et al. [398]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Iordache et al. [409]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Ji & Feng [442]QuickBird (2.4 m)-AllQuickBird (0.6 m)--The whole study areaThe whole study areaReference fractional abundance mapsPartial-
Jiao et al. [434]Landsat -AllAirborne images--Representative areaRepresentative areaReference fractional abundance mapsFull-
Kamal & Phinn [418]CASI-AllMap of the mangrove speciesderived from aerial photographic interpretation at scale of 1:25,000 Provided by Queensland Herbarium/Environmental Protection Agency (EPA)--400 samplesRandomReference fractional abundance mapsPartial-
Knight & Voth [422]MODIS-AllLandsat image--The whole study areaThe whole study areaReference fractional abundance mapsFull-
Liu et al. [399]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Lu et al. [435]LandsatHigh-albedo, low-albedo, vegetation, soilAllQuickBirdHybrid method-250 pointsRandomReference fractional abundance mapsPartialSpatial resolution variation
Lu et al. [432]LandsatHigh-albedo, low-albedo, vegetation, soilAllQuickBirdHybrid method-1512 samplesRandomReference fractional abundance mapsPartial-
Lu et al. [423]Landsat
MODIS
Forest and non-forest
Vegetation, shade and soil
AllAnnual classifications of the Program for Monitoring Deforestation in the Brazilian Amazon (PRODES)-Official truth-terrain data from deforested and non-deforested areas prepared by PRODES--Reference fractional abundance mapsFull-
Martin & Plaza [410]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Martin et al. [411]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Mei al. [307]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Mei & He [412]AVIRISCupriteAllReference map--The whole study areaThe whole study areaReference map--
Mianji et al. [400]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Negrón-Juárez et al. [433]LandsatPhotosynthetic vegetation, non-photosynthetic vegetationAllIn situ data--30 pixelrandomReference fractional abundance mapsPartial-
Qian et al. [425]HYDICEAsphalt, trees, water, soil AllReference map--The whole study areaThe whole study areaReference map--
Reno et al. [436]Landsat Vegetation, soil, waterAllIn situ data-Photos, botanical observations168 ground points -Reference fractional abundance mapsFull-
Sankey & Glenn [437] Landsat -AllIn situ data--100 plots (30 × 30 m)RandomReference fractional abundance mapsFull-
Sunderman & Weisberg [438]Landsat-AllIn situ data--400 plots RandomReference fractional abundance mapsFull-
Swatantran et al. [401] AVIRIS-AllIn situ data-Laser Vegetation Imaging Sensor125 field plots classified based on WHR type for analysis by species/vegetation typeRandomReference fractional abundance mapsFull-
Vicente & de Souza Filho [389]ASTER-AllIn situ data-X-ray diffraction analysis on the same samples42 soil samplesRandomReference fractional abundance mapsFull-
Villa et al. [413]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Weng et al. [391]ASTERGreen vegetation, soils low-albedo surfaces
and high-albedo surface
AllOther ASTER imagesSame procedures-The whole study areaThe whole study areaReference fractional abundance mapsFull-
Xia et al. [414]AVIRIS
HYDICE
-
Asphalt, trees, water, soil
AllReference map--The whole study areaThe whole study areaReference map--
Xia et al. [402]AVIRIS-AllReference map--The whole study areaThe whole study areaReference map--
Yang et al. [415]AVIRIS
HYDICE
-AllReference map--The whole study areaThe whole study areaReference map--
Youngentob et al. [426]HyMap (3.5 m)-AllIn situ data--99 isolated eucalypt paddock treesRandomReference fractional abundance mapsFull-
Zare [403]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map--
Zhan et al. [416]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map-
Zhao et al. [417]AVIRIS -AllReference map--The whole study areaThe whole study areaReference map-
Zurita-Milla et al. [419]MERIS-AllHigh-spatial-resolution land-cover dataset (Dutch land-use database) (25 m)--The whole study areaThe whole study areaReference fractional abundance mapsFullSpatial resampling the reference maps
Table A5. Main characteristics of the eligible papers that were published in 2010.
Table A5. Main characteristics of the eligible papers that were published in 2010.
PaperRemote ImageDetermined EndmembersValidated EndmembersSources of Reference DataMethod for Mapping the Endmembers Validation of Reference Data with Other Reference DataSample Sizes and Number of Small Sample Sizes Sampling DesignsReference DataEstimation of Fractional AbundancesError in Co-Localization and Spatial Resampling
Alves Aguilar et al. [496]MODIS (0.5–1 km)Vegetation, soil1Landsat TM image (30 m)NDVIIn situ observationsLandsat imageRepresentative areaReference fractional abundance map Partial-
Biggs et al. [477]Landsat (30 m)Green vegetation, nonphotosynthetic vegetation, impervious surfaces, soil, shadeAllHigh
resolution imagery
Photointerpretation-38 squares RandomReference fractional abundance mapsFull-
Bolman [478]Landsat (30 m)Deciduous crowns, fully leaved crowns, shade2In situ data -17 plotsUniformReference fractional abundance maps Full-
Borfecchia et al. [489]Landsat (30 m)--QuickBird image (2.8 m)Maximum Likelihood
classification
Aerial photos The whole study areaThe whole study areaReference fractional abundance mapsFull
Castrodad et al. [471]HYDICE Trees, grass, roadAllReference map--The whole study areaThe whole study areaReference maps--
HyMAPConiferous trees,
deciduous trees, grass, water, crop, road, concrete, gravel
AllReference map--The whole study areaThe whole study areaReference maps--
Cavalli et al. [494]MIVIS (3 m)Vegetation, soil1Land cover map-In situ observations-RandomReference maps--
Chang et al. [458]AVIRIS (20 m)Cuprite, vegetation, soilAllReference map--The whole study areaThe whole study areaReference maps--
HYDICE (1.5 m)--Reference map--The whole study areaThe whole study areaReference maps--
Chen et al. [475]HJ-1 CCD (30 m)VegetationAllIn situ data-Land-use, land-cover, vegetation maps--Reference fractional abundance mapFull-
Eches et al. [457]AVIRIS (20 m)Cuprite, vegetation, soilAllReference map--The whole study areaThe whole study areaReference maps--
Eckmann et al. [496]MODIS (0.5–1 km)Fire1Band 9 of ASTER image (30 m)-GLC 2000 land-coverAster imageRepresentative areaReference map --
Elatawneh et al. [473]Hyperion (30 m)Land-cover classesAllQuickBird image-In situ observationsThe whole study areaThe whole study areaReference fractional abundance mapsFull-
Elmore & Guin [484]Landsat (30 m)Vegetation, substrate, and shadeAllAerial photographsPhotointerpretationLand cover based on aerial photography called GIRAS-RandomReference fractional abundance maps Full-
Estes et al. [447]ASTER (15–30–90 m)--In situ data--127 circles (11.3 m radius)-Reference fractional abundance mapsFullChange the windows of pixels
Gilichinsky et al. [492]Landsat (30 m)
SPOT (10 m)
Lichen classes1In situ data--229 plotsUniformReference fractional abundance mapsFull-
Golubiewski & Wessman [456]AVIRIS (20 m)Vegetation, soil, manmade
materials
AllIn situ data----Reference fractional abundance maps--
He et al. [485]Landsat (30 m)2 vegetations, waterAllQuickBird imageClassification-The whole study areaThe whole study areaReference fractional abundance mapsFull-
Hendrix et al. [464]CASI--In situ data--The whole study areaThe whole study areaReference maps--
Hu & Weng [445]ASTER (15–30–90 m)--QuickBird image (0.61 m)Classification-The whole study areaThe whole study areaReference fractional abundance mapsFull-
Huang et al. [479]Landsat (30 m)Fractional vegetation coverAllIn situ data--12 polygons (45 × 30 m)RandomReference fractional abundance mapFull-
Huang et al. [449]AVIRIS (20 m)Road, trees, lawn, path, roof, shadowAllReference map--The whole study areaThe whole study areaReference maps--
Huck et al. [459] AVIRIS (20 m)MineralsAllReference map--The whole study areaThe whole study areaReference maps--
Iordarche et al. [460] AVIRIS (20 m)MineralsAllReference map--The whole study areaThe whole study areaReference maps--
Jin et al. [450]AVIRIS (20 m)
AVIRIS (20 m)
Minerals
-
AllReference map--The whole study areaThe whole study areaReference maps--
Li et al. [482]Landsat (30 m)Low albedo, high albedo, soil, vegetationAllIn situ data--400 samplesRandomReference fractional abundance mapFull-
Liu et al. [491]Landsat (30 m)Urban, forest, water, cropland, grass, developing landAllQuickBird image (0.61 m)PhotointerpretationIn situ observations3000 samplesUniformReference fractional abundance mapFull-
Liu & Yue [486]Landsat TM (30 m)
SPOT (10–20 m)
Urban vegetation fractionAllIn situ data--samplesRandomReference fractional abundance mapFull-
Luo et al. [451]AVIRIS (20 m)--Reference map--The whole study areaThe whole study areaReference maps--
Luo et al. [452]AVIRIS (20 m)--Reference map--The whole study areaThe whole study areaReference maps--
Martin et al. [461]AVIRIS (20 m)Alunite, buddingtonite, calcite, kaolinite and muscoviteAllReference map--The whole study areaThe whole study areaReference maps--
Martin & Plaza [462]AVIRIS (20 m) Minerals AllReference map--The whole study areaThe whole study areaReference maps--
Martin & Plaza [462]AVIRIS (20 m) Minerals Field reference data --The whole of study area Reference maps--
Mei et al. [453]AVIRIS (20 m)VegetationAllReference map--The whole study areaThe whole study areaReference maps--
Mei et al. [454]AVIRIS (20 m)MineralAllReference map--The whole study areaThe whole study areaReference maps--
Meng et al. [476] HJ-1A/1B (30 m)Road, vegetation,
Building
AllAerial photoPhotointerpretation
Classification
-The whole study areaThe whole study areaReference fractional abundance mapsFull-
Meusburger et al. [497]QuickBird (2.4 m) Vegetations
Soil
-In situ data --43 plots (10 × 10 m)RandomReference fractional abundance mapFull-
Meusburger et al. [498] QuickBird (2.4 m) Vegetations
Soil
All In situ data --63 samplesRandomReference fractional abundance mapFull-
Mezned et al. [446]ASTER (30 m)
Landsat ETM+ (15 m)
Calcite, clays, gypsum, oxyhydroxides, pyriteAllIn situ data ---RandomReference fractional abundance mapsPartial-
Mucher et al. [444]AHS (2.4 m)Heathland vegetationAllIn situ data -Aerial photos104 circles (3 m radius) -Reference fractional abundance mapsFull-
Pacheco & McNairn [480]Landsat (30 m)
SPOT (20 m)
Vegetation, soil and residueAllDigital photographs-Soil Landscapes of Canada
Working Group, 2007
Digital imagesRepresentative areaReference fractional abundance mapsFullSize and spatial resolution of the reference maps
Pascucci et al. [101]ATM (2 m)
CASI (2 m)
Soil, vegetationAllLand cover map In situ observations25 samplesRandomReference fractional abundance mapsFull-
Plaza & Plaza [465]DAIS (6 m)Cork-oak trees, pasture, bare soilAllROSIS image (1.2 m)Maximum-likelihood supervised classification-The whole study areaThe whole study areaReference fractional abundance mapsFullCo-localization the maps
Powell & Roberts [483] Landsat (30 m)Vegetation, impervious soilAllAerial photos--41 samples-Reference fractional abundance mapsFull-
Raksuntorn et al. [463]AVIRIS (10 m) HYDICE (10 m)Minerals
-
All
-
Reference map
Reference map
--The whole study area
The whole study area
The whole study area The whole study areaReference maps
Reference maps
--
Ruescas et al. [448]AVHRR (1 km)Vegetation, burnt area, rocks, soilAllAHS image (6 m)Maximum likelihood classificationStatistic reports
provided by the Environmental Ministry of Spain
AHS imageRepresentative areaReference fractional abundance mapsFullEvaluation of the errors in co-localization and spatial-resampling
Sarapirome & Kulrat [493]Landsat (30 m)Vegetation, impervious soil;
vegetation, soil, shade
AllAir photos-In situ observations--Reference fractional abundance mapsFull-
Schmidt & Witte [499]SPOT (2.5–10 m)Water, soil, vegetationAllIn situ data--Polygons Random Reference maps--
Silván-Cárdenas & Wang [490]Landsat (30 m)VegetationsAllAISA image (1 m)Spectral angle mapper classificationIn situ observations300 points (30 × 30 m)RandomReference fractional abundance mapsFull-
Soenen et al. [500]SPOT (10–25 m)Sunlit canopy, sunlit background, shadowAllIn situ data--36 plots (400 m2) Random Reference fractional abundance mapsFullThe size of reference maps
Solans Vila & Barbosa [481]Landsat (15 m)Green vegetation, soil, shade, non-photosynthetic vegetationAllIn situ data---- Reference fractional abundance mapsFull-
Somers et al. [472]Landsat (30 m)
Hyperion (30 m)
Eucalyptus trees, soil, litter and grassAllIn situ data--46 plotsStratified randomReference fractional abundance mapFull-
Tommervik et al. [487] Landsat (30 m)VegetationsAllAerial photographs and QuickBird-2 imagePhotointerpretation-10 plotsRandomReference fractional abundance mapFull-
Verrelst et al. [467]CHRIS (17 m)Vegetation, snowAllAerial photographs--Aerial photographsRepresentative areaReference fractional abundance mapFull-
Villa et al. [455]AVIRIS (10 m) HYDICE (10 m)-
Asphalt, trees, water, soil
-
-
Reference map
Reference map
--The whole study area
The whole study area
The whole study area
The whole study area
Reference maps
Reference maps
--
Xiong et al. [470]HYDICE (10 m)--Reference map--The whole study areaThe whole study areaReference maps--
Yang & Everitt [443]Airborne hyperspectral image (about 1.5 m)Invasive weedsAllIn situ data--425 circular areas (diameter of 3 m)Stratified randomReference fractional abundance mapFull-
Yang et al. [488]Landsat TM (30 m)2Vegetation, impervious surfaces (low and high albedo), soilAllAerial photographsPhotointerpretation-138 samplesRandomReference fractional abundance mapsFull-
Table A6. Main characteristics of the eligible papers that were published in 1996.
Table A6. Main characteristics of the eligible papers that were published in 1996.
PaperRemote ImageDetermined EndmembersValidated EndmembersSources of Reference DataMethod for Mapping the Endmembers Validation of Reference Data with Other Reference DataSample Sizes and Number of Small Sample Sizes Sampling DesignsReference DataEstimation of Fractional AbundancesError in Co-Localization and Spatial Resampling
Ben-dor et al. [507]SPOTMineralAllGeological map-GER scanner dataThe whole study areaThe whole study areaReference fractional abundance map PartialCo-localization the maps
Bowers & Rowan [503]AVIRISMineralAllGeological map--The whole study areaThe whole study areaReference fractional abundance map Partial-
Hunt et al. [502]AVIRIS-AllLandsat imageUnconstrained linear spectral unmixing-The whole study areaThe whole study areaReference fractional abundance map Partial-
Rosenthal et al. [505]Landsat-AllHigh resolution aerial photographs--The whole study areaThe whole study areaReference fractional abundance map Full-
Thomas et al. [14] Landsat-AllImagesPhotointerpretation-The whole study areaThe whole study areaReference fractional abundance map Full-
Ustin et al. [501]AVIRIS-AllAerial photograph-Field based vegetation mapThe whole study areaThe whole study areaReference fractional abundance map Full-
Van der Meer [504]GERIS-AllMap--The whole study areaThe whole study areaReference fractional abundance map Partial-
Van der Meer [506]Landsat-AllMap--The whole study areaThe whole study areaReference fractional abundance map Partial-
Table A7. Main characteristics of the eligible papers that were published in 1995.
Table A7. Main characteristics of the eligible papers that were published in 1995.
PaperRemote ImageDetermined EndmembersValidated EndmembersSources of Reference DataMethod for Mapping the Endmembers Validation of Reference Data with Other Reference DataSample Sizes and Number of Small Sample SizesSampling DesignsReference DataEstimation of Fractional AbundancesError in Co-Localization and Spatial Resampling
Bianchi et al. [514]MIVIS (4 m)Oil, water, wood, cultivated field, smooth and grooved surface soil, rice field1In situ data--200 samplesUniformReference fractional abundance map Full-
Dwyer et al. [509]AVIRIS (20 m)MineralsAllGeological map-Remotely sensed and ground-based dataThe whole study areaThe whole study areaReference maps-
Hall et al. [515]MMRCanopy, canopy plus background, backgroundAllIn situ data----Reference fractional abundance map Full-
Kerdiles & Grondona [508]AVHRR (1 km)Vegetation, soilAllLandsat TM image (30 m)classification---Reference fractional abundance maps Full-
Lacaze et al. [510]AVIRIS (20 m)Vegetation, soil, rockAllLandsat TM image (30 m)classification---Reference fractional abundance maps Full-
Lavreau et al. [512]Landsat (30 m)VegetationAllLand cover map--- Reference maps- -
Rowan et al. [511]AVIRIS (20 m)MineralsAllGeological map--The whole study areaThe whole study areaReference maps--
Van Der Meer [513]Landsat (30 m)MineralsAllGeological map-In situ observationsThe whole study areaThe whole study areaReference fractional abundance maps Full-

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Figure 1. PRISMA flow chart showing the different steps of the dataset creation, where ntot was the total number of papers; n2022–2020 was the number of papers that were published in 2022, 2021, and 2020; n2011–2010 was the number of papers that were published in 2011 and 2010; n1996–1995 was the number of papers that were published in 1996 and 1995.
Figure 1. PRISMA flow chart showing the different steps of the dataset creation, where ntot was the total number of papers; n2022–2020 was the number of papers that were published in 2022, 2021, and 2020; n2011–2010 was the number of papers that were published in 2011 and 2010; n1996–1995 was the number of papers that were published in 1996 and 1995.
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Figure 2. Distribution of the papers that applied the spectral unmixing to remote images (orange box in the Figure 1) according to different ways in which their results were validated, where n2022–2020 was the number of papers that were published in 2022, 2021, and 2020; n2011–2010 was the number of papers that were published in 2011 and 2010; n1996–1995 was the number of papers that were published in 1996 and 1995.
Figure 2. Distribution of the papers that applied the spectral unmixing to remote images (orange box in the Figure 1) according to different ways in which their results were validated, where n2022–2020 was the number of papers that were published in 2022, 2021, and 2020; n2011–2010 was the number of papers that were published in 2011 and 2010; n1996–1995 was the number of papers that were published in 1996 and 1995.
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Figure 3. Distribution of the eligible papers according to the metrics employed to evaluate the spatial accuracy.
Figure 3. Distribution of the eligible papers according to the metrics employed to evaluate the spatial accuracy.
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Figure 4. Key issues in the spatial validation that were addressed by the eligible papers.
Figure 4. Key issues in the spatial validation that were addressed by the eligible papers.
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Figure 5. Distribution of the eligible papers that fully or partially validated endmembers determined with hyperspectral images (right) or multispectral images (left), where n was the number of papers considered in each pie chart.
Figure 5. Distribution of the eligible papers that fully or partially validated endmembers determined with hyperspectral images (right) or multispectral images (left), where n was the number of papers considered in each pie chart.
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Figure 6. Distribution of the eligible papers according to the sample sizes and the number of the small sample sizes that were chosen to analyze hyperspectral (right) or multispectral (left) images, where n was the number of papers considered in each pie chart.
Figure 6. Distribution of the eligible papers according to the sample sizes and the number of the small sample sizes that were chosen to analyze hyperspectral (right) or multispectral (left) images, where n was the number of papers considered in each pie chart.
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Figure 7. Distribution of the eligible papers according to the reference data sources that were chosen to analyze hyperspectral (right) or multispectral (left) images, where n was the total number of papers considered in each pie chart.
Figure 7. Distribution of the eligible papers according to the reference data sources that were chosen to analyze hyperspectral (right) or multispectral (left) images, where n was the total number of papers considered in each pie chart.
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Figure 8. Reference data available online together with hyperspectral images: (a) Jasper Ridge reference map and spectral library [535]; (b) Cuprite reference map [536]; (c) Samson reference map and spectral library [535]; (d) Indian Pines reference map [535]; (e) University of Houston reference map [535]; (f) Salinas Valley reference map [535]; (g) Urban reference map [535]; (h) Pavia University reference map [535]; (i) Washington DC reference map [535]; (j) Pavia center reference map [535].
Figure 8. Reference data available online together with hyperspectral images: (a) Jasper Ridge reference map and spectral library [535]; (b) Cuprite reference map [536]; (c) Samson reference map and spectral library [535]; (d) Indian Pines reference map [535]; (e) University of Houston reference map [535]; (f) Salinas Valley reference map [535]; (g) Urban reference map [535]; (h) Pavia University reference map [535]; (i) Washington DC reference map [535]; (j) Pavia center reference map [535].
Remotesensing 15 02822 g008aRemotesensing 15 02822 g008bRemotesensing 15 02822 g008c
Figure 9. Distribution of the eligible papers that did not specify the reference maps used, fully and partially estimated fractional abundances according to the reference data sources, where n was the total number of papers that were clustered according to the reference data sources and included in the pie charts: (a) The papers that employed the maps; (b) The papers that employed in situ data; (c) The papers that employed the images; (d) The papers that employed the previous reference maps.
Figure 9. Distribution of the eligible papers that did not specify the reference maps used, fully and partially estimated fractional abundances according to the reference data sources, where n was the total number of papers that were clustered according to the reference data sources and included in the pie charts: (a) The papers that employed the maps; (b) The papers that employed in situ data; (c) The papers that employed the images; (d) The papers that employed the previous reference maps.
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Table 1. Studies that introduced spectral unmixing procedure.
Table 1. Studies that introduced spectral unmixing procedure.
PaperPublication YearStudy AreaSpectral RangeName Given to Spectral Unmixing ProcedureCitations in Google Scholar
Adams & McCord [27]1971Lunar0.35–2.5 μm-136
Singer & McCord [28]1979Mars0.35–2.5 μm-347
Hapke [29]1981Planets -2200
Johnson et al. [12]1983Minerals0.35–2.5 μmSemi-empirical mixing model288
Smith et al. [13]1985Minerals0.60–2.20 μmSpectral mixing model454
Adams et al. [23]1986Mars0.35–2.5 μmSpectral mixture modeling1634
Adams et al. [16]1989-1.2–2.4 μmSpectral mixture analysis131
Table 2. Reviews on the spectral unmixing procedure.
Table 2. Reviews on the spectral unmixing procedure.
PaperPublication YearPublication TitleNumber of References Cited in the ReviewCitations in Google Scholar 1
Ichoku & Karneili [1] 1996A review of mixture modelling techniques for subpixel land cover estimation57281
Heinz & Chein-I-Chang [33]2001Fully Constrained Least Squares Linear Spectral
Mixture Analysis Method for Material Quantification
in Hyperspectral Imagery
391955
Keshava & Mustard [6]2002Spectral unmixing402761
Keshava [34]2003A Survey of Spectral Unmixing Algorithms3641
Martinez et al. [35]2006Endmember extraction algorithms from hyperspectral images1667
Veganzones & Grana [36] 2008Endmember Extraction Methods: A Short Review2382
Bioucas-Dias & Plaza [7]2010Hyperspectral unmixing: Geometrical, statistical, and sparse regression-based approaches9777
Parente & Plaza [37]2010Survey of geometric and statistical unmixing algorithms for hyperspectral images53124
Bioucas-Dias & Plaza [38]2011An overview on hyperspectral unmixing:
geometrical, statistical, and sparse regression based approaches
5178
Somer et al. [39]2011Endmember variability in Spectral Mixture Analysis: A review179660
Bioucas-Dias et al. [40]2012Hyperspectral Unmixing Overview: Geometrical,
Statistical, and Sparse Regression-Based Approaches
962597
Quintano et al. [41]2012Spectral unmixing: a review163141
Ismail & Bchir [42] 2014Survey on Number of Endmembers Estimation
Techniques for Hyperspectral Data Unmixing
221
Heylen et al. [8] 2014A Review of Nonlinear Hyperspectral Unmixing Methods201452
Shi & Wang [43]2014Incorporating spatial information in spectral unmixing: A review106197
Drumetz et al. [44]2016Variability of the endmembers in spectral unmixing: recent advances2634
Wang et al. [45]2016A survey of methods incorporating spatial
information in image classification and spectral unmixing
28075
Wei & Wang [5]2020An Overview on Linear Unmixing of Hyperspectral Data7417
Borsoi et al. [4]2021Spectral Variability in Hyperspectral Data Unmixing31763
1 Accessed on 31 January 2023.
Table 10. Sample sizes of the reference data that were employed by the eligible papers.
Table 10. Sample sizes of the reference data that were employed by the eligible papers.
Sample Sizes of the Reference DataPapers Published in 2022, 2021, and 2020Papers Published in 2011 and 2010Papers Published in 1996 and 1995
Whole study area 1725510
Small sample sizes78381
Representative area2170
Not specified59125
Table 11. Reference data sources employed by the eligible papers.
Table 11. Reference data sources employed by the eligible papers.
Sources of Reference DataPapers Published in 2022, 2021, and 2020Papers Published in 2011 and 2010Papers Published in 1996 and 1995
Maps1328
In situ data55352
Images106316
Previous reference maps156440
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Cavalli, R.M. Spatial Validation of Spectral Unmixing Results: A Systematic Review. Remote Sens. 2023, 15, 2822. https://doi.org/10.3390/rs15112822

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Cavalli RM. Spatial Validation of Spectral Unmixing Results: A Systematic Review. Remote Sensing. 2023; 15(11):2822. https://doi.org/10.3390/rs15112822

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Cavalli, Rosa Maria. 2023. "Spatial Validation of Spectral Unmixing Results: A Systematic Review" Remote Sensing 15, no. 11: 2822. https://doi.org/10.3390/rs15112822

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