Search results for "crop traits"

showing 2 items of 2 documents

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

2021

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time seri…

2. Zero hungerland surface phenology (LSP)010504 meteorology & atmospheric sciencesScienceQGoogle Earth Engine (GEE)0211 other engineering and technologiesGaussian Process Regression (GPR)02 engineering and technology15. Life on land01 natural sciencescrop traitsGeneral Earth and Planetary Sciencesland surface phenology (LSP); Google Earth Engine (GEE); Gaussian Process Regression (GPR); Sentinel-2; gap-filling; crop traits; hybrid modelsSentinel-2gap-filling021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote 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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