Search results for "Hybrid retrieval"

showing 4 items of 4 documents

Mapping landscape canopy nitrogen content from space using PRISMA data

2021

Abstract Satellite imaging spectroscopy for terrestrial applications is reaching maturity with recently launched and upcoming science-driven missions, e.g. PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), respectively. Moreover, the high-priority mission candidate Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is expected to globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. Thanks to the provision of contiguous visible-to-shortwave infrared spectral data, hyperspectral missions open enhanced …

Active learningActive learning (machine learning)Computer scienceDimensionality reductionHyperspectral imagingPRISMAContext (language use)CollinearityHybrid retrievalDimensionality reductionImaging spectroscopyAtomic and Molecular Physics and OpticsComputer Science ApplicationsImaging spectroscopyCHIMEKrigingEnMAPCanopy nitrogen contentComputers in Earth SciencesEngineering (miscellaneous)Gaussian process regressionRemote sensingISPRS Journal of Photogrammetry and Remote Sensing
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Seasonal Mapping of Irrigated Winter Wheat Traits in Argentina with a Hybrid Retrieval Workflow Using Sentinel-2 Imagery

2022

Earth observation offers an unprecedented opportunity to monitor intensively cultivated areas providing key support to assess fertilizer needs and crop water uptake. Routinely, vegetation traits mapping can help farmers to monitor plant development along the crop’s phenological cycle, which is particularly relevant for irrigated agricultural areas. The high spatial and temporal resolution of the Sentinel-2 (S2) multispectral instrument leverages the possibility to estimate leaf area index (LAI), canopy chlorophyll content (CCC), and vegetation water content (VWC) from space. Therefore, our study presents a hybrid retrieval workflow combining a physically-based strategy with a machine learni…

Leaf Area IndexVegetation Water and Chlorophyll ContentActive LearningContenido de Agua y Clorofila de la VegetaciónDimencionality ReductionÍndice de Superficie FoliarAprendizaje ActivoReducción de DimensionalidadKrigingImágenesHybrid Retrieval WorkflowFlujo de Trabajo de Recuperación HíbridoGeneral Earth and Planetary SciencesImageryleaf area index; vegetation water and chlorophyll content; Gaussian processes regression; hybrid retrieval workflow; dimensionality reduction; active learningKrigeageRemote Sensing
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Assessing Non-Photosynthetic Cropland Biomass from Spaceborne Hyperspectral Imagery

2021

Non-photosynthetic vegetation (NPV) biomass has been identified as a priority variable for upcoming spaceborne imaging spectroscopy missions, calling for a quantitative estimation of lignocellulosic plant material as opposed to the sole indication of surface coverage. Therefore, we propose a hybrid model for the retrieval of non-photosynthetic cropland biomass. The workflow included coupling the leaf optical model PROSPECT-PRO with the canopy reflectance model 4SAIL, which allowed us to simulate NPV biomass from carbon-based constituents (CBC) and leaf area index (LAI). PROSAIL-PRO provided a training database for a Gaussian process regression (GPR) algorithm, simulating a wide range of non…

PCACoefficient of determinationDimensionality reductionScienceQBiomassHyperspectral imaginghybrid retrievalPRISMAPROSAIL-PROVegetationNPVImaging spectroscopyCHIMEKrigingactive learningGeneral Earth and Planetary SciencesEnvironmental scienceLeaf area indexPRISMA; CHIME; NPV; Gaussian process regression; hybrid retrieval; active learning; PCA; PROSAIL-PROGaussian process regressionRemote sensingRemote Sensing
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Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.

2022

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically-based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C)…

sentinel-2active learning (AL)Soil ScienceGeologyUNESCO::CIENCIAS TECNOLÓGICASUncertainty estimategaussian processes (GP)google earth engineBiophysical and biochemical crop traiteuclidean distance-based diversity (EBD)top-of-atmosphere reflectancehybrid retrieval methodsHybrid retrieval methoduncertainty estimatesbiophysical and biochemical crop traitsatmosphere radiative transfer modelComputers in Earth SciencesRemote sensing of environment
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