Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe

A simplified approach to satellite-based monitoring system of sugarcane plantation to manage yield decline at Wonji-Shoa Sugar Estate, central Ethiopia

Drastic and continuous decline in cane yields has become a major threat to sustainable sugarcane production in Ethiopia. Among the causes for the decline are the inefficient and ineffective system of monitoring sugarcane plantations. Adopting satellite-based crop monitoring through the Landviewer platform may circumvent this problem. However, the reliability of vegetation indexes calculated by the platform is unknown and thus requires evaluation. Accordingly, we tested the accuracy of selected Landviewer Calculated Vegetation Indexes (LCVIs) on three major sugarcane varieties and two cropping types. The goodness-of-fit of the sigmoid curve to the LCVIs profile of sugarcane was evaluated. The correlations between LCVIs and yield components, LCVIs and fractional green canopy cover (FGCC), as well as the time-serious Normalized Difference Vegetation Index (NDVI) and yields, were also analysed. We found that the goodness-of-fit of the sigmoid curve was significant (p < 0.001), with 84%-95% accuracy in all the indexes. The majority of LCVIs showed significant (p < 0.05) relationships with yield components and FGCC. The time-series NDVI also demonstrated a significant relationship with cane yield (R2 = 0.73-0.85) at the age of 10 months and above. The accuracy level of LCVIs varies with varieties and crop types, but the Normalized Difference Phenology Index (NDPI), Soil Adjusted Vegetation Index (SAVI), and NDVI were identified as the most consistent and effective LCVIs for sugarcane monitoring. Therefore, the accuracy of LCVIs was dependable and can be used effectively in monitoring sugarcane plantations to tackle the problem of continuous decline in the yield of the crop.

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

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 (R 2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.

Estimating Evapotranspiration over Heterogeneous Surface with Sentinel-2 and Sentinel-3 Data: A Case Study in Heihe River Basin

Evapotranspiration (ET) is an important part of surface–atmosphere interactions, connecting the transfer of matter and energy. Land surface heterogeneity is a natural attribute of the Earth’s surface and is an inevitable problem in calculating ET with coarse resolution remote sensing data, which results in significant error in the ET estimation. This study aims to explore the effect and applicability of the evaporative fraction and area fraction (EFAF) method for correcting 1 km coarse resolution ET. In this study we use the input parameter upscaling (IPUS) algorithm to estimate energy fluxes and the EFAF method to correct ET estimates. Five ground stations in the midstream and downstream regions of the Heihe River Basin (HRB) were used to validate the latent heat flux (LE) calculated by the IPUS algorithm and EFAF method. The evaluation results show that the performance of the EFAF method is superior to that of the IPUS algorithm, with the coefficient of determination (R2) increasing, the root mean square error (RMSE) decreasing, and the mean bias error (MBE) decreasing by 17 W/m2 on average. In general, the EFAF method is suitable for correcting the deviation in LE estimated based on Sentinel data caused by land surface heterogeneity and can be applied to obtain accurate estimates of ET.

The Swiss data cube, analysis ready data archive using earth observations of Switzerland

Since the opening of Earth Observation (EO) archives (USGS/NASA Landsat and EC/ESA Sentinels), large collections of EO data are freely available, offering scientists new possibilities to better understand and quantify environmental changes. Fully exploiting these satellite EO data will require new approaches for their acquisition, management, distribution, and analysis. Given rapid environmental changes and the emergence of big data, innovative solutions are needed to support policy frameworks and related actions toward sustainable development. Here we present the Swiss Data Cube (SDC), unleashing the information power of Big Earth Data for monitoring the environment, providing Analysis Ready Data over the geographic extent of Switzerland since 1984, which is updated on a daily basis. Based on a cloud-computing platform allowing to access, visualize and analyse optical (Sentinel-2; Landsat 5, 7, 8) and radar (Sentinel-1) imagery, the SDC minimizes the time and knowledge required for environmental analyses, by offering consistent calibrated and spatially co-registered satellite observations. SDC derived analysis ready data supports generation of environmental information, allowing to inform a variety of environmental policies with unprecedented timeliness and quality.

Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery

Global food security is critical to eliminating hunger and malnutrition. In the changing climate, farmers in developing countries must adopt technologies and farming practices such as precision agriculture (PA). PA-based approaches enable farmers to cope with frequent and intensified droughts and heatwaves, optimising yields, increasing efficiencies, and reducing operational costs. Biophysical parameters such as Leaf Area Index (LAI), Leaf Chlorophyll Content (LCab), and Canopy Chlorophyll Content (CCC) are essential for characterising field-level spatial variability and thus are necessary for enabling variable rate application technologies, precision irrigation, and crop monitoring. Moreover, robust machine learning algorithms offer prospects for improving the estimation of biophysical parameters due to their capability to deal with non-linear data, small samples, and noisy variables. This study compared the predictive performance of sparse Partial Least Squares (sPLS), Random Forest (RF), and Gradient Boosting Machines (GBM) for estimating LAI, LCab, and CCC with Sentinel-2 imagery in Bothaville, South Africa and identified, using variable importance measures, the most influential bands for estimating crop biophysical parameters. The results showed that RF was superior in estimating all three biophysical parameters, followed by GBM which was better in estimating LAI and CCC, but not LCab, where sPLS was relatively better. Since all biophysical parameters could be achieved with RF, it can be considered a good contender for operationalisation. Overall, the findings in this study are significant for future biophysical product development using RF to reduce reliance on many algorithms for specific parameters, thus facilitating the rapid extraction of actionable information to support PA and crop monitoring activities.

Regional suitability prediction of soil salinization based on remote-sensing derivatives and optimal spectral index

Soil salinization is an extremely serious land degradation problem in arid and semi-arid regions that hinders the sustainable development of agriculture and food security. Information and research on soil salinity using remote sensing (RS) technology provide a quick and accurate assessment and solutions to address this problem. This study aims to compare the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction and exploration of the potential application of derivatives to RS prediction of salinized soils. It explores the ability of derivatives to be used in the Landsat-8 OLI and Sentinel-2A MSI multispectral data, and it was used as a data source as well as to address the adaptability of salinity prediction on a regional scale. The two-dimensional (2D) and three-dimensional (3D) optimal spectral indices are used to screen the bands that are most sensitive to soil salinity (0-10 cm), and RS data and topographic factors are combined with machine learning to construct a comprehensive soil salinity estimation model based on gray correlation analysis. The results are as follows: (1) The optimal spectral index (2D, 3D) can effectively consider possible combinations of the bands between the interaction effects and responding to sensitive bands of soil properties to circumvent the problem of applicability of spectral indices in different regions; (2) Both the Landsat-8 OLI and Sentinel-2A MSI multispectral RS data sources, after the first-order derivative techniques are all processed, show improvements in the prediction accuracy of the model; (3) The best performance/accuracy of the predictive model is for sentinel data under first-order derivatives. This study compared the capabilities of Landsat-8 OLI and Sentinel-2A MSI in RS prediction in finding the potential application of derivatives to RS prediction of salinized soils, with the results providing some theoretical basis and technical guidance for salinized soil prediction and environmental management planning.

Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

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

Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is leaf area index (LAI) if algorithms account for variability in atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopy-atmosphere radiative transfer models PROSAIL-6SV were used to simulate a look-up table (LUT) of TOA radiance data and associated input variables. This LUT was then used to train the Bayesian machine learning algorithms Gaussian processes regression (GPR) and variational heteroscedastic GPR (VHGPR). PROSAIL simulations were also used to train GPR and VHGPR models for LAI retrieval from S2 images at bottom-of-atmosphere (BOA) level (L2A product) for comparison purposes. The BOA and TOA LAI products were consistently validated against a field dataset with GPR (R2 of 0.78) and with VHGPR (R 2 of 0.80) and for both cases a slightly lower RMSE for the TOA LAI product (about 10% reduction). Because of delivering superior accuracies and lower uncertainties, the VHGPR models were further applied for LAI mapping using S2 acquisitions over the agricultural sites Marchfeld (Austria) and Barrax (Spain). The models led to consistent LAI maps at BOA and TOA scale. The LAI maps were also compared against LAI maps as generated by the SNAP toolbox, which is based on a neural network (NN). Maps were again consistent, however the SNAP NN model tends to overestimate over dense vegetation cover. Overall, this study demonstrated that hybrid LAI retrieval algorithms can be developed from TOA radiance data given a cloud-free sky, thus without the need of atmospheric correction. To the benefit of the community, the development of such hybrid models for the retrieval vegetation properties from BOA or TOA images has been streamlined in the freely downloadable ALG-ARTMO software framework.

Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery

Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.

Assessment of Green Infrastructure in Riparian Zones Using Copernicus Programme

This article presents an approach to identify Green Infrastructure (GI), its benefits and condition. This information enables environmental agencies to prioritise conservation, management and restoration strategies accordingly. The study focuses on riparian areas due to their potential to supply Ecosystem Services (ES), such as water quality, biodiversity, soil protection and flood or drought risk reduction. Natural Water Retention Measures (NWRM) related to agriculture and forestry are the type of GI considered specifically within these riparian areas. The approach is based on ES condition indicators, defined by the European Environment Agency (EEA) to support the policy targets of the 2020 Biodiversity Strategy. Indicators that can be assessed through remote sensing techniques are used, namely: capacity to provide ecosystem services, proximity to protected areas, greening response and water stress. Specifically, the approach uses and evaluates the potential of freely available products from the Copernicus Land Monitoring Service (CLMS) to monitor GI. Moreover, vegetation and water indices are calculated using data from the Sentinel-2 MSI Level-2A scenes and integrated in the analysis. The approach has been tested in the Italian Po river basin in 2018. Firstly, agriculture and forest NWRM were identified in the riparian areas of the river network. Secondly, the Riparian Zones products from the CLMS local component and the satellite-based indices were linked to the aforementioned ES condition indicators. This led to the development of a pixel-based model that evaluates the identified GI according to: (i) its disposition to provide riparian regulative ES and (ii) its condition in the analysed year. Finally, the model was used to prioritise GI for conservation or restoration initiatives, based on its potential to deliver ES and current condition.

Retrieval of Evapotranspiration from Sentinel-2: Comparison of Vegetation Indices, Semi-Empirical Models and SNAP Biophysical Processor Approach

Remote sensing evapotranspiration estimation over agricultural areas is increasingly used for irrigation management during the crop growing cycle. Different methodologies based on remote sensing have emerged for the leaf area index (LAI) and the canopy chlorophyll content (CCC) estimation, essential biophysical parameters for crop evapotranspiration monitoring. Using Sentinel-2 (S2) spectral information, this study performed a comparative analysis of empirical (vegetation indices), semi-empirical (CLAIR model with fixed and calibrated extinction coefficient) and artificial neural network S2 products derived from the Sentinel Application Platform Software (SNAP) biophysical processor (ANN S2 products) approaches for the estimation of LAI and CCC. Four independent in situ collected datasets of LAI and CCC, obtained with standard instruments (LAI-2000, SPAD) and a smartphone application (PocketLAI), were used. The ANN S2 products present good statistics for LAI (R2 > 0.70, root mean square error (RMSE) < 0.86) and CCC (R2 > 0.75, RMSE < 0.68 g/m2) retrievals. The normalized Sentinel-2 LAI index (SeLI) is the index that presents good statistics in each dataset (R2 > 0.71, RMSE < 0.78) and for the CCC, the ratio red-edge chlorophyll index (CIred-edge) (R2 > 0.67, RMSE < 0.62 g/m2). Both indices use bands located in the red-edge zone, highlighting the importance of this region. The LAI CLAIR model with a fixed extinction coefficient value produces a R2 > 0.63 and a RMSE < 1.47 and calibrating this coefficient for each study area only improves the statistics in two areas (RMSE ≈ 0.70). Finally, this study analyzed the influence of the LAI parameter estimated with the different methodologies in the calculation of crop potential evapotranspiration (ETc) with the adapted Penman–Monteith (FAO-56 PM), using a multi-temporal dataset. The results were compared with ETc estimated as the product of the reference evapotranspiration (ETo) and on the crop coefficient (Kc) derived from FAO table values. In the absence of independent reference ET data, the estimated ETc with the LAI in situ values were considered as the proxy of the ground-truth. ETc estimated with the ANN S2 LAI product is the closest to the ETc values calculated with the LAI in situ (R2 > 0.90, RMSE < 0.41 mm/d). Our findings indicate the good validation of ANN S2 LAI and CCC products and their further suitability for the implementation in evapotranspiration retrieval of agricultural areas.

Validation of ESA Sentinel-2 L2A Aerosol Optical Thickness and Columnar Water Vapour during 2017–2018

This study presents a validation of aerosol optical thickness (AOT) and integrated water vapour (IWV) products provided by the European Space Agency (ESA) from multi-spectral imager (MSI) measurements on board the Sentinel-2 satellite (ESA-L2A). For that purpose, data from 94 Aerosol Robotic Network (AERONET) stations over Europe and adjacent regions, covering a wide geographical region with a variety of climate and environmental conditions and during the period between March 2017 and December 2018 have been used. The comparison between ESA-L2A and AERONET shows a better agreement for IWV than the AOT, with normalized root mean square errors (NRMSE) of 5.33% and 9.04%, respectively. This conclusion is also reflected in the values of R2, which are 0.99 and 0.65 for IWV and AOT, respectively. The study period was divided into two sub-periods, before and after 15 January 2018, when the Sentinel-2A spectral response functions of bands 1 and 2 (centered at 443 and 492 nm) were updated by ESA, in order to investigate if the lack of agreement in the AOT values was connected to the use of incorrect spectral response functions. The comparison of ESA-L2A AOT with AERONET measurements showed a better agreement for the second sub-period, with root mean square error (RMSE) values of 0.08 in comparison with 0.14 in the first sub-period. This same conclusion was attained considering mean bias error (MBE) values that decreased from 0.09 to 0.01. The ESA-L2A AOT values estimated with the new spectral response functions were closer to the correspondent reference AERONET values than the ones obtained using the previous spectral response functions. IWV was not affected by this change since the retrieval algorithm does not use bands 1 and 2 of Sentinel-2. Additionally, an analysis of potential uncertainty sources to several factors affecting the AOT comparison is presented and recommendations regarding the use of ESA-L2A AOT dataset are given.

Evaluation of Vegetation Biophysical Variables Time Series Derived from Synthetic Sentinel-2 Images

Time series of vegetation biophysical variables (leaf area index (LAI), fraction canopy cover (FCOVER), fraction of absorbed photosynthetically active radiation (FAPAR), canopy chlorophyll content (CCC), and canopy water content (CWC)) were estimated from interpolated Sentinel-2 (S2-LIKE) surface reflectance images, for an agricultural region located in central Canada, using the Simplified Level 2 Product Prototype Processor (SL2P). S2-LIKE surface reflectance data were generated by blending clear-sky Sentinel-2 Multispectral Imager (S2-MSI) images with daily BRDF-adjusted Moderate Resolution Imaging Spectrometer images using the Prediction Smooth Reflectance Fusion Model (PSFRM), and validated using thirteen independent S2-MSI images (RMSE ≤ 6%). The uncertainty of S2-LIKE surface reflectance data increases with the time delay between the prediction date and the closest S2-MSI image used for training PSFRM. Vegetation biophysical variables from S2-LIKE products are validated qualitatively and quantitatively by comparison to the corresponding vegetation biophysical variables from S2-MSI products (RMSE = 0.55 for LAI, ~10% for FCOVER and FAPAR, and 0.13 g/m2 for CCC and 0.16 kg/m2 for CWC). Uncertainties of vegetation biophysical variables derived from S2-LIKE products are almost linearly related to the uncertainty of the input reflectance data. When compared to the in situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 field campaign, uncertainties of LAI (0.83) and FCOVER (13.73%) estimates from S2-LIKE products were slightly larger than uncertainties of LAI (0.57) and FCOVER (11.80%) estimates from S2-MSI products. However, equal uncertainties (0.32 kg/m2) were obtained for CWC estimates using SL2P with either S2-LIKE or S2-MSI input data.

Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)

The spatial quantification of green leaf area index (LAIgreen), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAIgreen index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAIgreen measurements were used. Commonly used LAIgreen indices applied on the Sentinel-2 10 m × 10 m pixel resulted in a validation R² lower than 0.6. By calculating all Sentinel-2 band combinations to identify high correlation and physical basis with LAIgreen, the new Sentinel-2 LAIgreen Index (SeLI) was defined. SeLI is a normalized index that uses the 705 nm and 865 nm centered bands, exploiting the red-edge region for low-saturating absorption sensitivity to photosynthetic vegetation. A R² of 0.708 (root mean squared error (RMSE) = 0.67) and a R² of 0.732 (RMSE = 0.69) were obtained with a linear fitting for the calibration and validation datasets, respectively, outperforming established indices. Sentinel-2 LAIgreen maps are presented.