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Remote Sensing for Crop Nutrients and Related Traits

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 7703

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Special Issue Editors

Prof. Dr. Fei Li

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Guest Editor

College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot, China
Interests: crop monitoring; hyperspectral remote sensing; spectra-based nutrient management

Prof. Dr. Kang Yu

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Guest Editor

TUM School of Life Sciences, Technical University of Munich, Munich, Germany
Interests: plant phenotyping; precision agriculture
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Special Issue Information

Dear Colleagues,

The acquisition of plant traits and nutritional information is crucial for precision crop nutrient management. Imaging spectroscopy is one of the most widely used remote sensing technologies in precision crop nutrient management, due to its advantage of estimating crops’ nutritional status and soil nutrient availability in a rapid and non-destructive manner. However, the application of imaging spectroscopy in crop nutrient monitoring remains challenging, given the fact that multiple nutrients (macro- and micronutrients), stresses, and phenological stages may have similar spectral responses confounded at different temporal and spatial scales. Therefore, it is critical to have a comprehensive understanding of the potential and limitations of imaging spectroscopy in crop monitoring so that we may further develop imaging spectroscopy in conjunction with other agro-technologies (e.g., mechanical engineering, variable rate technology, UAVs, UGVs, robotics, crop modeling, and AI) for a more precise characterization of plant traits, nutritional status, and even the causes of crop stresses.

This Special Issue aims to collect studies (both review and research articles) on the monitoring of crop nutrients and their related traits. Topics may cover a broad sense of applications of imaging spectroscopy methods and remote sensing data (e.g., multispectral, hyperspectral, thermal, fluorescence, and LiDAR), data analysis techniques (vegetation index, image analysis, radiative transfer modeling, machine learning, and deep learning) for the remote monitoring of crop nutritional status, and other issues of relevance. Articles may address, but are not limited to, the following topics:

UAV remote sensing
satellite remote sensing
crop nutrients
biomass
leaf pigments
leaf area index
crop water stress
spectra-based crop nutrient management

Prof. Dr. Fei Li
Prof. Dr. Kang Yu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

plant imaging spectroscopy
crop nutritional traits
vegetative index
machine learning
hyperspectral
multispectral
thermal
fluorescence

Published Papers (6 papers)

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Research

24 pages, 21589 KiB

Open AccessArticle

Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill)

by Renato Herrig Furlanetto, Luís Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Adilson de Oliveira Junior and Rubson Natal Ribeiro Sibaldelli

Remote Sens. 2024, 16(11), 1900; https://doi.org/10.3390/rs16111900 - 25 May 2024

Viewed by 248

Abstract

Identifying potassium (K⁺) deficiency in plants has traditionally been a difficult and expensive process. Traditional methods involve inspecting leaves for symptoms and conducting a laboratory analysis. These methods are not only time-consuming but also use toxic reagents. Additionally, the analysis is performed during the reproductive stage of growth, which does not allow enough time for corrective fertilization. Moreover, soybean growers do not have other tools to analyze the nutrition status during the earlier stages of development. Thus, this study proposes a quick approach for monitoring K⁺ in soybean crops using hyperspectral data through principal component analysis (PCA) and linear discriminant analysis (LDA) with a wavelength selection algorithm. The experiment was carried out at the Brazilian National Soybean Research Center in the 2017–2018, 2018–2019, and 2019–2020 soybean crop seasons, at the stages of development V4–V5, R1–R2, R3–R4, and R5.1–R5.3. Three treatments were evaluated that varied in K⁺ availability: severe potassium deficiency (SPD), moderate potassium deficiency (MPD), and an adequate supply of potassium (ASP). Spectral data were collected using an ASD Fieldspec 3 Jr. hyperspectral sensor. The results showed a variation in the leaf spectral signature based on the K⁺ availability, with SPD having higher reflectance in the visible region due to a lower concentration of pigments. PCA explained 100% of the variance across all stages and seasons, making it possible to distinguish SPD at an early development stage. LDA showed over 70% and 59% classification accuracies for discriminating a K⁺ deficiency in the simulation and validation stages. This study demonstrates the potential of the method as a rapid nondestructive and accurate tool for identifying K⁺ deficiency in soybean leaves. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Nutrients and Related Traits)

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32 pages, 8034 KiB

Open AccessArticle

New 3-D Fluorescence Spectral Indices for Multiple Pigment Inversions of Plant Leaves via 3-D Fluorescence Spectra

by Shoupeng Tian, Yao Zhang, Jiaoru Wang, Rongxu Zhang, Weizhi Wu, Yadong He, Xiaobin Wu, Wei Sun, Dong Li, Yixin Xiao and Fumin Wang

Remote Sens. 2024, 16(11), 1885; https://doi.org/10.3390/rs16111885 - 24 May 2024

Viewed by 268

Abstract

High-sensitivity fluorescence monitoring has been widely used in agriculture and environmental science. However, the active fluorescence detection information of leaf segments mainly focuses on total chlorophyll, and the fluorescence information of chlorophyll a, chlorophyll b, and some other pigments has not been explored. This only considers the fluorescence spectrum characteristics at a single wavelength or the fluorescence integral from a range of wavelength regions and does not completely consider the linkage relation between the excitation, emission, and interference information. In this paper, the three-dimensional fluorescence spectrum, containing the excitation and emission fluorescence spectra, and the corresponding multiple pigment characteristics from the upgraded LOPEX_ZJU database were collected. The linkages of excitation and emission of the three-dimensional fluorescence spectra of these pigments were analyzed for the newly built multiple pigment 3-D fluorescence spectral indices (3-D FSIs), including those of chlorophyll a, chlorophyll b, carotenoid, anthocyanin, and flavonoid 3-D FSIs. Then, these pigment inversion models were established and validated. The results show that the 3-D FSIs performances for the photosynthetic pigment content inversion (including chlorophyll a and b, and carotenoids) were much better than those for the photo-protective pigments (including anthocyanins and flavonoids) from the 3-D fluorescence spectra of these plant leaves. Here, the 3-D fluorescence normalization index (FNI ((F_430,690-F_430,763)/(F_430,690+F_430,763))) for the chlorophyll a inversion model has a high accuracy, the RMSE is 2.96 μg/cm², and the 3-D fluorescence reciprocal difference index (FRI (F_650,704/F_650,668) for the chlorophyll b model has an encouraging RMSE (2.01 μg/cm²). The RMSE of the 3-D fluorescence ratio index (FRI (F_500,748/F_500,717)) for the carotenoid inversion is 3.77 μg/cm²RMSE. Only FRI (F_370,615/F_370,438) was selected for the modeling and validating evaluation of the leaf Flas content inversion, but the evaluation metrics were not good, with an RMSE (151.13 μg/cm²). For Ants, although there was a 3-D FSI (FRDI (1/F_540,679-1/F_540,557)), and its evaluation metrics, with an RMSE (2.8 μg/cm²), were at or over 0.05 level, the validating evaluation metric VC (98.3577%) was not encouraging. These results showed that fluorescence, as a nondestructive and efficient detection method, could determine the contents of chlorophyll a, chlorophyll b, and carotenoid in plant leaves, providing a new method to detect plant information. It can also provide a potential chance for the fluorescence images of fine photo-protective pigments, especially chlorophyll a and b, using the special active fluorescence excitation light source and a few fluorescence imaging channels from the optimal FSIs. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Nutrients and Related Traits)

18 pages, 4911 KiB

Open AccessArticle

Mapping Plant Nitrogen Concentration and Aboveground Biomass of Potato Crops from Sentinel-2 Data Using Ensemble Learning Models

by Hang Yin, Fei Li, Haibo Yang, Yunfei Di, Yuncai Hu and Kang Yu

Remote Sens. 2024, 16(2), 349; https://doi.org/10.3390/rs16020349 - 16 Jan 2024

Viewed by 990

Abstract

Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel approach for large-scale N monitoring. In this study, we evaluated the performance of different types of spectral bands and indices (SIs) coupled with ensemble learning models (ELMs) at retrieving the plant N concentration (PNC) and plant aboveground biomass (AGB) of potato from Sentinel-2 images. Cloud-free Sentinel-2 imagery was acquired during the tuber-formation to starch-accumulation stages from 2020 to 2021. Fourteen optimal SIs were selected using the successive projections algorithm (SPA) and principal component analysis (PCA). The PNC and AGB estimation models were then built using an ELMs. The results showed that the SIs based on chlorophyll absorption bands were strongly related to potato PNC and AGB. Also, the N-correlated bands were mainly concentrated in the red-edge (705 nm) and short-wave infrared (1610 and 2190 nm) regions. The ELMs successfully predicted PNC and AGB (R²_PNC = 0.74; R²_AGB = 0.82). Compared with the other five base models (k-nearest neighbor (KNN), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR)), the ELMs provided higher PNC and AGB estimation accuracy and effectively reduced overfitting to training data. This study demonstrated that the promising solution of using SPA-PCA coupled with an ensemble learning model improves the estimation accuracy of potato PNC and AGB based on Sentinel-2 imagery data. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Nutrients and Related Traits)

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18 pages, 14931 KiB

Open AccessArticle

UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation

by Qi Wang, Xiaokai Chen, Huayi Meng, Huiling Miao, Shiyu Jiang and Qingrui Chang

Remote Sens. 2023, 15(19), 4658; https://doi.org/10.3390/rs15194658 - 22 Sep 2023

Cited by 1 | Viewed by 1073

Abstract

Chlorophyll is an important indicator for monitoring crop growth and is vital for agricultural management. Therefore, rapid and accurate estimation of chlorophyll content is important for decision support in precision agriculture to accurately monitor the SPAD (Soil and Plant Analyzer Development) values of winter wheat. This study used winter wheat to obtain canopy reflectance based on UAV hyperspectral data and to calculate different vegetation indices and red-edge parameters. The best-performing vegetation indices and red-edge parameters were selected by Pearson correlation analysis and multiple stepwise regression (MSR). SPAD values were estimated using a combination of vegetation indices, vegetation indices and red-edge parameters as model factors, two types of machine learning (ML), a support vector machine (SVM), and a backward propagation neural network (BPNN), and partial least squares regression (PLSR) for four growth stages of winter wheat, and validated using independent samples. The results show that for the same data source, the best vegetation indices or red-edge parameters for estimating SPAD values differed at different growth stages and that combining vegetation indices with red-edge parameters gave better estimates than using only vegetation indices as an input factor for estimating SPAD values. There is no significant difference between PLSR, SVM, and BPNN methods in estimating SPAD values, with better stability of the estimated models using machine learning methods. Different growth stages have a large impact on winter wheat SPAD values estimates, with the accuracy of the four growth stage models increasing in the following order: booting < heading < filling < flowering. This study shows that using a combination of vegetation indices and red-edge parameters can improve SPAD values estimates compared to using vegetation indices alone. In the future, the choice of appropriate factors and methods will need to be considered when constructing models to estimate crop SPAD values. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Nutrients and Related Traits)

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33 pages, 5891 KiB

Open AccessArticle

Investigating Foliar Macro- and Micronutrient Variation with Chlorophyll Fluorescence and Reflectance Measurements at the Leaf and Canopy Scales in Potato

by Jaakko Oivukkamäki, Jon Atherton, Shan Xu, Anu Riikonen, Chao Zhang, Teemu Hakala, Eija Honkavaara and Albert Porcar-Castell

Remote Sens. 2023, 15(10), 2498; https://doi.org/10.3390/rs15102498 - 9 May 2023

Cited by 2 | Viewed by 2179

Abstract

Vegetation indices (VIs) related to plant greenness have been studied extensively for the remote detection of foliar nitrogen content. Yet, the potential of chlorophyll fluorescence (ChlF) and photoprotection-based indices such as the photochemical reflectance index (PRI) or the chlorophyll/carotenoid index (CCI) for the detection of a wide range of nutrients remains elusive. We measured the dynamics of foliar macro- and micronutrient contents in potato plants as affected by fertilization and water stress, along with leaf and canopy level observations of spectral reflectance and ChlF (or solar-induced fluorescence). ChlF and photoprotection-related indices were more strongly related to a wide range of foliar nutrient contents compared to greenness-based indices. At the leaf level, relationships were largely mediated by foliar chlorophyll contents (Cab) and leaf morphology, which resulted in two contrasting groupings: a group dominated by macronutrients N, P, K, and Mg that decreased during canopy development and was positively correlated with Cab, and a group including Cu, Mn, Zn, and S that increased and was negatively related to Cab. At the canopy-level, spectral indices were additionally influenced by canopy structure, and so their capacity to detect foliar nutrient contents depends on the spatiotemporal covariation between foliar Cab, morphology, and canopy structure within the observations. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Nutrients and Related Traits)

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19 pages, 4213 KiB

Open AccessArticle

Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes

by Junxiang Peng, Niklas Zeiner, David Parsons, Jean-Baptiste Féret, Mats Söderström and Julien Morel

Remote Sens. 2023, 15(9), 2350; https://doi.org/10.3390/rs15092350 - 29 Apr 2023

Cited by 2 | Viewed by 2055

Abstract

Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (DMY) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season DMY using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash–Sutcliffe model efficiency (NSE) values (average ± standard deviation) for the calibration, validation and evaluation of 0.92 ± 0.01, 0.55 ± 0.22 and 0.86 ± 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance. Full article

(This article belongs to the Special Issue Remote Sensing for Crop Nutrients and Related Traits)

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