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"https://www.academia.edu/login?post_login_redirect_url=https%3A%2F%2Fwww.academia.edu%2F120463937%2FMapping_Productivity_and_Essential_Biophysical_Parameters_of_Cultivated_Tropical_Grasslands_from_Sentinel_2_Imagery%3Fshow_translation%3Dtrue"; window.loswp.previewableAttachments = [{"id":115604633,"identifier":"Attachment_115604633","shouldShowBulkDownload":false}]; window.loswp.shouldDetectTimezone = true; window.loswp.shouldShowBulkDownload = true; window.loswp.showSignupCaptcha = false window.loswp.willEdgeCache = false; window.loswp.work = {"work":{"id":120463937,"created_at":"2024-06-03T04:18:07.768-07:00","from_world_paper_id":255552293,"updated_at":"2025-02-01T03:42:43.186-08:00","_data":{"publisher":"Multidisciplinary Digital Publishing Institute","ai_title_tag":"Estimating Grassland Productivity via Sentinel-2","grobid_abstract":"Nitrogen (N) is the main nutrient element that maintains productivity in forages; it is inextricably linked to dry matter increase and plant support capacity. In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R 2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg −1. The estimation of grassland productivity based on red-edge and NIR bands showed a R 2 = 0.54 and a RMSE = 1800 kg ha −1. For the LAI, the best index was the NAOC (R 2 = 0.57 and RMSE = 1.4 m 2 m −2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.","publication_date":"2020,5,15","publication_name":"Agronomy","grobid_abstract_attachment_id":"115604633"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true,"seo_quality":null}}["work"]; window.loswp.workCoauthors = [45518824]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "safe_v1"; window.loswp.fullPageMobileSutdModalVariant = "control"; window.loswp.useOptimizedScribd4genScript = false; window.loginModal = {}; window.loginModal.appleClientId = 'edu.academia.applesignon'; window.userInChina = "false";</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container design-variant-safe-v1"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":115604633,"attachmentType":"pdf"}"><img alt="First page of “Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/115604633/mini_magick20240802-1-6w0sla.png?1722571542" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/images/single_work_splash/adobe_icon.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title ds2-5-heading-sans-serif-lg">Mapping Productivity and Essential Biophysical Parameters of Cultivated Tropical Grasslands from Sentinel-2 Imagery</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="45518824" href="https://independent.academia.edu/PetersonFiorio"><img alt="Profile image of Peterson Fiorio" class="ds-work-card--author-avatar" src="//a.academia-assets.com/images/s65_no_pic.png" />Peterson Fiorio</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2020, Agronomy</p><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">23 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 120463937; 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In recent years, high spectral and spatial resolution remote sensors, e.g., the European Space Agency (ESA)'s Sentinel satellite missions, have become freely available for agricultural science, and have proven to be powerful monitoring tools. The use of vegetation indices has been essential for crop monitoring and biomass estimation models. The objective of this work is to test and demonstrate the applicability of different vegetation indices to estimate the biomass productivity, the foliar nitrogen content (FNC), the plant height and the leaf area index (LAI) of several tropical grasslands species submitted to different nitrogen (N) rates in an experimental area of São Paulo, Brazil. Field reflectance data of Panicum maximum and Urochloa brizantha species' cultivars were taken and convoluted to the Sentinel-2 satellite bands. Subsequently, different vegetation indices (Normalized Difference Vegetation Index (NDI), Three Band Index (TBI), Difference light Height (DLH), Three Band Dall'Olmo (DO), and Normalized Area Over reflectance Curve (NAOC)) were tested for the experimental grassland areas, and composed of Urochloa decumbens and Urochloa brizantha grass species, which were sampled and destructively analyzed. Our results show the use of different relevant Sentinel-2 bands in the visible (VIS)-near infrared (NIR) regions for the estimation of the different biophysical parameters. The FNC obtained the best correlation for the TBI index combining blue, green and red bands with a determination coefficient (R 2) of 0.38 and Root Mean Square Error (RMSE) of 3.4 g kg −1. The estimation of grassland productivity based on red-edge and NIR bands showed a R 2 = 0.54 and a RMSE = 1800 kg ha −1. For the LAI, the best index was the NAOC (R 2 = 0.57 and RMSE = 1.4 m 2 m −2). High values of FNC, productivity and LAI based on different sets of Sentinel-2 bands were consistently obtained for areas under N fertilization.</p></div></div></div></div></div></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><style type="text/css">.ds-loswp-section--container { display: flex; flex-direction: column; width: 100%; padding-right: 40px; }</style><div class="ds2-5-content-section"><div class="ds2-5-content-section__heading"><h2 class="ds2-5-content-section__heading__text">Related papers</h2></div><div class="ds2-5-content-section__main"><div class="ds2-5-content-section__main__content"><div class="ds-loswp-section--container"><div class="ds-related-content--container"><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="36503775" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/36503775/Review_of_spectral_vegetation_indices_and_methods_for_estimation_of_crop_biophysical_variables">Review of spectral vegetation indices and methods for estimation of crop biophysical variables</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="837317" href="https://bas.academia.edu/LachezarFilchev">Lachezar Filchev</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Aerospace Research in Bulgaria, 2017</p><p class="ds-related-work--abstract ds2-5-body-sm">In present article a brief overview is presented on spectral vegetation indices and methods for estimation of crop main biophysical variables and their proxies. The main VIs used in estimation of nitrogen and chlorophyll, biomass, LAI and fAPAR, fCover, and photosynthesis are summarized. Biophysical variables and vegetation indices A number of techniques have evolved to derive the biophysical variables of vegetation using remote sensing data; these can be grouped into three broad categories: the inversion of radiative transfer models [39], machine learning (for example neural networks) [4] and the use of vegetation Indices. There are generally few ways of deriving the biophysical estimates using empirical or semi-empirical relationships: 1) single regression; 2) stepwise linear regression; 3) partial least squares (PLS) regression; 4) artificial neural networks [12]. Methods based on vegetation indices (VIs) have the benefit of being computationally simple while they are generally less site specific and more universally applicable than the other methods. The performance of the different indices and selected "optimal" wavebands depends on vegetation and land cover type, the variables to be retrieved, sun/view geometry to name but a few [12]. Satellite spectral data has the potential to measure the reflected radiation from many plants, thus making assessment of biophysical variables feasible on canopy level. The regression models relate in situ measurements and VIs. The VIs are mathematical transformations of the original spectral reflectance that are designed to reduce the additive and multiplicative errors associated with atmospheric effects, solar illumination, soil background effects, and sensor viewing geometry [29].</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Review of spectral vegetation indices and methods for estimation of crop biophysical variables","attachmentId":56423762,"attachmentType":"pdf","work_url":"https://www.academia.edu/36503775/Review_of_spectral_vegetation_indices_and_methods_for_estimation_of_crop_biophysical_variables","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/36503775/Review_of_spectral_vegetation_indices_and_methods_for_estimation_of_crop_biophysical_variables"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="56705426" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/56705426/Remote_Sensing_of_Grassland_Biophysical_Parameters_in_the_Context_of_the_Sentinel_2_Satellite_Mission">Remote Sensing of Grassland Biophysical Parameters in the Context of the Sentinel-2 Satellite Mission</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="100973286" href="https://independent.academia.edu/RJuszczak">Radoslaw Juszczak</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Sensors, 2016</p><p class="ds-related-work--abstract ds2-5-body-sm">This study investigates the potential of the Sentinel-2 satellite for monitoring the seasonal changes in grassland total canopy chlorophyll content (CCC), fraction of photosynthetically active radiation absorbed by the vegetation canopy (FAPAR), and fraction of photosynthetically active radiation absorbed only by its photosynthesizing components (GFAPAR). Reflectance observations were collected on a continuous basis during growing seasons by means of a newly developed ASD-WhiteRef system. Two models using Sentinel-2 simulated data (linear regression-vegetation indices (VIs) approach and multiple regression (MR) reflectance approach) were tested to estimate vegetation biophysical parameters. To assess whether the use of full solar spectrum reflectance data is able to provide an added value in CCC and GFAPAR estimation accuracy, a third model based on partial least squares regression (PLSR) and the ASD-WhiteRef reflectance data was tested. The results showed that FAPAR remained quite ...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Remote Sensing of Grassland Biophysical Parameters in the Context of the Sentinel-2 Satellite Mission","attachmentId":71959612,"attachmentType":"pdf","work_url":"https://www.academia.edu/56705426/Remote_Sensing_of_Grassland_Biophysical_Parameters_in_the_Context_of_the_Sentinel_2_Satellite_Mission","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/56705426/Remote_Sensing_of_Grassland_Biophysical_Parameters_in_the_Context_of_the_Sentinel_2_Satellite_Mission"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="48281615" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/48281615/On_estimating_Gross_Primary_Productivity_of_Mediterranean_grasslands_under_different_fertilization_regimes_using_vegetation_indices_and_hyperspectral_reflectance">On estimating Gross Primary Productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="25624247" href="https://lisboa.academia.edu/MariaCaldeira">Maria Caldeira</a></div><p class="ds-related-work--abstract ds2-5-body-sm">We applied an empirical modelling approach for Gross Primary Productivity (GPP) estimation from hyperspectral reflectance of Mediterranean grasslands undergoing different fertilization treatments. The objective of the study was to identify combinations of vegetation indices and bands that better represent GPP changes between the annualpeak of growth and senescence dry out in Mediterranean grasslands. In-situ hyperspectral measurements of vegetation were collected at the same time as CO 2 gas exchange measurements were performed in control (C) and fertilized plots with added nitrogen (N), phosphorus (P) or the combination of N, P and potassium (NPK). Reflectance values were aggregated, according to their similarity (r>90%), in 26 continuous wavelength intervals (Hyp). Also, the same reflectance values were resampled reproducing the spectral bands of both Sentinel-2A Multispectral Instrument (S2) and Landsat 8 Operation Land Imager (L8) simulating the signal that would be captured in ideal conditions by either Sentinel-2A or Landsat 8. The LEAPS procedure was applied to select the best set of the vegetation indices or spectral bands for GPP estimation using Hyp, S2 or L8. The LEAPS selected some vegetation indices putting in evidence their explanatory power as indicators of the dynamic changes occurring in community vegetation properties such as canopy water content (NDWI) or chlorophyll and carotenoids/chlorophyll ratio (MTCI, PSRI, GNDVI) and underlining their importance for grasslands GPP estimates. For Hyp and S2, bands showed similar explanatory power than vegetation indices to estimate GPP. A two-step LEAPS procedure allowed us also to identify spectral bands with potential for improving GPP estimates. This procedure clearly indicates the shortwave infrared region of the spectra as promising for this purpose. The comparison of S2 and L8 based models showed similar explanatory power of the two simulated satellite sensors when spectral bands were adopted. Altogether our results show the potential of sensors on board of Sentinel 2 and Landsat 8 satellites for monitoring grasslands phenology and improving GPP estimates in support of a sustainable agriculture management.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"On estimating Gross Primary Productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance","attachmentId":66976817,"attachmentType":"pdf","work_url":"https://www.academia.edu/48281615/On_estimating_Gross_Primary_Productivity_of_Mediterranean_grasslands_under_different_fertilization_regimes_using_vegetation_indices_and_hyperspectral_reflectance","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/48281615/On_estimating_Gross_Primary_Productivity_of_Mediterranean_grasslands_under_different_fertilization_regimes_using_vegetation_indices_and_hyperspectral_reflectance"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="11647440" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/11647440/Spectral_Data_for_Determination_of_Crop_Vegetation_Indices">Spectral Data for Determination of Crop Vegetation Indices</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="22181871" href="https://uma-ir.academia.edu/elmiraghazanfarijajin">elmira ghazanfari-jajin</a></div><p class="ds-related-work--abstract ds2-5-body-sm">The field experiments with four levels of N-fertilizer (0, 30, 60, and 90 kgℎ −1 ) in two repetitions were conducted for three years to select some appropriate vegetation indices for winter wheat. Hyper-spectral reflectance data using a portable field spectroradiometer (351 to 2,500 nm) were recorded from 10 am to 2 pm under cloudless conditions at two different growth stages of winter wheat. All two-band and three band combinations of several vegetation indices were subsequently calculated in an algorithm for determining linear regression analysis against SPAD value, protein content, and grain yield. R square matrices were used to make contour plots and 3-D scatters. Using overlaying in analysis tools of ArcMap the between first and second year results, a number of common hot spots with strong correlations were revealed. The selected hot spots were validated with the dataset of the third year to choose the best vegetation indices for crop variable estimations.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Spectral Data for Determination of Crop Vegetation Indices","attachmentId":37108016,"attachmentType":"pdf","work_url":"https://www.academia.edu/11647440/Spectral_Data_for_Determination_of_Crop_Vegetation_Indices","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/11647440/Spectral_Data_for_Determination_of_Crop_Vegetation_Indices"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="13685353" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/13685353/Regional_estimation_of_savanna_grass_nitrogen_using_the_red_edge_band_of_the_spaceborne_RapidEye_sensor">Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32827617" href="https://wur.academia.edu/IgnasHeitkonig">Ignas Heitkonig</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="9250450" href="https://independent.academia.edu/AbelRamoelo">Abel Ramoelo</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2012</p><p class="ds-related-work--abstract ds2-5-body-sm">a b s t r a c t</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne RapidEye sensor","attachmentId":45060943,"attachmentType":"pdf","work_url":"https://www.academia.edu/13685353/Regional_estimation_of_savanna_grass_nitrogen_using_the_red_edge_band_of_the_spaceborne_RapidEye_sensor","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/13685353/Regional_estimation_of_savanna_grass_nitrogen_using_the_red_edge_band_of_the_spaceborne_RapidEye_sensor"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="50076944" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/50076944/Atmospheric_and_Radiometric_Correction_Algorithms_for_the_Multitemporal_Assessment_of_Grasslands_Productivity">Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="199012155" href="https://independent.academia.edu/FedericoVillarrealGuerrero">Federico Villarreal-Guerrero</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Remote Sensing</p><p class="ds-related-work--abstract ds2-5-body-sm">A key step in the processing of satellite imagery is the radiometric correction of images to account for reflectance that water vapor, atmospheric dust, and other atmospheric elements add to the images, causing imprecisions in variables of interest estimated at the earth's surface level. That issue is important when performing spatiotemporal analyses to determine ecosystems' productivity. In this study, three correction methods were applied to satellite images for the period 2010-2014. These methods were Atmospheric Correction for Flat Terrain 2 (ATCOR2), Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Dark Object Substract 1 (DOS1). The images included 12 sub-scenes from the Landsat Thematic Mapper (TM) and the Operational Land Imager (OLI) sensors. The images corresponded to three Permanent Monitoring Sites (PMS) of grasslands, 'Teseachi', 'Eden', and 'El Sitio', located in the state of Chihuahua, Mexico. After the corrections were applied to the images, they were evaluated in terms of their precision for biomass estimation. For that, biomass production was measured during the study period at the three PMS to calibrate production models developed with simple and multiple linear regression (SLR and MLR) techniques. When the estimations were made with MLR, DOS1 obtained an R 2 of 0.97 (p < 0.05) for 2012 and values greater than 0.70 (p < 0.05) during 2013-2014. The rest of the algorithms did not show significant results and DOS1, which is the simplest algorithm, resulted in the best biomass estimator. Thus, in the multitemporal analysis of grassland based on spectral information, it is not necessary to apply complex correction procedures. The maps of biomass production, elaborated from images corrected with DOS1, can be used as a reference point for the assessment of the grassland condition, as well as to determine the grazing capacity and thus the potential animal production in such ecosystems.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity","attachmentId":68196540,"attachmentType":"pdf","work_url":"https://www.academia.edu/50076944/Atmospheric_and_Radiometric_Correction_Algorithms_for_the_Multitemporal_Assessment_of_Grasslands_Productivity","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/50076944/Atmospheric_and_Radiometric_Correction_Algorithms_for_the_Multitemporal_Assessment_of_Grasslands_Productivity"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="27017963" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/27017963/A_Comparison_of_Satellite_Derived_Vegetation_Indices_for_Approximating_Gross_Primary_Productivity_of_Grasslands">A Comparison of Satellite-Derived Vegetation Indices for Approximating Gross Primary Productivity of Grasslands</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="1036487" href="https://unsw.academia.edu/ShipingChen">Shiping Chen</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Rangeland Ecology & Management, 2014</p><p class="ds-related-work--abstract ds2-5-body-sm">Gross primary productivity (GPP) is a key component of ecosystem carbon fluxes and the carbon balance between the biosphere and the atmosphere. Accurate estimation of GPP is essential for quantifying plant production and carbon balance for grasslands. Satellite-derived vegetation indices (VIs) are often used to approximate GPP. The widely used VIs include atmospherically resistant vegetation index, enhanced vegetation index (EVI), normalized difference greenness index, normalized difference vegetation index, reduced simple ratio, ratio vegetation index, and soil-adjusted vegetation index (SAVI). The evaluation of the performance of these VIs for approximating GPP, however, has been limited to one or two VIs and/or using GPP observations from one or two sites. In this study, we examined the relationships between the nine VIs derived from the moderate resolution imaging spectroradiometer (MODIS) and tower-based GPP at five eddy covariance flux sites over the grasslands of northern China. Our results showed that the nine VIs were generally good predictors of GPP for grasslands of northern China. Overall, EVI was the best predictor. The correlation between EVI and GPP also declined from the south to the north, indicating that EVI and GPP exhibited closer relationships in more southerly sites with higher vegetation cover. We also examined the seasonal influence on the correlation between VIs and GPP. SAVI exhibited the best correlation with GPP in spring when the grassland canopy was sparse, while EVI exhibited the best correlation with GPP in summer when the grassland cover was dense. Our results also showed that VIs could capture variations in observed GPP better in drought period than in nondrought period for an alpine meadow site because of the suppression of vegetation growth by drought.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Comparison of Satellite-Derived Vegetation Indices for Approximating Gross Primary Productivity of Grasslands","attachmentId":47276844,"attachmentType":"pdf","work_url":"https://www.academia.edu/27017963/A_Comparison_of_Satellite_Derived_Vegetation_Indices_for_Approximating_Gross_Primary_Productivity_of_Grasslands","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/27017963/A_Comparison_of_Satellite_Derived_Vegetation_Indices_for_Approximating_Gross_Primary_Productivity_of_Grasslands"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="15398117" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/15398117/NIRS_meets_Ellenbergs_indicator_values_Prediction_of_moisture_and_nitrogen_values_of_agricultural_grassland_vegetation_by_means_of_near_infrared_spectral_characteristics">NIRS meets Ellenberg's indicator values: Prediction of moisture and nitrogen values of agricultural grassland vegetation by means of near-infrared spectral characteristics</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="34544374" href="https://independent.academia.edu/NorbertH%C3%B6lzel">Norbert Hölzel</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Ecological Indicators, 2012</p><p class="ds-related-work--abstract ds2-5-body-sm">Ellenberg indicator values are widely used ecological tools to elucidate relationships between vegetation and environment in ecological research and environmental planning. However, they are mainly deduced from expert knowledge on plant species and are thus subject of ongoing discussion. We researched if Ellenberg indicator values can be directly extracted from the vegetation biomass itself. Mean Ellenberg "moisture" (mF) and "nitrogen" (mN) values of 141 grassland plots were related to nutrient concentrations, fibre fractions and spectral information of the aboveground biomass. We developed calibration models for the prediction of mF and mN using spectral characteristics of biomass samples with near-infrared reflectance spectroscopy (NIRS). Prediction goodness was evaluated with internal cross-validations and with an external validation data set. NIRS could accurately predict Ellenberg mN, and with less accuracy Ellenberg mF. Predictions were not more precise for cover-weighted Ellenberg values compared with un-weighted values. Both Ellenberg mN and mF showed significant and strong correlations with some of the nutrient and fibre concentrations in the biomass. Against expectations, Ellenberg mN was more closely related to phosphorus than to nitrogen concentrations, suggesting that this value rather indicates productivity than solely nitrogen. To our knowledge we showed for the first time that mean Ellenberg indicator values could be directly predicted from the aboveground biomass, which underlines the usefulness of the NIRS technology for ecological studies, especially in grasslands ecosystems.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"NIRS meets Ellenberg's indicator values: Prediction of moisture and nitrogen values of agricultural grassland vegetation by means of near-infrared spectral characteristics","attachmentId":43224527,"attachmentType":"pdf","work_url":"https://www.academia.edu/15398117/NIRS_meets_Ellenbergs_indicator_values_Prediction_of_moisture_and_nitrogen_values_of_agricultural_grassland_vegetation_by_means_of_near_infrared_spectral_characteristics","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/15398117/NIRS_meets_Ellenbergs_indicator_values_Prediction_of_moisture_and_nitrogen_values_of_agricultural_grassland_vegetation_by_means_of_near_infrared_spectral_characteristics"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="118368016" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/118368016/Remote_Sensing_Grassland_Productivity_Attributes_A_Systematic_Review">Remote Sensing Grassland Productivity Attributes: A Systematic Review</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="306129969" href="https://independent.academia.edu/MbulisiSibanda1">Mbulisi Sibanda</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Remote Sensing</p><p class="ds-related-work--abstract ds2-5-body-sm">A third of the land on the Earth is composed of grasslands, mainly used for forage. Much effort is being conducted to develop tools to estimate grassland productivity (GP) at different extents, concentrating on spatial and seasonal variability pertaining to climate change. GP is a reliable indicator of how well an ecosystem works because of its close connection to the ecological system equilibrium. The most commonly used proxies of GP in ecological studies are aboveground biomass (AGB), leaf area index (LAI), canopy storage capacity (CSC), and chlorophyll and nitrogen content. Grassland science gains much information from the capacity of remote sensing (RS) techniques to calculate GP proxies. An overview of the studies on RS-based GP prediction techniques and a discussion of current matters determining GP monitoring are critical for improving future GP prediction performance. A systematic review of articles published between 1970 and October 2021 (203 peer-reviewed articles from Web...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Remote Sensing Grassland Productivity Attributes: A Systematic Review","attachmentId":114011784,"attachmentType":"pdf","work_url":"https://www.academia.edu/118368016/Remote_Sensing_Grassland_Productivity_Attributes_A_Systematic_Review","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/118368016/Remote_Sensing_Grassland_Productivity_Attributes_A_Systematic_Review"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="21019688" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/21019688/A_method_using_different_remote_sensing_techniques_for_estimating_grassland_bio_physical_variables">A method using different remote sensing techniques for estimating grassland bio-physical variables</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="32051371" href="https://wur.academia.edu/JanGPWClevers">Jan G. P. W. Clevers</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2005</p><p class="ds-related-work--abstract ds2-5-body-sm">For efficient grassland management, information on the spatial variation of the crop within fields is of the utmost importance. Currently, this mainly depends on qualitative expert knowledge. Quantitative information on the actual status of grass swards at the right moment in the season is important for management decisions, like nitrogen supply, water supply or harvesting.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A method using different remote sensing techniques for estimating grassland bio-physical variables","attachmentId":41674007,"attachmentType":"pdf","work_url":"https://www.academia.edu/21019688/A_method_using_different_remote_sensing_techniques_for_estimating_grassland_bio_physical_variables","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/21019688/A_method_using_different_remote_sensing_techniques_for_estimating_grassland_bio_physical_variables"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div></div></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":115604633,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":115604633,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_115604633" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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