0000000000512097

AUTHOR

Matías Salinero-delgado

showing 5 related works from this author

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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Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine

2022

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SC…

Vegetation traitsTime seriesvegetation traits; Sentinel-3; TOA radiance; OLCI; Gaussian process regression; machine learning; hybrid method; time series; Google Earth EngineTOA radianceMachine learningHybrid methodGeneral Earth and Planetary SciencesMatemática AplicadaSentinel-3OLCIGoogle Earth EngineGaussian process regressionRemote Sensing
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Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine

2021

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine …

Earth observationGoogle Earth Engine (GEE); Gaussian process regression (GPR); machine learning; Sentinel-2; gap filling; leaf area index (LAI)010504 meteorology & atmospheric sciencesComputer scienceScienceleaf area index (LAI)0211 other engineering and technologiesCloud computing02 engineering and technologycomputer.software_genre01 natural sciencesKrigingGaussian process regression (GPR)021101 geological & geomatics engineering0105 earth and related environmental sciencesPixelbusiness.industryQGoogle Earth Engine (GEE)machine learningKernel (image processing)Ground-penetrating radarGeneral Earth and Planetary SciencesData miningSentinel-2Scale (map)businesscomputergap fillingLevel of detailRemote Sensing; Volume 13; Issue 3; Pages: 403
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Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes

2023

Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Senti…

radar vegetation index; time series; irrigated winter wheat; cross-correlationGeneral Earth and Planetary SciencesRemote Sensing
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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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