José Estévez

Parc Científic de la Universitat de València

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Jochem Verrelst

Parc Científic de la Universitat de València

Katja Berger

Parc Científic de la Universitat de València

Matías Salinero-Delgado

Parc Científic de la Universitat de València

Luca Pipia

Institut Cartogràfic i Geològic de Catalunya

Santiago Belda

University of Alicante

Juan Pablo Rivera

Universidad Autónoma de Nayarit

Pablo Reyes-Muñoz

Parc Científic de la Universitat de València

Vanessa Paredes

Instituto Tecnológico Agrario de Castilla y León

Jorge Vicent

Universitat de València

Matthias Wocher

Ludwig-Maximilians-Universität München

Cooperative Institutions

Parc Científic de la Universitat de València

Universitat de València

Universitat Politècnica de València

Helmholtz Centre for Environmental Research

Leipzig University

Universitat Politècnica de Catalunya

Max Planck Institute for Biogeochemistry

Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences

Magellium (France)

University of Milan

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Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

Remote Sensing (2024)

Ana B. Pascual-Venteo Jose L. Garcia Katja Berger José Estévez Jorge Vicent

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching R2 = 0.92 (LAI), R2 = 0.72 (CCC) and 0.68 (CWC), R2 = 0.94 (FAPAR), and R2 = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between 4% and 16% (R2 between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level.

10.3390/rs16071211

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Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Remote Sensing of Environment (2022)

José Estévez Matías Salinero-Delgado Katja Berger Luca Pipia Juan Pablo Rivera

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) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (

10.1016/j.rse.2022.112958

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Quantifying agricultural traits and land surface phenology metrics in Google Earth Engine.

Matías Salinero-Delgado José Estévez Luca Pipia Santiago Belda Katja Berger

Monitoring of crop growth, variability and dynamics over agricultural areas is needed to optimize management practices and thus to ensure global food security. Nonetheless, estimation of 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.&#160;Since 2017, the European Space Agency (ESA) Copernicus Sentinel-2A & B (S2) have been providing high resolution optical imagery all over the globe with an observation frequency of 5 days. With 13 spectral channels and 10-60m spatial resolution, time series of these data offer untapped potential for monitoring cultivated areas. In this respect, the processing of S2 imagery in cloud-based platforms, such as Google Earth Engine (GEE), allows large-scale precise mapping of agricultural fields. The arrival of GEE enabled us to propose an end-to-end processing chain for vegetation phenology characterization using S2 imagery at large scale.To achieve this, the following pipeline was implemented: (1) building hybrid Gaussian process regression (GPR) models optimized with active learning (AL) for retrieval of crop traits, such as leaf area index (LAI), fractional vegetation cover (FVC), canopy chlorophyll content (laiCab), canopy dry matter content (laiCm) and canopy water content (laiCw), (2) implementing these models into GEE, (3) generating spatially continuous maps and gap-filled time series of these crop traits, and finally (4) calculating land surface phenology (LSP) metrics, such as start of season (SOS) or end of season (EOS), by using the conventional double logistic approach.In respect to step (1): variable-specific training datasets were generated in the ARTMO software environment using PROSAIL model simulations, with training samples reduced in number but optimized in quality, i.e. representativeness, using the Euclidean-distance based (EBD) AL technique. In this way, light retrieval models were generated via GPR, a ML algorithm which builds up a retrieval model by learning the non-linear relationships between the spectral signals and crop traits of interest. Overall, good to high performance was achieved in particular for the estimation of canopy-level traits, such as LAI and laiCab, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. Subsequently, (2) the retrieval models were integrated into the GEE environment to perform mean value prediction on-the-fly. In this way, time series of crop traits based on S2 images were produced quasi-instantly over the area of interest. As demonstration of the workflow capability to easily reconstruct time series of S2 entire tiles, phenology maps from multiple crop traits were generated over an agricultural area in Castile and Leon, Spain. For this region also crop calendar data were available to assess the validity of the LSP metrics derived from crop traits. In addition, LSP metrics derived from the Normalized Difference Vegetation Index (NDVI) were used as reference, demonstrating the good quality of the quantitative traits products to describe phenology. Thanks to the GEE framework, the proposed workflow can be carried out globally in any time window, thus representing a shift in satellite data processing towards cloud computing.&#160;

Land Cover

Earth observation

10.5194/egusphere-egu22-9944

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Mapping Essential Vegetation Variables Over Europe Using Gaussian Process Regression and Sentinel-3 Data in Google Earth Engine

Pablo Reyes-Muñoz Luca Pipia Matías Salinero-Delgado Charlotte De Grave José Estévez

Along with the unprecedented availability of satellite data acquisition and technological facilities, monitoring of the Biosphere became priority during last years. At the same time, machine learning (ML) solutions evolved into standard practice to solve prediction problems and speed up processing tasks. With the ambition to overcome limitations related to technical resources in satellite image processing, in this work we implemented the ML algorithm Gaussian process regression (GPR) into Google Earth Engine (GEE) to enable spatiotemporal mapping of vegetation traits at European scale. Also, associated uncertainty is provided, allowing to evaluate robustness of the models. In the case of LAI, deviations lower than 1.2 m ² /m ² are observed. The used imagery collection came from the Sentinel-3 (S3) OLCI (Ocean and Land Colour Instrument) top-of-atmosphere (TOA) radiance (L1C) starting from April 2016 until the present date. The generated products were then further used to analyze phenology. A demonstration case is provided over the Iberian peninsula. We observed annual patterns with peaks during spring close to 20 µg/cm ² for LCC (Leaf Clorophyl Content), 1.5 m ² /m ² for LAI (Leaf Area Index) and 0.5 for FAPAR (Fraction of Absorbed Photosyntheticaly Active Radiation) and FVC (Fractional Vegetation Cover), calculated as average over the targeted area. Eventually, the developed S3 vegetation products are aimed to support of the FLEX fluorescence mission that is dedicated to monitor vegetation photosynthetic activity.

10.1109/igarss47720.2021.9553717

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Citations (2)

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression

Remote Sensing (2021)

Matías Salinero-Delgado José Estévez Luca Pipia Santiago Belda Katja Berger

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 series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.

Growing season

Photosynthetically active radiation

10.3390/rs14010146

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Citations (44)

Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow

Remote Sensing (2021)

José Estévez Katja Berger Jorge Vicent Juan Pablo Rivera Matthias Wocher

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Established VHGPR models were then applied to S2 L1C and L2A reflectance data for mapping: leaf chlorophyll content (

10.3390/rs13081589

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

Remote Sensing (2022)

Pablo Reyes-Muñoz Luca Pipia Matías Salinero-Delgado Santiago Belda Katja Berger

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) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor.Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016-2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated with regard to jurisdictional claims in good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.

Photosynthetically active radiation

Moderate-resolution imaging spectroradiometer

Land Cover

Earth observation

10.3390/rs14061347

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Citations (18)

Gaussian processes retrieval of LAI from Sentinel-2 top-of-atmosphere radiance data

ISPRS Journal of Photogrammetry and Remote Sensing (2020)

José Estévez Jorge Vicent Juan Pablo Rivera Pablo Morcillo-Pallarés Francesco Vuolo

Lookup table

10.1016/j.isprsjprs.2020.07.004

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Citations (56)