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Special Issue "Applications of Spectroscopy in Agriculture and Vegetation Research"

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 May 2019

Special Issue Editors

Guest Editor
Dr. Jochem Verrelst

Senior Scientist, Laboratory for Earth Observation, Image Processing Laboratory - Scientific Park, University of Valencia, C/ Catedrático José Beltrán, 2, 46980 Paterna, Valencia, Spain
Website | E-Mail
Phone: (+34) 963544067
Interests: imaging spectroscopy; vegetation properties retrieval; FLEX, vegetation fluorescence; optical remote sensing; radiative transfer models; retrieval methods
Guest Editor
Dr. Tobias Hank

Senior Scientist, Department of Geography, Faculty of Geosciences, LUDWIG-MAXIMILIANS-UNIVERSITÄT München, Munich 80333, Germany
Website | E-Mail
Phone: +49(0)89/2180-6682
Interests: precision agriculture; biophysical modelling; imaging spectroscopy; data assimilation
Guest Editor
Dr. Martin Schlerf

Senior Research Associate, Luxembourg Institute of Science and Technology (LIST), 5, avenue des Hauts-Fourneaux, L-4362 Esch/Alzette, Luxembourg
Website | E-Mail
Phone: +352 275 888 5004
Interests: hyperspectral and thermal remote sensing; retrieval of biochemical and structural vegetation properties; water stress detection; crop nitrogen assessment
Guest Editor
Dr. Xia Yao

Senior Scientist, College of Agriculture, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu 210095, P. R. China
Website | E-Mail
Phone: 86-25-84396565
Interests: crop SIF; phenotyping on LiDAR and UAV platforms; quantification of crop properties; disease surveillance; hyperspectral remote sensing
Guest Editor
Dr. Karolina Sakowska

Biometeorology Research Group, Institute of Ecology, University of Innsbruck, 6020 Innsbruck, Austria
Website | E-Mail
Phone: +39 377 9999 168
Interests: remote sensing of vegetation properties; ecosystem–atmosphere exchange of carbon dioxide and carbonyl sulfide (COS); fluorescence and COS as tracers for canopy photosynthesis

Special Issue Information

Dear Colleagues,

Vegetated ecosystems are an important component of terrestrial (agro)ecosystems largely mediating gas and energy exchange at the atmosphere–biosphere–pedosphere interface. The precise spatial and temporal acquisition of information about vegetation status, health, and photosynthetic functioning is fundamental to model the dynamic response of vegetation to changing environmental conditions.

This information can probably be best obtained with imaging spectrometers, which provide a unique opportunity to collect spectrally and spatially continuous data on vegetation traits at ecologically relevant scales over wide areas. With the ongoing progress of implementing imaging spectrometers in UAVs, aircrafts, and spaceborne missions, imaging spectroscopy has become a mature technique for capturing and quantifying vegetation properties and dynamics.

Apart from the steady progress in imaging spectroscopy sensor technology, also the continuing increase of computational power and the increasing availability and democratization of spectroscopic data have nurtured the recent boost in imaging spectroscopy science, leading to a methodological expansion and a diversification in vegetation-related applications. With imaging spectroscopy science now reaching maturity, it becomes feasible to compile recent applications in a dedicated Special Issue.

This Special Issue aims to address fundamental and applied research relating the spectral properties of vegetation to agronomic and biophysical variables, genetic and phenotypic parameters, as well as diurnal and seasonal dynamics linked to light harvesting and photoprotection.

Among the ongoing initiatives in this field, the CA17134 (SENSECO) COST Action, which aims to bring together scientists working on spectroscopy in agriculture and vegetation research, is a recent and prominent one. SENSECO places emphasis on investigating the role of spatial and temporal scales, as well as on promoting the synergistic use of multiple sensors at multiple scales and streamlining data quality and traceability. These kinds of initiatives intend to unite scientists to unravel the links between plant physiology, agroecosystem functioning, and biogeochemical cycles and will strengthen the progress in terrestrial imaging spectroscopy missions dedicated to vegetation monitoring (e.g., FLEX, EnMAP).

To gather a consolidated overview about the different activities around spectroscopy applications in agriculture and vegetation research within the COST Action and in connection with dedicated campaigns and satellite mission preparation programs, we invite papers on the following non-exhaustive list of topics:

  • Progress made in spectroscopy studies: considering, e.g., reflectance and fluorescence data acquisition protocols and point clouds, state of the art and performance of instrumentation, retrieval methods, and modelling applications.
  • Quantification of agronomic biophysical and biochemical variables: proximal sensing, UAV platforms for precision farming applications such as phenotyping, assessment of crop damage, identification of diseases, pests, water and nutrient management, growth status (LAI, biomass), photosynthetic activity, grain quality, crop acreage, and productivity.
  • Multi-scale observations in the spatial and temporal domains to quantify and monitor leaf-to-ecosystem functioning and biogeochemical traits and cycles. Synergistic use of multiple sensors to bridge the scaling gap.
  • Methodological progress in retrieval methods in the domains of machine learning, physical model inversion, hybrid methods, and from experimental-towards-operational methods and coupling of models and imaging spectroscopy.
  • Quantification of dynamic vegetation functioning (stress, productivity) from hyperspectral reflectance, fluorescence, and point-data sources.

Dr. Jochem Verrelst
Dr. Tobias Hank
Dr. Martin Schlerf
Dr. Xia Yao
Dr. Karolina Sakowska
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 papers will be 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 1800 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

  • spectroscopy
  • reflectance
  • fluorescence
  • vegetation
  • agriculture
  • scaling
  • sensor synergy
  • biophysical variables
  • quality
  • productivity

Published Papers (8 papers)

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Research

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Open AccessArticle SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions
Remote Sens. 2019, 11(8), 967; https://doi.org/10.3390/rs11080967 (registering DOI)
Received: 6 March 2019 / Revised: 12 April 2019 / Accepted: 18 April 2019 / Published: 23 April 2019
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Abstract
The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. [...] Read more.
The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat
Remote Sens. 2019, 11(8), 920; https://doi.org/10.3390/rs11080920
Received: 27 February 2019 / Revised: 2 April 2019 / Accepted: 9 April 2019 / Published: 16 April 2019
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Abstract
Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation [...] Read more.
Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation structural and functional dynamics. Traditional methods of direct tissue analysis or the use of handheld meters, are not able to capture chlorophyll variability at anything beyond point scales, so are not particularly useful for informing decisions on plant health and status at the field scale. Examining the spectral response of plants via remote sensing has shown much promise as a means to capture variations in vegetation properties, while offering a non-destructive and scalable approach to monitoring. However, determining the optimum combination of spectra or spectral indices to inform plant response remains an active area of investigation. Here, we explore the use of a machine learning approach to enhance the estimation of leaf chlorophyll (Chlt), defined as the sum of chlorophyll a and b, from spectral reflectance data. Using an ASD FieldSpec 4 Hi-Res spectroradiometer, 2700 individual leaf hyperspectral reflectance measurements were acquired from wheat plants grown across a gradient of soil salinity and nutrient levels in a greenhouse experiment. The extractable Chlt was determined from laboratory analysis of 270 collocated samples, each composed of three leaf discs. A random forest regression algorithm was trained against these data, with input predictors based upon (1) reflectance values from 2102 bands across the 400–2500 nm spectral range; and (2) 45 established vegetation indices. As a benchmark, a standard univariate regression analysis was performed to model the relationship between measured Chlt and the selected vegetation indices. Results show that the root mean square error (RMSE) was significantly reduced when using the machine learning approach compared to standard linear regression. When exploiting the entire spectral range of individual bands as input variables, the random forest estimated Chlt with an RMSE of 5.49 µg·cm−2 and an R2 of 0.89. Model accuracy was improved when using vegetation indices as input variables, producing an RMSE ranging from 3.62 to 3.91 µg·cm−2, depending on the particular combination of indices selected. In further analysis, input predictors were ranked according to their importance level, and a step-wise reduction in the number of input features (from 45 down to 7) was performed. Implementing this resulted in no significant effect on the RMSE, and showed that much the same prediction accuracy could be obtained by a smaller subset of indices. Importantly, the random forest regression approach identified many important variables that were not good predictors according to their linear regression statistics. Overall, the research illustrates the promise in using established vegetation indices as input variables in a machine learning approach for the enhanced estimation of Chlt from hyperspectral data. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle Using Hyperspectral Crop Residue Angle Index to Estimate Maize and Winter-Wheat Residue Cover: A Laboratory Study
Remote Sens. 2019, 11(7), 807; https://doi.org/10.3390/rs11070807
Received: 6 March 2019 / Revised: 1 April 2019 / Accepted: 2 April 2019 / Published: 3 April 2019
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Abstract
Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into [...] Read more.
Crop residue left in the field after harvest helps to protect against water and wind erosion, increase soil organic matter, and improve soil quality, so a proper estimate of the quantity of crop residue is crucial to optimize tillage and for research into environmental effects. Although remote-sensing-based techniques to estimate crop residue cover (CRC) have proven to be good tools for determining CRC, their application is limited by variations in the moisture of crop residue and soil. In this study, we propose a crop residue angle index (CRAI) to estimate the CRC for four distinct soils with varying soil moisture (SM) content and crop residue moisture (CRM). The current study uses laboratory-based tests ((i) a dry dataset (air-dried soils and crop residues, n = 392); (ii) a wet dataset (wet soils and crop residues, n = 822); (iii) a saturated dataset (saturated soils and crop residues, n = 402); and (iv) all datasets (n = 1616)), which allows us to analysis the soil and crop residue hyperspectral response to varying SM/CRM. The CRAI combines two features that reflect the moisture content in soil and crop residue. The first is the different reflectance of soil and crop residue as a function of moisture in the near-infrared band (833 nm) and short-wave near-infrared band (1670 nm), and the second is different reflectance of soils and crop residues to lignin, cellulose, and moisture in the bands at 2101, 2031, and 2201 nm. The effects of moisture and soil type on the proposed CRAI and selected traditional spectral indices ((i) hyperspectral cellulose absorption index; (ii) hyperspectral shortwave infrared normalized difference residue index; and (iii) selected broad-band spectral indices) were compared by using a laboratory-based dataset. The results show that the SM/CRM significantly affects the broad-band spectral indices and all other spectral indices investigated are less correlated with CRC when using all datasets than when using only the dry, wet, or saturated dataset. Laboratory study suggests that the CRAI is promising for estimating CRC with the four soils and with varying SM/CRM. However, because the CRAI was only validated by a laboratory-based dataset, additional field testing is thus required to verify the use of satellite hyperspectral remote-sensing images for different crops and ecological areas. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar
Remote Sens. 2019, 11(5), 506; https://doi.org/10.3390/rs11050506
Received: 1 February 2019 / Revised: 22 February 2019 / Accepted: 22 February 2019 / Published: 2 March 2019
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Abstract
As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for [...] Read more.
As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands
Remote Sens. 2019, 11(4), 458; https://doi.org/10.3390/rs11040458
Received: 5 February 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
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Abstract
Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies [...] Read more.
Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (β diversity) and species diversity at aggregate spatial scales (γ diversity). Shannon indices of γ and β diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59–85% of variance in γ diversity and 68–79% of variance in β diversity using spatial heterogeneity in canopy optical properties. Variation in both γ and β diversity were associated most strongly with heterogeneity in reflectance in blue (350–370 nm), red (660–770 nm), and near infrared (810–1050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, β diversity was greater, but γ diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both γ and β diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle Soil Moisture Retrieval Model for Remote Sensing Using Reflected Hyperspectral Information
Remote Sens. 2019, 11(3), 366; https://doi.org/10.3390/rs11030366
Received: 26 December 2018 / Revised: 31 January 2019 / Accepted: 7 February 2019 / Published: 12 February 2019
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Abstract
The variation and the spatial–temporal distribution of soil water content have significant effects on heat balance, agricultural moisture, etc. A soil moisture (SM) retrieval model can provide a theoretical basis for realizing a rapid test and revealing the spatial–temporal variation of the surface [...] Read more.
The variation and the spatial–temporal distribution of soil water content have significant effects on heat balance, agricultural moisture, etc. A soil moisture (SM) retrieval model can provide a theoretical basis for realizing a rapid test and revealing the spatial–temporal variation of the surface water. However, remote sensors do not measure soil water content directly. Therefore, it is of great importance to establish a SM retrieval model. In this paper, the relationship between SM and diffuse reflectance was first derived using the absorption coefficient and scattering coefficient related to SM. Then, based on Kubelka–Munk (KM) theory, the SM retrieval model using reflectance information was further derived, which is a semi-empirical model with an unknown parameter obtained either from fitting or from experimental measurements. The validity and reliability of the model were confirmed with the validation set. The results showed that the root mean square errors of prediction (RMSEPs) of four soils were generally less than 0.017, while the coefficients of determination (R2s) of four soils were generally more than 0.85, and the ratios of the performance to deviation (RPDs) of four soils were greater than 2.5 (470–2400 nm). Therefore, the model has high prediction accuracy, and can be well applied to the prediction of water content in different sorts of soils. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessArticle Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance
Remote Sens. 2019, 11(2), 197; https://doi.org/10.3390/rs11020197
Received: 18 December 2018 / Revised: 12 January 2019 / Accepted: 17 January 2019 / Published: 20 January 2019
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Abstract
Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding [...] Read more.
Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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Open AccessLetter Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model
Remote Sens. 2018, 10(12), 2063; https://doi.org/10.3390/rs10122063
Received: 19 September 2018 / Revised: 13 December 2018 / Accepted: 17 December 2018 / Published: 19 December 2018
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Abstract
Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of [...] Read more.
Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of all, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the coupled PROSPECT-D and SAIL radiative transfer models (PROSAIL) were employed to emulate the setup of future hyperspectral sensors in the visible and near-infrared (VNIR) spectral regions with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with the highest mean absolute error (MAE) between model simulation and spectral measurement. The largest mismatch could be found in the green visible and red edge regions, which can be explained by complex interactions of several biochemical and structural variables in these spectral domains. For leaf area index (LAI, m2·m−2) retrieval, results indicated only a small improvement when using optimized spectral samplings. However, a significant increase in accuracy for leaf chlorophyll content (LCC, µg·cm−2) estimations could be obtained, with the relative root mean square error (RMSE) decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE of ~0.01) to stabilize the retrieval of crop biochemical variables. Full article
(This article belongs to the Special Issue Applications of Spectroscopy in Agriculture and Vegetation Research)
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