Search results for "leaf area"

showing 4 items of 124 documents

Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data.

2021

Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) f…

leaf area indexARTMO toolboxSciencenitrogen; chlorophyll; leaf area index; agro-ecosystem monitoring; spectral indices; random forest; gaussian processes regression; ARTMO toolboxQspectral indiceschlorophyllgaussian processes regressionagro-ecosystem monitoringnitrogenrandom forestRemote sensing
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Estimation of key biophysical parameters related to crop stress through new remote sensors and multi-crop in situ data

2020

El monitoreo de los cultivos a lo largo de toda la etapa de crecimiento es esencial para detectar anomalías y optimizar los costes y recursos del sector agrícola. Las variables biofísicas de la vegetación, principalmente el contenido en agua, el índice de área foliar o la clorofila, están consideradas indicadores importantes de la salud, crecimiento y productividad de la vegetación. Además, estos parámetros biofísicos no solo son importantes por proporcionar información por sí mismos del estado fisiológico de la vegetación, también porque son parámetros clave de entrada en importantes modelos agronómicos. La medida directa de estos parámetros biofísicos sobre el terreno requiere elevados re…

parámetros biofísicosagriculturaleaf area indexclorofilasentinel-2:CIENCIAS DE LA TIERRA Y DEL ESPACIO [UNESCO]teledetección:CIENCIAS AGRARIAS [UNESCO]UNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIOcontenido en aguaUNESCO::CIENCIAS AGRARIAS
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Mapping productivity and essential biophysical parameters of cultivated tropical grasslands from sentinel-2 imagery.

2020

Nitrogen (N) is the main nutrient element that maintains productivity in forages

productivityTeledetecció010504 meteorology & atmospheric sciencesNitrogenTropical and subtropical grasslands savannas and shrublandsUrochloa brizanthaBiomassaPanicum01 natural sciencesNormalized Difference Vegetation IndexGrasslandCapim Urochloalcsh:AgriculturePastagemremote sensingVegetation indexUrochloaNitrogênioLeaf area indexPASTAGENS0105 earth and related environmental sciencesProductivityBiomass (ecology)geographygeography.geographical_feature_categoryleaf area indexbiology<i>Panicum</i>PasturesUrochloa decumbenslcsh:S04 agricultural and veterinary sciencesVegetationRemote sensingbiology.organism_classificationTropical grasslandsBiomass productionAgronomyProductivity (ecology)vegetation indicesLeaf area index040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental scienceSentinel-2<i>Urochloa</i>Agronomy and Crop ScienceImatges ProcessamentSensoriamento Remoto
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Global Estimation of Biophysical Variables from Google Earth Engine Platform

2018

This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estim…

random forestsCWC010504 meteorology & atmospheric sciencesMean squared errorScience0211 other engineering and technologiesGoogle Earth Engine; LAI; FVC; FAPAR; CWC; plant traits; random forests; PROSAIL02 engineering and technologyLand cover01 natural sciencesAtmospheric radiative transfer codesRange (statistics)Parametrization (atmospheric modeling)FAPARLeaf area index021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingPROSAILQ15. Life on landFVCLAIRandom forestplant traits13. Climate actionPhotosynthetically active radiationGeneral Earth and Planetary SciencesEnvironmental scienceGoogle Earth EngineRemote Sensing; Volume 10; Issue 8; Pages: 1167
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