Pablo Reyes-Muñoz

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

Santiago Belda

University of Alicante

Matías Salinero-Delgado

Parc Científic de la Universitat de València

Juan Pablo Rivera

Universidad Autónoma de Nayarit

Luca Pipia

Institut Cartogràfic i Geològic de Catalunya

Dávid Kovács

Parc Científic de la Universitat de València

Gerbrand Koren

Utrecht University

Enrico Tomelleri

Free University of Bozen-Bolzano

Helge Aasen

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Cooperative Institutions

Parc Científic de la Universitat de València

Universitat de València

Finnish Meteorological Institute

Centre National de la Recherche Scientifique

Centre d'Études Spatiales de la Biosphère

Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

Max Planck Institute for Biogeochemistry

Université Toulouse III - Paul Sabatier

Institut de Recherche pour le Développement

Université de Toulouse

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Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Lammert Kooistra Katja Berger Benjamin Brede Lukas Valentin Graf Helge Aasen

Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring.

10.5194/bg-2023-88

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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

Remote Sensing (2023)

Gabriel Caballero Alejandro Pezzola Cristina Winschel Paolo Sanchez Angonova Alejandra Casella

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 Sentinel-2 (S2) vegetation water content (VWC) time series over a dry agri-environment in southern Argentina. MOGP models not only exploit the auto-correlations of S1 and S2 data streams independently but also the inter-channel cross-correlations. The S1 RVI and S2 VWC time series at the selected study sites being the inputs of the MOGP models proved to be closely correlated. Regarding the set of assessed models, the Convolutional Gaussian model (CONV) delivered noteworthy accurate data fusion results over winter wheat croplands belonging to the 2020 and 2021 campaigns (NRMSEwheat2020 = 16.1%; NRMSEwheat2021 = 10.1%). Posteriorly, we removed S2 observations from the S1 & S2 dataset corresponding to the complete phenological cycles of winter wheat from September to the end of December to simulate the presence of clouds in the scenes and applied the CONV model at the pixel level to reconstruct spatiotemporally-latent VWC maps. After applying the fusion strategy, the phenology of winter wheat was successfully recovered in the absence of optical data. Strong correlations were obtained between S2 VWC and S1 & S2 MOGP VWC reconstructed maps for the assessment dates (R2¯wheat−2020 = 0.95, R2¯wheat−2021 = 0.96). Altogether, the fusion of S1 SAR and S2 optical EO data streams with MOGP offers a powerful innovative approach for cropland trait monitoring over cloudy high-latitude regions.

Sensor Fusion

10.3390/rs15071822

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Citations (15)

Comment on bg-2023-88

Lammert Kooistra Katja Berger Benjamin Brede Lukas Valentin Graf Helge Aasen

Abstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring.

10.5194/bg-2023-88-rc1

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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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Citations (60)

A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0

Wolfgang Knorr Mathew Williams Tea Thum T. Kaminski Michael Voßbeck

Abstract. Advances in Earth Observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water land-surface models with the capability to simultaneously assimilate several of such data streams. The present article discusses the requirements for such a model and presents one such model based on the combination of the existing DALEC land vegetation carbon cycle model with the BETHY land-surface and terrestrial vegetation scheme. The resulting D&B model, made available as a community model, is presented together with a comprehensive evaluation for two selected study sites of widely varying climate. We then demonstrate the concept of land surface modelling aided by data streams that are available from satellite remote sensing. Here we present D&B with four observation operators that translate model-derived variables into measurements available from such data streams, namely: fraction of photosynthetically active radiation (FAPAR), solar-induced chlorophyll fluorescence (SIF), vegetation optical depth (VOD) at microwave frequencies, and near-surface soil moisture, also available from microwave measurements. As a first step, we evaluate the combined model system using local observations, and finally discuss the potential of the system presented for multi-stream data assimilation in the context of Earth Observation systems.

10.5194/egusphere-2024-1534

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Multi-decadal temporal reconstruction of Sentinel-3 OLCI-based vegetation products with multi-output Gaussian process regression

Ecological Informatics (2024)

Dávid Kovács Pablo Reyes-Muñoz Katja Berger Viktor Ixion Mészáros Gabriel Caballero

10.1016/j.ecoinf.2024.102816

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Monitoring vegetation traits over Europe using top-of-atmosphere Sentinel-3 data in Google Earth Engine

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

<p>Monitoring the terrestrial photosynthetic capacity is vital for understanding ecological processes and modelling the responses of vegetated ecosystems to diverse environmental changes. Among multiple instruments foreseen to collect data over global terrestrial landscapes in the near future, the "FLuorescence EXplorer" (FLEX) mission of the European Space Agency (ESA) is planned to be launched by 2024. FLEX will be dedicated to vegetation fluorescence measurements and will partner with the operational Sentinel-3 (S3) in a tandem mission. Thanks to the emergence of cloud-computing platforms, such as Google Earth Engine (GEE), and the ability of machine learning (ML) methods to efficiently solve prediction problems, a shift of paradigm moving away from traditional image analysis to independent cloud-based processing can be observed. Therefore, we present a workflow to automate the spatiotemporal mapping of essential vegetation traits from S3 imagery in GEE, including leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC). The retrieval strategy involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulated by the coupled canopy radiative transfer model (RTM) Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) and the atmospheric RTM Second Simulation of a Satellite Signal in the Solar Spectrum-vector (6SV). This approach takes advantage of the physical principles of RTMs with the computational performance of ML. The established S3 TOA-GPR 1.0 retrieval models were directly implemented in GEE to quantify the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor. Theoretical validation provided good to high accuracy with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI). Subsequently, a three-fold evaluation approach was pursued at diverse sites and land cover types: (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 campaigns. Validation against these three data sets achieved promising results. For the MODIS FAPAR product, selected sites demonstrated coherent seasonal patterns, with spatially-averaged mean differences of only 7%. With respect to spatial mapping comparison, estimates provided by the S3 TOA-GPR 1.0 models indicated the highest consistency with FVC and FAPAR CGLS products, with absolute deviations of retrievals below 0.3. Moreover, the direct validation of our S3 TOA-GPR 1.0 models against VALERI estimates indicated good retrieval performance for LAI, FAPAR and FVC. With these promising results, our proposed retrieval workflow opens the path towards usage and optimisation of continental-to-global monitoring of fundamental vegetation traits in GEE, accessible to the whole research community. Eventually, observations of these vegetation traits can be assimilated into terrestrial biosphere models for estimating global gross primary productivity and carbon fluxes. Consequently, once FLEX is launched, the presented S3 TOA-GPR 1.0 retrieval models are expected to contribute to process-based assimilation models aiming to quantify actual terrestrial photosynthetic activity from future S3-FLEX mission data.&#160;</p>

Photosynthetically active radiation

Earth observation

10.5194/egusphere-egu22-5919

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Citations (12)

Supplementary material to "A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0"

Wolfgang Knorr Matthew Williams Tea Thum Thomas Kaminski Michael Voßbeck

Assimilation (phonology)

10.5194/egusphere-2024-1534-supplement

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Untangling the Causal Links between Satellite Vegetation Products and Environmental Drivers on a Global Scale by the Granger Causality Method

Remote Sensing (2023)

Dávid Kovács Eatidal Amin Katja Berger Pablo Reyes-Muñoz Jochem Verrelst

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products’ (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T’s causality is evenly distributed amongst all biomes with fractional covers between ∼10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet’s ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.

Biome

Shortwave radiation

Photosynthetically active radiation

10.3390/rs15204956

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Citations (5)

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)