SCOPE model inversion for Sentinel-3 data retrieval

The SCOPE is a coupled radiative transfer and energy balance model used for simulation of vegetation optical properties and temperature at leaf and canopy level over a spectral range from 0.4 to 50 μm. Inversion of the model allows retrieving a number of plant traits: pigments (Cab, Car, Cant), dry matter content (Cdm), water content (Cw), leaf area index (LAI) and others. Subsequent forward simulation can calculate photosynthesis, evapotranspiration (ET) and a fraction of absorbed photosynthetically active radiation (fAPAR) that can be used further for integrated water use efficiency (WUE) and light use efficiency (LUE) calculations, respectively. The higher the accuracy in retrieved parameters is achieved the higher precision in calculated ecosystem functional properties will be. This work aimed to develop a model-based retrieval algorithm from multispectral satellite data. The initial retrieval algorithm used numerical optimization of residuals squared sum and operated over the spectral range from 0.4 to 2.4 μm. First, the algorithm was extended to the thermal domain (up to 50 μm) and validated against open-source spectral measurement datasets (SPECCHIO). As the SCOPE model operates at both leaf and canopy levels, we had to use different cost functions and constraints for each level. Having validated the hyperspectral retrieval algorithm, we tried to make a convolution to the multispectral case of Sentinel-3 satellite sensors: ocean and land colour instrument (OLCI) and sea and land surface temperature radiometer (SLTR). Finally, parameter retrieved with the algorithm from Sentinel-3 images were used for a forward simulation of the SCOPE model and calculation of integrated WUE and LUE at few selected FLUXNET towers. The results of the simulation were validated against data from FLUXNET eddy-covariance towers

In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data

Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale

Accurate information on surface soil moisture (SSM) content at a global scale under different climatic conditions is important for hydrological and climatological applications. Machine-learning-based systematic integration of in situ hydrological measurements, complex environmental and climate data, and satellite observation facilitate the generation of reliable data products to monitor and analyse the exchange of water, energy, and carbon in the Earth system at a proper space–time resolution. This study investigates the estimation of daily SSM using 8 optimised machine learning (ML) algorithms and 10 ensemble models (constructed via model bootstrap aggregating techniques and five-fold cross-validation). The algorithmic implementations were trained and tested using International Soil Moisture Network (ISMN) data collected from 1722 stations distributed across the world. The result showed that the K-neighbours Regressor (KNR) had the lowest root-mean-square error (0.0379 cm3 cm−3) on the “test_random” set (for testing the performance of randomly split data during training), the Random Forest Regressor (RFR) had the lowest RMSE (0.0599 cm3 cm−3) on the “test_temporal” set (for testing the performance on the period that was not used in training), and AdaBoost (AB) had the lowest RMSE (0.0786 cm3 cm−3) on the “test_independent-stations” set (for testing the performance on the stations that were not used in training). Independent evaluation on novel stations across different climate zones was conducted. For the optimised ML algorithms, the median RMSE values were below 0.1 cm3 cm−3. GradientBoosting (GB), Multi-layer Perceptron Regressor (MLPR), Stochastic Gradient Descent Regressor (SGDR), and RFR achieved a median r score of 0.6 in 12, 11, 9, and 9 climate zones, respectively, out of 15 climate zones. The performance of ensemble models improved significantly, with the median RMSE value below 0.075 cm3 cm−3 for all climate zones. All voting regressors achieved r scores of above 0.6 in 13 climate zones; BSh (hot semi-arid climate) and BWh (hot desert climate) were the exceptions because of the sparse distribution of training stations. The metric evaluation showed that ensemble models can improve the performance of single ML algorithms and achieve more stable results. Based on the results computed for three different test sets, the ensemble model with KNR, RFR and Extreme Gradient Boosting (XB) performed the best. Overall, our investigation shows that ensemble machine learning algorithms have a greater capability with respect to predicting SSM compared with the optimised or base ML algorithms; this indicates their huge potential applicability in estimating water cycle budgets, managing irrigation, and predicting crop yields.</p

In-situ start and end of growing season dates of major European crop types from France and Bulgaria at a field level

Crop phenology data offer crucial information for crop yield estimation, agricultural management, and assessment of agroecosystems. Such information becomes more important in the context of increasing year-to-year climatic variability. The dataset provides in-situ crop phenology data (first leaves emergence and harvest date) of major European crops (wheat, corn, sunflower, rapeseed) from seventeen field study sites in Bulgaria and two in France. Additional information such as the sowing date, area of each site, coordinates, method and equipment used for phenophase data estimation, and photos of the France sites are also provided. The georeferenced ground-truth dataset provides a solid base for a better understanding of crop growth and can be used to validate the retrieval of phenological stages from remote sensing data.This publication is the result of the Action CA17134 SENSECO (Opticalsynergies for spatiotemporal sensing of scalable ecophysiological traits) funded by COST (European Cooperation in Science and Technology, www.cost.eu, accessed on March 17, 2023). We thank Tiphaine Tallec for the in-situ data of the French sites, mainly funded by the Institut National des Sciences de l'Univers (INSU) through the ICOS ERIC and the OSR SW observatory (https://osr.cesbio.cnrs.fr/). Facilities and staff are funded and supported by the Observatory Midi-Pyrenean, the University Paul Sabatier of Toulouse 3, CNRS (Centre National de la Recherche Scientifique), CNES (Centre National d'Etude Spatial) and IRD (Institut de Recherche pour le Développement). We further thank Dessislava Ganeva for the in-situ data of the Bulgarian sites. SB was by partially supported by Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union NextGenerationEU (ZAMBRANO 21-04)

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence

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

International audienceAbstract. 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

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recommend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions

Simulation of ecosystem fluxes with the SCOPE model: Sensitivity to parametrization and evaluation with flux tower observations

Accurate estimates of carbon, water and energy fluxes between the Earth surface and the atmosphere are crucial for enhancing our understanding of ecosystem–climate interactions. Such estimates can be made by combining remote sensing derived land surface parameters with climate reanalysis data. We analysed to what degree generic (plant functional type (PFT)-independent) satellite-derived vegetation properties and climate reanalysis data can explain land surface fluxes and to what extent the PFT-specific information extends the flux simulations. For this purpose, we used the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model, which combines radiative transfer in plant leaves and vegetation canopies with photosynthesis and energy balance in a single model representation of the vegetation. We evaluated the performance of SCOPE in simulating fluxes by comparison to 63 eddy covariance sites representing 10 PFTs. We varied the sources of maximum carboxylation capacity (Vcmax25) and BallBerrySlope values (default vs literature), the seasonality of Vcmax25 and the meteorological forcing (locally measured and climate reanalysis). The average performance of daily flux in terms of root-mean-square error (RMSE) was 2.3 ± 0.8 μmol CO2 m−2 s−1 (R2= 0.74 ± 0.12) for gross primary productivity (GPP), 24 ± 8 W m−2 (R2= 0.68 ± 0.16) for latent heat flux (λE) and 50 ± 15 W m−2 (R20.47±0.17) for sensible heat flux (H). The inter-site variability of the annual accumulated GPP flux was captured well with seasonally varying PFT-specific Vcmax25 (R2= 0.74, RMSE = 308 g C m−2 yr−1 and bias = −68 g C m−2 yr−1). The annual accumulated evapotranspiration (ET) was overestimated (R2= 0.31, RMSE = 101 mm yr−1 and bias = 37 mm yr−1), mainly in the ecosystems with subtropical Mediterranean climate, for which the soil resistance to evaporation from porous space (rss) had to be constrained from soil moisture content (SMC) or land surface temperature (LST). Overall, the study demonstrates that SCOPE model can simulate ecosystem flux with high accuracy without site-specific calibration of its parameters

Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ, data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R2 0.36 and RMSE 2.5 t ha−1. This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields

Using Sentinel-2-Based Metrics to Characterize the Spatial Heterogeneity of FLEX Sun-Induced Chlorophyll Fluorescence on Sub-Pixel Scale

Current and upcoming Sun-Induced chlorophyll Fluorescence (SIF) satellite products (e.g., GOME, TROPOMI, OCO, FLEX) have medium-to-coarse spatial resolutions (i.e., 0.3–80 km) and integrate radiances from different sources into a single ground surface unit (i.e., pixel). However, intrapixel heterogeneity, i.e., different soil and vegetation fractional cover and/or different chlorophyll content or vegetation structure in a fluorescence pixel, increases the challenge in retrieving and quantifying SIF. High spatial resolution Sentinel-2 (S2) data (20 m) can be used to better characterize the intrapixel heterogeneity of SIF and potentially extend the application of satellite-derived SIF to heterogeneous areas. In the context of the COST Action Optical synergies for spatiotemporal SENsing of Scalable ECOphysiological traits (SENSECO), in which this study was conducted, we proposed direct (i.e., spatial heterogeneity coefficient, standard deviation, normalized entropy, ensemble decision trees) and patch mosaic (i.e., local Moran’s I) approaches to characterize the spatial heterogeneity of SIF collected at 760 and 687 nm (SIF760 and SIF687, respectively) and to correlate it with the spatial heterogeneity of selected S2 derivatives. We used HyPlant airborne imagery acquired over an agricultural area in Braccagni (Italy) to emulate S2-like top-of-the-canopy reflectance and SIF imagery at different spatial resolutions (i.e., 300, 20, and 5 m). The ensemble decision trees method characterized FLEX intrapixel heterogeneity best (R2 > 0.9 for all predictors with respect to SIF760 and SIF687). Nevertheless, the standard deviation and spatial heterogeneity coefficient using k-means clustering scene classification also provided acceptable results. In particular, the near-infrared reflectance of terrestrial vegetation (NIRv) index accounted for most of the spatial heterogeneity of SIF760 in all applied methods (R2 = 0.76 with the standard deviation method; R2 = 0.63 with the spatial heterogeneity coefficient method using a scene classification map with 15 classes). The models developed for SIF687 did not perform as well as those for SIF760, possibly due to the uncertainties in fluorescence retrieval at 687 nm and the low signal-to-noise ratio in the red spectral region. Our study shows the potential of the proposed methods to be implemented as part of the FLEX ground segment processing chain to quantify the intrapixel heterogeneity of a FLEX pixel and/or as a quality flag to determine the reliability of the retrieved fluorescence