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Remote Sensing for Surface Biophysical Parameter Retrieval

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 23328

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Special Issue Editors

Dr. Shengbiao Wu

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Guest Editor

Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
Interests: vegetation remote sensing; radiative transfer; vegetation phenology; urban greenspace exposure; urban heat exposure
Special Issues, Collections and Topics in MDPI journals

Dr. Baodong Xu

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Guest Editor

College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
Interests: biophysical parameter retrieval; agricultural remote sensing; product validation; crop type mapping; vegetation dynamics

Prof. Dr. Gaofei Yin

E-Mail Website
Guest Editor

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China
2. CREAF, 08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
3. CSIC, Global Ecology Unit, CREAF-CSIC-UAB, 08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Interests: vegetation remote sensing; radiative transfer; biophysical parameter retrieval; land surface phenology; photochemical reflectance index
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jean-Philippe Gastellu-Etchegorry

E-Mail Website
Guest Editor

CESBIO, Toulouse University, CNES, CNRS, IRD, UT3, Toulouse, France
Interests: 3D radiative transfer modeling; vegetation; hyperspectral; LiDAR; fluorescence; radiative budget
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Surface biophysical parameters across leave (e.g., leaf chlorophyll nitrogen and water contents, leaf mass per area, and leaf inclination angle), canopy (e.g., canopy leaf area index, height and biomass, and tree crown area, and diameter at breast height), and landscape (e.g., surface albedo, temperature, and radiative budget) scales are crucial for modeling terrestrial processes, monitoring agricultural ecosystems, and quantifying many human–environment interactions. Remote sensing has become the mainstream technology for retrieving and mapping large-scale and long-term biophysical surface parameters. Their successful retrievals using remote sensing rely on accurate physical models, robust retrieval approaches, high-quality data observations, and reliable validation strategies. Therefore, advances are needed to better monitor the biophysical surface parameters, and to also improve our understanding and modeling of terrestrial ecosystems processes and human–nature relationships.

This Special Issue aims to report on the state-of-the-art in the monitoring of biophysical surface parameters with remote sensing. Related articles are welcome, including, but not limited to, the following topics:

Physical models (e.g., 1-D and 3-D) across multi-scales (e.g., leaf, canopy, individual tree-crown, ecosystem, landscape, and rugged terrain) for modeling remote sensing signals integrated with surface biophysical parameters.
Experimental sources of inaccuracy when retrieving surface biophysical parameters from remote sensing observations: atmosphere conditions, geometric configuration of observation, directional and neighborhood effects, 3-D architecture of the studied landscape, etc.
Retrieval approaches (e.g., empirically based, physically based, and machine learning and deep learning approaches) across multi-scale observation platforms (e.g., smartphone, wireless sensor network (WSN), tower-based cameras, unmanned aerial vehicle (UAV), airborne, and satellite) for retrieving surface biophysical parameters.
New proposed or improved operational algorithms for recent satellite missions (e.g., Himawari-8, DSCOVR EPIC, Landsat-8, Sentinel-2, Worldview, PlanetScope, and PRISMA) to large-scale map surface biophysical parameters.
Rapid estimation approaches using the cloud-computing platforms (e.g., Google Earth Engine (GEE), Amazon Web Services (AWS), and Microsoft Azure).
Multi-source data fusion techniques (e.g., optical and LIDAR) for improving satellite data quality by minimizing artifact effects (e.g., clouds, topography, reflectance anisotropy, and satellite orbit drift effects), filling data gaps, and reconstructing time-series observations.
Calibration and validation strategies for assessing the accuracy and uncertainty of remote sensing biophysical parameter products.
Long-time spatial and temporal analysis of surface biophysical products, with the underlying drivers and implications for terrestrial ecosystems process and human–nature interactions.

Original research and review articles are welcome. Review articles are suggested to cover one or more of the above topics.

Dr. Shengbiao Wu
Dr. Baodong Xu
Prof. Dr. Gaofei Yin
Prof. Dr. Jean-Philippe Gastellu-Etchegorry
Guest Editors

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Keywords

radiative transfer modeling
biophysical parameter estimation
high-resolution satellite
cloud-computing technique
machine learning
multi-source data fusion/integration

Published Papers (11 papers)

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21 pages, 6691 KiB

Open AccessArticle

Evaluation of the SAIL Radiative Transfer Model for Simulating Canopy Reflectance of Row Crop Canopies

by Dalei Han, Jing Liu, Runfei Zhang, Zhigang Liu, Tingrui Guo, Hao Jiang, Jin Wang, Huarong Zhao, Sanxue Ren and Peiqi Yang

Remote Sens. 2023, 15(23), 5433; https://doi.org/10.3390/rs15235433 - 21 Nov 2023

Cited by 1 | Viewed by 1243

Abstract

The widely used SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer model (RTM) is designed for canopies that can be considered as homogeneous turbid media and thus should be inadequate for row canopies. However, numerous studies have employed the SAIL model for row crops (e.g., wheat and maize) to simulate canopy reflectance or retrieve vegetation properties with satisfactory accuracy. One crucial reason may be that under certain conditions, a row crop canopy can be considered as a turbid medium, fulfilling the assumption of the SAIL model. Yet, a comprehensive analysis about the performance of SAIL in row canopies under various conditions is currently absent. In this study, we employed field datasets of wheat canopies and synthetic datasets of wheat and maize canopies to explore the impacts of the vegetation cover fraction (fCover), solar angle and soil background on the performance of SAIL in row crops. In the numerical experiments, the LESS 3D RTM was used as a reference to evaluate the performance of SAIL for various scenarios. The results show that the fCover is the most significant factor, and the row canopy with a high fCover has a low soil background influence. For a non-black soil background, both the field measurement and simulation datasets showed that the SAIL model accuracy initially decreased, and then increased with an increasing fCover, with the most significant errors occurring when the fCover was between about 0.4 and 0.7. As for the solar angles, the accuracy of synthetic wheat canopy will be higher with a larger SZA (solar zenith angle), but that of a synthetic maize canopy is little affected by the SZA. The accuracy of the SAA (solar azimuth angle) in an across-row direction is always higher than that in an along-row direction. Additionally, when the SZA ranges from 65° to 75° and the fCover of wheat canopies are greater than 0.6, SAIL can simulate the canopy reflectance with satisfactory accuracy (rRMSE < 10%); the same accuracy can be achieved in maize canopies as long as the fCover is greater than 0.8. These findings provide insight into the applicability of SAIL in row crops and support the use of SAIL in row canopies under certain conditions (with rRMSE < 10%). Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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23 pages, 17397 KiB

Open AccessArticle

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

by Farzane Mohseni, Meisam Amani, Pegah Mohammadpour, Mohammad Kakooei, Shuanggen Jin and Armin Moghimi

Remote Sens. 2023, 15(14), 3495; https://doi.org/10.3390/rs15143495 - 11 Jul 2023

Cited by 4 | Viewed by 2329

Abstract

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95). Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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18 pages, 7845 KiB

Open AccessArticle

Comparison of Satellite Platform for Mapping the Distribution of Mauritius Thorn (Caesalpinia decapetala) and River Red Gum (Eucalyptus camaldulensis) in the Vhembe Biosphere Reserve

by Farai Dondofema, Nthaduleni Nethengwe, Peter Taylor and Abel Ramoelo

Remote Sens. 2023, 15(11), 2753; https://doi.org/10.3390/rs15112753 - 25 May 2023

Cited by 1 | Viewed by 1651

Abstract

Mapping and tracking invasive alien plant species (IAPS) and their invasiveness can be achieved using remote sensing (RS) and geographic information systems (GIS). Continuous monitoring using RS, GIS and modelling are fundamental tools for informing invasion and management strategies. Using systematic comparisons, we look at three remote sensing imagery platforms and how accurately they can be classified within the Vhembe biosphere reserve, Limpopo Province, South Africa. Supervised classification of National Geospatial Information Colour Digital Aerial Imagery, DigitalGlobe Worldview 2 and CNES SPOT 6 was performed. The Spectral Angle Mapper (SAM) algorithm was used to identify the best satellite for species-level classification. The accuracy of the classifications produced an overall accuracy (OA) of 71% with a Kappa coefficient (KC) of 0.76 for CDA photographs, an OA of 81% and a KC of 0.80 for Worldview 2, and an OA of 89% with a KC of 0.86 for SPOT 6 imagery. Therefore, SPOT 6 imagery came out as the most suitable for species-level classification. The classification results from the SPOT 6 imagery were used as input data for further species distribution modelling of Mauritius Thorn and River Red Gum in the VBR. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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19 pages, 4507 KiB

Open AccessArticle

Improving LandTrendr Forest Disturbance Mapping in China Using Multi-Season Observations and Multispectral Indices

by Dean Qiu, Yunjian Liang, Rong Shang and Jing M. Chen

Remote Sens. 2023, 15(9), 2381; https://doi.org/10.3390/rs15092381 - 1 May 2023

Cited by 2 | Viewed by 2785

Abstract

Forest disturbance detection is of great significance for understanding forest dynamics. The Landsat-based detection of the Trends in Disturbance and Recovery (LandTrendr) algorithm is widely used for forest disturbance mapping. However, there are still two limitations in LandTrendr: first, it only used for summer-composited observations, which may delay the detection of forest disturbances that occurred in autumn and winter by one year, and second, it detected all disturbance types simultaneously using a single spectral index, which may reduce the mapping accuracy for certain forest disturbance types. Here, we modified LandTrendr (mLandTrendr) for forest disturbance mapping in China by using multi-season observations and multispectral indices. Validations using the randomly selected 1957 reference forest disturbance samples across China showed that the overall accuracy (F1 score) of forest disturbance detection in China was improved by 21% with these two modifications. The mLandTrendr can quickly and accurately detect forest disturbance and can be extended to national and global forest disturbance mapping for various forest types. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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18 pages, 7498 KiB

Open AccessArticle

Negative Air Ion (NAI) Dynamics over Zhejiang Province, China, Based on Multivariate Remote Sensing Products

by Sichen Tao, Zongchen Sun, Xingwen Lin, Zhenzhen Zhang, Chaofan Wu, Zhaoyang Zhang, Benzhi Zhou, Zhen Zhao, Chenchen Cao, Xinyu Guan, Qianjin Zhuang, Qingqing Wen and Yuling Xu

Remote Sens. 2023, 15(3), 738; https://doi.org/10.3390/rs15030738 - 27 Jan 2023

Cited by 3 | Viewed by 2664

Abstract

Negative air ions (NAIs), which are known as the “air vitamin”, have been widely used as a measure of air cleanness. Field observation provides an alternative way to record site-level NAIs. However, these observations fail to capture the regional distribution of NAIs due to the limited number of sites. In this study, satellite-based bio-geophysical parameters from the climate, topography, air quality, vegetation, and anthropogenic intensity were used to estimate the daily NAIs with the Random Forest model (RF). In situ NAI observations over Zhejiang Province, China were incorporated into the model. Daily NAIs were averaged to capture the spatio-temporal distribution. The results showed that (1) the RF algorithm performed better than traditional regression analysis and the common BP neural network to generate regional NAIs at a spatial scale of 500 m over the larger scale, with an RMSE of 258.62, R² of 0.878 for model training, and R² of 0.732 for model testing; (2) in the variable importance measures (VIM) analysis, 87.96% of the NAI variance was caused by the elevation, aspect, slope, surface temperature, solar-induced chlorophyll fluorescence (SIF), relative humidity (RH), and the concentration of carbon monoxide (CO), while path analysis indicated that SIF was one of the most important factors affecting NAI concentration across the whole region; (3) NAI concentrations in 87.16% of the region were classified above grade III (>500 ions cm⁻³), which was able to meet the needs of human health maintenance; (4) the highest NAI concentration was distributed over the southwest of the Zhejiang Province, where forest land dominates. The lowest NAI concentration was mostly found in the northeast regions, where urban areas are well-developed; and (5) among different land types, the NAI concentrations were ranked as forest land > water bodies > barren > grassland > croplands > urban and built-up. Among different seasons, summer and winter have the highest and lowest NAIs, respectively. Our study provided a substantial reference for ecosystem services assessment in Zhejiang Province. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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19 pages, 7652 KiB

Open AccessArticle

Assessing the Accuracy of Landsat Vegetation Fractional Cover for Monitoring Australian Drylands

by Andres Sutton, Adrian Fisher and Graciela Metternicht

Remote Sens. 2022, 14(24), 6322; https://doi.org/10.3390/rs14246322 - 13 Dec 2022

Cited by 7 | Viewed by 2175

Abstract

Satellite-derived vegetation fractional cover (VFC) has shown to be a promising tool for dryland ecosystem monitoring. This model, calibrated through biophysical field measurements, depicts the sub-pixel proportion of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil (BS). The distinction between NPV and BS makes it particularly important for drylands, as these fractions often dominate. Two Landsat VFC products are available for the Australian continent: the original Joint Remote Sensing Research Program (JRSRP) product, and a newer Digital Earth Australia (DEA) product. Although similar validation statistics have been presented for each, an evaluation of their differences has not been undertaken. Moreover, spatial variability of VFC accuracy within drylands has not been comprehensively assessed. Here, a large field dataset (4207 sites) was employed to compare Landsat VFC accuracy across the Australian continent, with detailed spatial and temporal analysis conducted on four regions of interest. Furthermore, spatiotemporal features of VFC unmixing error (UE) were explored to characterize model uncertainty in large areas yet to be field sampled. Our results showed that the JRSRP and DEA VFC were very similar (RMSE = 4.00–6.59) and can be employed interchangeably. Drylands did not show a substantial difference in accuracy compared to the continental assessment; however contrasting variations were observed in dryland subtypes (e.g., semi-arid and arid zones). Moreover, VFC effectively tracked total ground cover change over time. UE increased with tree cover and height, indicating that model uncertainty was low in typical dryland landscapes. Together, these results provide guiding points to understanding the Australian ecosystems where VFC can be used with confidence. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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17 pages, 5845 KiB

Open AccessArticle

Estimation of Vegetation Leaf-Area-Index Dynamics from Multiple Satellite Products through Deep-Learning Method

by Tian Liu, Huaan Jin, Ainong Li, Hongliang Fang, Dandan Wei, Xinyao Xie and Xi Nan

Remote Sens. 2022, 14(19), 4733; https://doi.org/10.3390/rs14194733 - 22 Sep 2022

Cited by 3 | Viewed by 1851

Abstract

A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show spatio-temporal discontinuities and are sometimes inconsistent with vegetation growth patterns. A deep-learning model was proposed to retrieve time-series LAIs from multiple satellite data in this paper. The fusion of three global LAI products (i.e., VIIRS, GLASS, and MODIS LAI) was first carried out through a double logistic function (DLF). Then, the DLF LAI, together with MODIS reflectance (MOD09A1) data, served as the training samples of the deep-learning long short-term memory (LSTM) model for the sequential LAI estimations. In addition, the LSTM models trained by a single LAI product were considered as indirect references for the further evaluation of our proposed approach. The validation results showed that our proposed LSTM_fusion LAI provided the best performance (R² = 0.83, RMSE = 0.82) when compared to LSTM_GLASS (R² = 0.79, RMSE = 0.93), LSTM_MODIS (R² = 0.78, RMSE = 1.25), LSTM_VIIRS (R² = 0.70, RMSE = 0.94), GLASS (R² = 0.68, RMSE = 1.05), MODIS (R² = 0.26, RMSE = 1.75), VIIRS (R² = 0.44, RMSE = 1.37) and DLF LAI (R² = 0.67, RMSE = 0.98). A temporal comparison among LSTM_fusion and three LAI products demonstrated that the LSTM_fusion model efficiently generated a time-series LAI that was smoother and more continuous than the VIIRS and MODIS LAIs. At the crop peak growth stage, the LSTM_fusion LAI values were closer to the reference maps than the GLASS LAI. Furthermore, our proposed method was proved to be effective and robust in maintaining the spatio-temporal continuity of the LAI when noisy reflectance data were used as the LSTM input. These findings highlighted that the DLF method helped to enhance the quality of the original satellite products, and the LSTM model trained by the coupled satellite products can provide reliable and robust estimations of the time-series LAI. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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24 pages, 49981 KiB

Open AccessArticle

Reflectance Anisotropy from MODIS for Albedo Retrieval from a Single Directional Reflectance

by Hu Zhang, Mengzhuo Zhao, Ziti Jiao, Yi Lian, Lei Chen, Lei Cui, Xiaoning Zhang, Yan Liu, Yadong Dong, Da Qian, Yiting Wang, Juan Li and Tiejun Cui

Remote Sens. 2022, 14(15), 3627; https://doi.org/10.3390/rs14153627 - 29 Jul 2022

Cited by 4 | Viewed by 1909

Abstract

Surface reflectance anisotropy and insufficient multi-angular observations are the main challenges in albedo estimation from satellite observations. Numerous studies have been developed for albedo retrieval from a single directional reflectance by associating the anisotropy information extracted from coarse-resolution bidirectional-reflectance distribution function (BRDF) data. The contribution of land-cover type (LCT) and the Normalized Difference Vegetation Index (NDVI) in distinguishing reflectance anisotropy in these methods remains controversial. This study first proposed an approach to extracting a priori BRDF (F) from the MODIS BRDF/albedo product by considering the distribution characteristics of the model parameters. LCT- and NDVI-based F were also extracted from the corresponding subset. Then, the F-based albedo was derived from simulated or satellite directional reflectance and the anisotropic information of F. Finally, the directional reflectance and F-based albedo were compared with the MODIS albedo or ground measurement, in order to show the ability of F to compensate for the effect of reflectance anisotropy in the albedo retrieval process. The method was fully validated by the global and time-series MODIS BRDF data. The results showed that reflectance anisotropy has an aggregated distribution pattern, and F can represent the reflectance anisotropy of most pixels within a tile. The improvement of LCT and NDVI only occurs when the tile contains a large area of vegetated and barren ground. With the exception of the hotspot and large viewing-zenith-angle area in the forward hemisphere, the F-based shortwave albedo has high consistency with the MODIS albedo product. A comparison with the ground measurements and MODIS albedo showed that the F-based albedo from a single directional reflectance generally achieves an absolute accuracy requirement, with a root-mean-square error (RMSE) of 0.027 and 0.036. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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24 pages, 9569 KiB

Open AccessArticle

Comparison between Physical and Empirical Methods for Simulating Surface Brightness Temperature Time Series

by Zunjian Bian, Yifan Lu, Yongming Du, Wei Zhao, Biao Cao, Tian Hu, Ruibo Li, Hua Li, Qing Xiao and Qinhuo Liu

Remote Sens. 2022, 14(14), 3385; https://doi.org/10.3390/rs14143385 - 14 Jul 2022

Cited by 1 | Viewed by 1679

Abstract

Land surface temperature (LST) is a vital parameter in the surface energy budget and water cycle. One of the most important foundations for LST studies is a theory to understand how to model LST with various influencing factors, such as canopy structure, solar radiation, and atmospheric conditions. Both physical-based and empirical methods have been widely applied. However, few studies have compared these two categories of methods. In this paper, a physical-based method, soil canopy observation of photochemistry and energy fluxes (SCOPE), and two empirical methods, random forest (RF) and long short-term memory (LSTM), were selected as representatives for comparison. Based on a series of measurements from meteorological stations in the Heihe River Basin, these methods were evaluated in different dimensions, i.e., the difference within the same surface type, between different years, and between different climate types. The comparison results indicate a relatively stable performance of SCOPE with a root mean square error (RMSE) of approximately 2.0 K regardless of surface types and years but requires many inputs and a high computational cost. The empirical methods performed relatively well in dealing with cases either within the same surface type or changes in temporal scales individually, with an RMSE of approximately 1.50 K, yet became less compatible in regard to different climate types. Although the overall accuracy is not as stable as that of the physical method, it has the advantages of fast calculation speed and little consideration of the internal structure of the model. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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26 pages, 8085 KiB

Open AccessArticle

Impact of BRDF Spatiotemporal Smoothing on Land Surface Albedo Estimation

by Jian Yang, Yanmin Shuai, Junbo Duan, Donghui Xie, Qingling Zhang and Ruishan Zhao

Remote Sens. 2022, 14(9), 2001; https://doi.org/10.3390/rs14092001 - 21 Apr 2022

Cited by 3 | Viewed by 1762

Abstract

Surface albedo, as a key parameter determining the partition of solar radiation at the Earth’s surface, has been developed into a satellite-based product from various Earth observation systems to serve numerous global or regional applications. Studies point out that apparent uncertainty can be introduced into albedo retrieval without consideration of surface anisotropy, which is a challenge to albedo estimation especially from observations with fewer angular samplings. Researchers have begun to introduce smoothed anisotropy prior knowledge into albedo estimation to improve the inversion efficiency, or for the scenario of observations with signal or poor angular sampling. Thus, it is necessary to further understand the potential influence of smoothed anisotropy features adopted in albedo estimation. We investigated the albedo variation induced by BRDF smoothing at both temporal and spatial scales over six typical landscapes in North America using MODIS standard anisotropy products with high quality BRDF inversed from multi-angle observations in 500 m and 5.6 km spatial resolutions. Components of selected typical landscapes were assessed with the confidence of the MCD12 land cover product and 30 m CDL (cropland data layer) classification maps followed by an evaluation of spatial heterogeneity in 30 m scale through the semi-variogram model. High quality BRDF of MODIS standard anisotropy products were smoothed in multi-temporal scales of 8 days, 16 days, and 32 days, and in multi-spatial scales from 500 m to 5.6 km. The induced relative and absolute albedo differences were estimated using the RossThick-LiSparseR model and BRDFs smoothed before and after spatiotemporal smoothing. Our results show that albedo estimated using BRDFs smoothed temporally from daily to monthly over each scenario exhibits relative differences of 11.3%, 12.5%, and 27.2% and detectable absolute differences of 0.025, 0.012, and 0.013, respectively, in MODIS near-infrared (0.7–5.0 µm), short-wave (0.3–5.0 µm), and visible (0.3–0.7 µm) broad bands. When BRDFs of investigated landscapes are smoothed from 500 m to 5.6 km, variations of estimated albedo can achieve up to 36.5%, 37.1%, and 94.7% on relative difference and absolute difference of 0.037, 0.024, and 0.018, respectively, in near-infrared (0.7–5.0 µm), short wave (0.3–5.0 µm), and visible (0.3–0.7 µm) broad bands. In addition, albedo differences caused by temporal smoothing show apparent seasonal characteristic that the differences are significantly higher in spring and summer than those in autumn and winter, while albedo differences induced by spatial smoothing exhibit a noticeable relationship with sill values of a fitted semi-variogram marked by a correlation coefficient of 0.8876. Both relative and absolute albedo differences induced by BRDF smoothing are demonstrated to be captured, thus, it is necessary to avoid the smoothing process in quantitative remote sensing communities, especially when immediate anisotropy retrievals are available at the required spatiotemporal scale. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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15 pages, 6311 KiB

Open AccessTechnical Note

LACC2.0: Improving the LACC Algorithm for Reconstructing Satellite-Derived Time Series of Vegetation Biochemical Parameters

by Mingzhu Xu, Rong Shang, Jing M. Chen and Lingfang Zeng

Remote Sens. 2023, 15(13), 3277; https://doi.org/10.3390/rs15133277 - 26 Jun 2023

Viewed by 1622

Abstract

The locally adjusted cubic-spline capping (LACC) algorithm is well recognized for its effectiveness in the global time series reconstruction of vegetation biophysical and biochemical parameters. However, in its application, we often encounter issues, such as identifying positively biased outliers for vegetation biochemical parameters and reducing the influence of long consecutive gaps. In this study, we improved the LACC algorithm to address the above two issues by (1) incorporating a procedure to remove outliers and (2) integrating the spatial information of neighboring pixels for large data gap filling. To evaluate the performance of the new version of LACC (namely LACC2.0), leaf chlorophyll content (LCC) was taken as an example. A reference LCC curve was generated for each pixel of the global map as the true value for global evaluation, and a time series of LCC with real gaps in the original data for each pixel was created by adding Gaussian noises into observations for testing the effectiveness of time series reconstruction algorithms. Results showed that the percentage of pixels with an RMSE smaller than 5 μg/cm² was improved from 81.2% in LACC to 96.4% in LACC2.0, demonstrating that LACC2.0 had the potential to provide a better reconstruction of global daily satellite-derived vegetation biochemical parameters. This finding highlights the significance of outlier removal and spatial-temporal fusion to enhance the accuracy and reliability of time series reconstruction. Full article

(This article belongs to the Special Issue Remote Sensing for Surface Biophysical Parameter Retrieval)

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