Remote Sensing

2024

Jump to: 2023, 2022, 2021, 2020, 2019, 2018

23 pages, 9448 KiB

Open AccessArticle

Monitoring Biophysical Variables (FVC, LAI, LC_ab, and CWC) and Cropland Dynamics at Field Scale Using Sentinel-2 Time Series

by Reza Hassanpour, Abolfazl Majnooni-Heris, Ahmad Fakheri Fard and Jochem Verrelst

Remote Sens. 2024, 16(13), 2284; https://doi.org/10.3390/rs16132284 - 22 Jun 2024

Viewed by 1178

Abstract

Biophysical variables play a crucial role in understanding phenological stages and crop dynamics, optimizing ultimate agricultural practices, and achieving sustainable crop yields. This study examined the effectiveness of the Sentinel-2 Biophysical Processor (S2BP) in accurately estimating crop dynamics descriptors, including fractional vegetation cover [...] Read more.

Biophysical variables play a crucial role in understanding phenological stages and crop dynamics, optimizing ultimate agricultural practices, and achieving sustainable crop yields. This study examined the effectiveness of the Sentinel-2 Biophysical Processor (S2BP) in accurately estimating crop dynamics descriptors, including fractional vegetation cover (FVC), leaf area index (LAI), leaf chlorophyll a and b (LC_ab), and canopy water content (CWC). The evaluation was conducted using estimation quality indicators (EQIs) and comprehensive ground throughout the entire growing season at the field scale. To identify soil and vegetation pixels, the spectral unmixing technique was employed. According to the EQIs, the best retrievals were obtained for FVC in around 99.9% of the 23,976 pixels that were analyzed during the growth season. For LAI, LC_ab, and CWC, over 60% of the examined pixels had inputs that were out-of-range. Furthermore, in over 35% of the pixels, the output values for LC_ab and CWC were out-of-range. The FVC, LAI, and LC_ab estimates agreed well with ground measurements (R² = 0.62–0.85), whereas a discrepancy was observed for CWC estimates when compared with ground measurements (R² = 0.51). Furthermore, the uncertainties of FVC, LAI, LC_ab, and CWC estimates were 0.09, 0.81 m²/m², 60.85 µg/cm², and 0.02 g/cm² through comparisons to ground FVC, LAI, C_ab, and CWC measurements, respectively. Considering EQIs and uncertainty metrics, the order of the estimation accuracy of the four variables was FVC > LAI > LC_ab > CWC. Our analysis revealed that temporal variations of FVC, LAI, and LC_ab were primarily driven by field-scale events like sowing date, growing period, and harvesting time, highlighting their sensitivity to agricultural practices. The robustness of S2BP results could be enhanced by implementing a pixel identification algorithm, like embedding spectral unmixing. Overall, this study provides detailed, pixel-by-pixel insights into the performance of S2BP in estimating FVC, LAI, LC_ab, and CWC, which are crucial for monitoring crop dynamics in precision agriculture. Full article

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23 pages, 15718 KiB

Open AccessArticle

Sentinel-2 Reference Fire Perimeters for the Assessment of Burned Area Products over Latin America and the Caribbean for the Year 2019

by Jon Gonzalez-Ibarzabal, Magí Franquesa, Armando Rodriguez-Montellano and Aitor Bastarrika

Remote Sens. 2024, 16(7), 1166; https://doi.org/10.3390/rs16071166 - 27 Mar 2024

Cited by 2 | Viewed by 1439

Abstract

The increasing availability of products generating burned area (BA) maps in recent years necessitates the creation of more accurate reference perimeters to validate these products and provide users with information about their accuracy. For this purpose, reference perimeters were created using Sentinel-2 images [...] Read more.

The increasing availability of products generating burned area (BA) maps in recent years necessitates the creation of more accurate reference perimeters to validate these products and provide users with information about their accuracy. For this purpose, reference perimeters were created using Sentinel-2 images in Latin America and the Caribbean (LAC) for the year 2019. The sampling was adapted to the peculiarities of the Sentinel-2 tiling grid system, and statistically representative sample units were selected for biomes and fire activity through stratified random sampling. Fire perimeters were extracted using a Random Forest supervised classification and results were manually supervised and refined. Efforts were made to maximize the temporal length covered by the reference perimeters for each sample, aiming to minimize temporal errors when using the perimeters for validation. The dataset covers 569,214.2

{k m}^{2}

(3.5% burned, 88.7% unburned, and 7.8% unobserved). These perimeters were compared with higher spatial resolution PlanetScope-derived perimeters, resulting in 8.4% commission errors and 3.8% omission errors. As a validation exercise, MCD64A1 and FireCCI51 global burned area products were validated using the Sentinel-2 reference dataset created, confirming that the temporal extent of the reference perimeters significantly affects the validation of such products. The reference fire perimeters are publicly available in the Burned Area Reference Database (BARD). Full article

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2023

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18 pages, 3855 KiB

Open AccessArticle

Optimal Sample Size and Composition for Crop Classification with Sen2-Agri’s Random Forest Classifier

by Urs Schulthess, Francelino Rodrigues, Matthieu Taymans, Nicolas Bellemans, Sophie Bontemps, Ivan Ortiz-Monasterio, Bruno Gérard and Pierre Defourny

Remote Sens. 2023, 15(3), 608; https://doi.org/10.3390/rs15030608 - 19 Jan 2023

Cited by 1 | Viewed by 2938

Abstract

Sen2-Agri is a software system that was developed to facilitate the use of multi-temporal satellite data for crop classification with a random forest (RF) classifier in an operational setting. It automatically ingests and processes Sentinel-2 and LandSat 8 images. Our goal was to [...] Read more.

Sen2-Agri is a software system that was developed to facilitate the use of multi-temporal satellite data for crop classification with a random forest (RF) classifier in an operational setting. It automatically ingests and processes Sentinel-2 and LandSat 8 images. Our goal was to provide practitioners with recommendations for the best sample size and composition. The study area was located in the Yaqui Valley in Mexico. Using polygons of more than 6000 labeled crop fields, we prepared data sets for training, in which the nine crops had an equal or proportional representation, called Equal or Ratio, respectively. Increasing the size of the training set improved the overall accuracy (OA). Gains became marginal once the total number of fields approximated 500 or 40 to 45 fields per crop type. Equal achieved slightly higher OAs than Ratio for a given number of fields. However, recall and F-scores of the individual crops tended to be higher for Ratio than for Equal. The high number of wheat fields in the Ratio scenarios, ranging from 275 to 2128, produced a more accurate classification of wheat than the maximal 80 fields of Equal. This resulted in a higher recall for wheat in the Ratio than in the Equal scenarios, which in turn limited the errors of commission of the non-wheat crops. Thus, a proportional representation of the crops in the training data is preferable and yields better accuracies, even for the minority crops. Full article

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2022

Jump to: 2024, 2023, 2021, 2020, 2019, 2018

21 pages, 3216 KiB

Open AccessArticle

Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America

by Alexander A. Howe, Sean A. Parks, Brian J. Harvey, Saba J. Saberi, James A. Lutz and Larissa L. Yocom

Remote Sens. 2022, 14(20), 5249; https://doi.org/10.3390/rs14205249 - 20 Oct 2022

Cited by 17 | Viewed by 4563

Abstract

Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 [...] Read more.

Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel’s finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems. Full article

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19 pages, 2864 KiB

Open AccessArticle

A First Approach to Determine If It Is Possible to Delineate In-Season N Fertilization Maps for Wheat Using NDVI Derived from Sentinel-2

by Asier Uribeetxebarria, Ander Castellón and Ana Aizpurua

Remote Sens. 2022, 14(12), 2872; https://doi.org/10.3390/rs14122872 - 15 Jun 2022

Cited by 9 | Viewed by 2884

Abstract

Adjusting nitrogen fertilization to the nutritional requirements of crops is one of the major challenges of modern agriculture. The amount of N needed is mainly determined by crop yield, so yield maps can be used to optimize N fertilization. As the adoption of [...] Read more.

Adjusting nitrogen fertilization to the nutritional requirements of crops is one of the major challenges of modern agriculture. The amount of N needed is mainly determined by crop yield, so yield maps can be used to optimize N fertilization. As the adoption of yield monitors is low among farmers, implementation of this approach is still low. However, as the Normalized Difference Vegetation Index (NDVI) is related to grain yield, the main objective of this work was to identify at which wheat growth stage a moderate agreement between NDVI and yield is obtained. For this, NDVI images obtained from Sentinel-2 were used, and the evolution of concordance was analyzed in 13 classified parcels of wheat employing the Kappa index (KI). In one-third of the plots, a moderate agreement (KI > 0.4) was reached before the stem elongation growth phase (when the last N application was made). In another one-third, moderate agreement was reached later, in more advanced development stages. For the cases in which this agreement did not exist, an attempt was made to find the causes. The MANOVA and subsequent descriptive discriminant analysis (DDA) showed that the NDVI dates that contribute the most to the differentiation between plots with and without agreement between grain yield maps and NDVI images were those corresponding to tillering. The sum of the NDVI values of the tillering phase was significantly lower in the group of plots that did not show concordance. Sentinel-2 imagery was successful on 66% of plots for delineation of management zones after GS 30, and thus is useful for producing fertilization maps for the upcoming season. However, to produce in-season fertilization maps, further studies are needed to better understand the mechanisms that regulate the relation between yield and NDVI at early growth stages (<GS 30). Full article

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27 pages, 11200 KiB

Open AccessFeature PaperArticle

Detection of Grassland Mowing Events for Germany by Combining Sentinel-1 and Sentinel-2 Time Series

by Sophie Reinermann, Ursula Gessner, Sarah Asam, Tobias Ullmann, Anne Schucknecht and Claudia Kuenzer

Remote Sens. 2022, 14(7), 1647; https://doi.org/10.3390/rs14071647 - 30 Mar 2022

Cited by 22 | Viewed by 4369

Abstract

Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend [...] Read more.

Grasslands cover one-third of the agricultural area in Germany and play an important economic role by providing fodder for livestock. In addition, they fulfill important ecosystem services, such as carbon storage, water purification, and the provision of habitats. These ecosystem services usually depend on the grassland management. In central Europe, grasslands are grazed and/or mown, whereby the management type and intensity vary in space and time. Spatial information on the mowing timing and frequency on larger scales are usually not available but would be required in order to assess the ecosystem services, species composition, and grassland yields. Time series of high-resolution satellite remote sensing data can be used to analyze the temporal and spatial dynamics of grasslands. Within this study, we aim to overcome the drawbacks identified by previous studies, such as optical data availability and the lack of comprehensive reference data, by testing the time series of various Sentinel-2 (S2) and Sentinal-1 (S1) parameters and combinations of them in order to detect mowing events in Germany in 2019. We developed a threshold-based algorithm by using information from a comprehensive reference dataset of heterogeneously managed grassland parcels in Germany, obtained by RGB cameras. The developed approach using the enhanced vegetation index (EVI) derived from S2 led to a successful mowing event detection in Germany (60.3% of mowing events detected, F1-Score = 0.64). However, events shortly before, during, or shortly after cloud gaps were missed and in regions with lower S2 orbit coverage fewer mowing events were detected. Therefore, S1-based backscatter, InSAR, and PolSAR features were investigated during S2 data gaps. From these, the PolSAR entropy detected mowing events most reliably. For a focus region, we tested an integrated approach by combining S2 and S1 parameters. This approach detected additional mowing events, but also led to many false positive events, resulting in a reduction in the F1-Score (from 0.65 of S2 to 0.61 of S2 + S1 for the focus region). According to our analysis, a majority of grasslands in Germany are only mown zero to two times (around 84%) and are probably additionally used for grazing. A small proportion is mown more often than four times (3%). Regions with a generally higher grassland mowing frequency are located in southern, south-eastern, and northern Germany. Full article

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2021

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27 pages, 25782 KiB

Open AccessEditor’s ChoiceArticle

Understanding the Requirements for Surveys to Support Satellite-Based Crop Type Mapping: Evidence from Sub-Saharan Africa

by George Azzari, Shruti Jain, Graham Jeffries, Talip Kilic and Siobhan Murray

Remote Sens. 2021, 13(23), 4749; https://doi.org/10.3390/rs13234749 - 23 Nov 2021

Cited by 9 | Viewed by 6194

Abstract

This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced [...] Read more.

This paper provides recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. The analysis focuses on maize cultivation in Malawi and Ethiopia, and leverages rich, georeferenced plot-level data from national household surveys that were conducted in 2018–20 and integrated with Sentinel-2 satellite imagery and complementary geospatial data. To identify the approach to survey data collection that yields optimal data for training remote sensing models, 26,250 in silico experiments are simulated within a machine learning framework. The best model is then applied to map seasonal maize cultivation from 2016 to 2019 at 10-m resolution in both countries. The analysis reveals that smallholder plots with maize cultivation can be identified with up to 75% accuracy. Collecting full plot boundaries or complete plot corner points provides the best quality of information for model training. Classification performance peaks with slightly less than 60% of the training data. Seemingly little erosion in accuracy under less preferable approaches to georeferencing plots results in the total area under maize cultivation being overestimated by 0.16–0.47 million hectares (8–24%) in Malawi. Full article

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21 pages, 4425 KiB

Open AccessArticle

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

by Mahlatse Kganyago, Paidamwoyo Mhangara and Clement Adjorlolo

Remote Sens. 2021, 13(21), 4314; https://doi.org/10.3390/rs13214314 - 27 Oct 2021

Cited by 27 | Viewed by 5274

Abstract

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 [...] Read more.

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 (LC_ab), 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, LC_ab, 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 LC_ab, 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. Full article

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29 pages, 27066 KiB

Open AccessArticle

Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations

by Jiří Tomíček, Jan Mišurec and Petr Lukeš

Remote Sens. 2021, 13(18), 3659; https://doi.org/10.3390/rs13183659 - 13 Sep 2021

Cited by 6 | Viewed by 2976

Abstract

In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (Triticum [...] Read more.

In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (Triticum aestivum), spring barley (Hordeum vulgare), winter rapeseed (Brassica napus subsp. napus), alfalfa (Medicago sativa), sugar beet (Beta vulgaris), and corn (Zea mays subsp. Mays) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PROSAIL simulations to retrieve the biophysical properties tailored for each crop type. Crop-specific PROSAIL model optimization and validation were based upon a large dataset of in situ measurements collected in 2017 and 2018 in lowland of Central Bohemia region. For LCC and LAI, respectively, low relative root mean square error (rRMSE; 25%, 37%) was achieved. Additionally, a relatively strong correlation with in situ measurements (r = 0.80) was obtained for LAI. On the contrary, the results of the LWC parameter retrieval proved to be unsatisfactory. We have developed a generic tool for biophysical monitoring of agricultural crops based on the interpretation of Sentinel-2 satellite data by inversion of the radiation transfer model. The resulting crop condition maps can serve as precision agriculture inputs for selective fertilizer and irrigation application as well as for yield potential assessment. Full article

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25 pages, 11179 KiB

Open AccessEditor’s ChoiceArticle

Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery

by Esther Shupel Ibrahim, Philippe Rufin, Leon Nill, Bahareh Kamali, Claas Nendel and Patrick Hostert

Remote Sens. 2021, 13(17), 3523; https://doi.org/10.3390/rs13173523 - 5 Sep 2021

Cited by 43 | Viewed by 8540

Abstract

Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and [...] Read more.

Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops—maize and potato—and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize–cereals and potato–maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize–legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services. Full article

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17 pages, 1562 KiB

Open AccessArticle

Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series

by Misganu Debella-Gilo and Arnt Kristian Gjertsen

Remote Sens. 2021, 13(2), 289; https://doi.org/10.3390/rs13020289 - 15 Jan 2021

Cited by 33 | Viewed by 6187

Abstract

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. [...] Read more.

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas. Full article

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2020

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19 pages, 5324 KiB

Open AccessArticle

Mapping the Forage Nitrogen-Phosphorus Ratio Based on Sentinel-2 MSI Data and a Random Forest Algorithm in an Alpine Grassland Ecosystem of the Tibetan Plateau

by Jinlong Gao, Jie Liu, Tiangang Liang, Mengjing Hou, Jing Ge, Qisheng Feng, Caixia Wu and Wenlong Li

Remote Sens. 2020, 12(18), 2929; https://doi.org/10.3390/rs12182929 - 10 Sep 2020

Cited by 13 | Viewed by 3821

Abstract

Nondestructive and accurate estimating of the forage nitrogen–phosphorus (N:P) ratio is conducive to the real-time diagnosis of nutrient limitation and the formulation of a management scheme during the growth and development of forage. New-generation high-resolution remote sensors equipped with strategic red-edge wavebands offer [...] Read more.

Nondestructive and accurate estimating of the forage nitrogen–phosphorus (N:P) ratio is conducive to the real-time diagnosis of nutrient limitation and the formulation of a management scheme during the growth and development of forage. New-generation high-resolution remote sensors equipped with strategic red-edge wavebands offer opportunities and challenges for estimating and mapping forage N:P ratio in support of the sustainable utilization of alpine grassland resources. This study aims to detect the forage N:P ratio as an ecological indicator of grassland nutrient content by employing Sentinel-2 multispectral instrument (MSI) data and a random forest (RF) algorithm. The results showed that the estimation accuracy (R²) of the forage N:P ratio model established by combining the optimized spectral bands and vegetation indices (VIs) is 0.49 and 0.59 in the vigorous growth period (July) and the senescing period (November) of forage, respectively. Moreover, Sentinel-2 MSI B9 and B12 bands contributed greatly to the estimation of the forage N:P ratio, and the VIs (RECI2) constructed by B5 and B8A bands performed well in the estimation of the forage N:P ratio. Overall, it is promising to map the spatial distribution of the forage N:P ratio in alpine grassland using Sentinel-2 MSI data at regional scales. This study will be potentially beneficial in implementing precise positioning of vegetation nutrient deficiency and scientific fertilization management of grassland. Full article

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19 pages, 5993 KiB

Open AccessArticle

Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations

by Eva Marino, Marta Yebra, Mariluz Guillén-Climent, Nur Algeet, José Luis Tomé, Javier Madrigal, Mercedes Guijarro and Carmen Hernando

Remote Sens. 2020, 12(14), 2251; https://doi.org/10.3390/rs12142251 - 14 Jul 2020

Cited by 33 | Viewed by 5911

Abstract

Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important [...] Read more.

Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important resources in field campaigns for LFMC monitoring, often focusing on indicator species to reduce sampling time and costs. This paper compares different remote sensing approaches to provide LFMC prediction of Cistus ladanifer, a fire-prone shrub species commonly found in Mediterranean areas and used by fire management services as an indicator species for wildfire risk assessment. Spectral indices (SI) were derived from satellite imagery of different spectral, spatial, and temporal resolution, including Sentinel-2 and two different reflectance products of the Moderate Resolution Imaging Spectrometer (MODIS); MCD43A4 and MOD09GA. The SI were used to calibrate empirical models for LFMC estimation using on ground field LFMC measurements from a monospecific shrubland area located in Madrid (Spain). The empirical models were fitted with different statistical methods: simple (LR) and multiple linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs). MCD43A4 images were also used to estimate LFMC from the inversion of radiative transfer models (RTM). Empirical model predictions and RTM simulations of LFMC were validated and compared using an independent sample of LFMC values observed in the field. Empirical models derived from MODIS products and Sentinel-2 data showed R² between estimated and observed LFMC from 0.72 to 0.75 and mean absolute errors ranging from 11% to 13%. GAMs outperformed regression methods in model calibration, but NLR had better results in model validation. LFMC derived from RTM simulations had a weaker correlation with field data (R² = 0.49) than the best empirical model fitted with MCD43A4 images (R² = 0.75). R² between observations and LFMC derived from RTM ranged from 0.56 to 0.85 when the validation was performed for each year independently. However, these values were still lower than the equivalent statistics using the empirical models (R² from 0.65 to 0.94) and the mean absolute errors per year for RTM were still high (ranging from 25% to 38%) compared to the empirical model (ranging 7% to 15%). Our results showed that spectral information derived from Sentinel-2 and different MODIS products provide valuable information for LFMC estimation in C. ladanifer shrubland. However, both empirical and RTM approaches tended to overestimate the lowest LFMC values, and therefore further work is needed to improve predictions, especially below the critical LFMC threshold used by fire management services to indicate higher flammability (<80%). Although lower extreme LFMC values are still difficult to estimate, the proposed empirical models may be useful to identify when the critical threshold for high fire risk has been reached with reasonable accuracy. This study demonstrates that remote sensing data is a promising source of information to derive reliable and cost-effective LFMC estimation models that can be used in operational wildfire risk systems. Full article

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21 pages, 3408 KiB

Open AccessArticle

Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring

by Blanka Vajsová, Dominique Fasbender, Csaba Wirnhardt, Slavko Lemajic and Wim Devos

Remote Sens. 2020, 12(14), 2195; https://doi.org/10.3390/rs12142195 - 9 Jul 2020

Cited by 26 | Viewed by 4366

Abstract

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a [...] Read more.

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators. Full article

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2019

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20 pages, 6883 KiB

Open AccessArticle

Winter Wheat Mapping Based on Sentinel-2 Data in Heterogeneous Planting Conditions

by Dongyan Zhang, Shengmei Fang, Bao She, Huihui Zhang, Ning Jin, Haoming Xia, Yuying Yang and Yang Ding

Remote Sens. 2019, 11(22), 2647; https://doi.org/10.3390/rs11222647 - 13 Nov 2019

Cited by 21 | Viewed by 4924

Abstract

Monitoring and mapping the spatial distribution of winter wheat accurately is important for crop management, damage assessment and yield prediction. In this study, northern and central Anhui province were selected as study areas, and Sentinel-2 imagery was employed to map winter wheat distribution [...] Read more.

Monitoring and mapping the spatial distribution of winter wheat accurately is important for crop management, damage assessment and yield prediction. In this study, northern and central Anhui province were selected as study areas, and Sentinel-2 imagery was employed to map winter wheat distribution and the results were verified with Planet imagery in the 2017–2018 growing season. The Sentinel-2 imagery at the heading stage was identified as the optimum period for winter wheat area extraction after analyzing the images from different growth stages using the Jeffries–Matusita distance method. Therefore, ten spectral bands, seven vegetation indices (VI), water index and building index generated from the image at the heading stage were used to classify winter wheat areas by a random forest (RF) algorithm. The result showed that the accuracy was from 93% to 97%, with a Kappa above 0.82 and a percentage error lower than 5% in northern Anhui, and an accuracy of about 80% with Kappa ranging from 0.70 to 0.78 and a percentage error of about 20% in central Anhui. Northern Anhui has a large planting scale of winter wheat and flat terrain while central Anhui grows relatively small winter wheat areas and a high degree of surface fragmentation, which makes the extraction effect in central Anhui inferior to that in northern Anhui. Further, an optimum subset data was obtained from VIs, water index, building index and spectral bands using an RF algorithm. The result of using the optimum subset data showed a high accuracy of classification with a great advantage in data volume and processing time. This study provides a perspective for winter wheat mapping under various climatic and complicated land surface conditions and is of great significance for crop monitoring and agricultural decision-making. Full article

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11 pages, 1367 KiB

Open AccessLetter

by Dominique Fasbender, Blanka Vajsová, Csaba Wirnhardt and Slavko Lemajic

Remote Sens. 2019, 11(13), 1527; https://doi.org/10.3390/rs11131527 - 27 Jun 2019

Cited by 2 | Viewed by 3363

Abstract

Emergence of new state-of-the-art technologies has enabled an unprecedented amount of high spatial resolution satellite data having great potential for exploitation of extracted time series for a vast range of applications. Despite the high temporal resolution of time series, the number of real [...] Read more.

Emergence of new state-of-the-art technologies has enabled an unprecedented amount of high spatial resolution satellite data having great potential for exploitation of extracted time series for a vast range of applications. Despite the high temporal resolution of time series, the number of real observations of optical data that can be utilized is reduced due to meteorological conditions (such as cloud or haze) prevailing at the time of acquisition. This fact has an effect on the density of the retrieved time series and subsequently on a number of coincidental observations when comparing the similarity of time series from two different data sources for which the simultaneous acquisition date is already scarce. Classical tools for assessing the similarity of such time series can prove to be difficult or even impossible because of a lack of simultaneous observations. In this paper, we propose a simple method in order to circumvent this scarcity issue. In the first step, we rely on an interpolation in order to produce artificial time series on the union of the original acquisition dates. Then, we extend the theory of the correlation coefficient (CC) estimator to these interpolated time series. After validation on synthetic data, this simple approach proved to be extremely efficient on a real case study where Sentinel-2 and PlanetScope NDVI time series on parcels in The Netherlands are compared. Indeed, compared to other methods, it reduced the number of undecided cases while also improving the power of the statistical test on the similarity between both types of time series and the precision of the estimated CC. Full article

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25 pages, 11056 KiB

Open AccessArticle

Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure

by Louis Baetens, Camille Desjardins and Olivier Hagolle

Remote Sens. 2019, 11(4), 433; https://doi.org/10.3390/rs11040433 - 20 Feb 2019

Cited by 183 | Viewed by 19475

Abstract

The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many [...] Read more.

The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations. The ALCD method was designed to minimize human operator time thanks to a manually-supervised active learning method. The trained classifier uses a combination of spectral and multi-temporal information as input features and produces fully-classified images. The ALCD method was validated using visual criteria, consistency checks, and compared to another manually-generated cloud masks, with an overall accuracy above 98%. ALCD was used to create 32 reference cloud masks, on 10 different sites, with different seasons and cloud cover types. These masks were used to validate the cloud and shadow masks produced by three Sentinel-2 Level 2A processors: MAJA, used by the French Space Agency (CNES) to deliver Level 2A products, Sen2Cor, used by the European Space Agency (ESA), and FMask, used by the United States Geological Survey (USGS). The results show that MAJA and FMask perform similarly, with an overall accuracy around 90% (91% for MAJA, 90% for FMask), while Sen2Cor’s overall accuracy is 84%. The reference cloud masks, as well as the ALCD software used to generate them are made available to the Sentinel-2 user community. Full article

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2018

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20 pages, 54149 KiB

Open AccessArticle

Assessment of Sentinel-2 MSI Spectral Band Reflectances for Estimating Fractional Vegetation Cover

by Bing Wang, Kun Jia, Shunlin Liang, Xianhong Xie, Xiangqin Wei, Xiang Zhao, Yunjun Yao and Xiaotong Zhang

Remote Sens. 2018, 10(12), 1927; https://doi.org/10.3390/rs10121927 - 30 Nov 2018

Cited by 61 | Viewed by 10659

Abstract

Fractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially [...] Read more.

Fractional vegetation cover (FVC) is an essential parameter for characterizing the land surface vegetation conditions and plays an important role in earth surface process simulations and global change studies. The Sentinel-2 missions carrying multi-spectral instrument (MSI) sensors with 13 multispectral bands are potentially useful for estimating FVC. However, the performance of these bands for FVC estimation is unclear. Therefore, the objective of this study was to assess the performance of Sentinel-2 MSI spectral band reflectances on FVC estimation. The samples, including the Sentinel-2 MSI canopy reflectances and corresponding FVC values, were simulated using the PROSPECT + SAIL radiative transfer model under different conditions, and random forest regression (RFR) method was then used to develop FVC estimation models and assess the performance of various band reflectances for FVC estimation. These models were finally evaluated using field survey data. The results indicate that the three most important bands of Sentinel-2 MSI data for FVC estimation are band 4 (Red), band 12 (SWIR2) and band 8a (NIR2). FVC estimation using these bands has a comparable accuracy (root mean square error (RMSE) = 0.085) with that using all bands (RMSE = 0.090). The results also demonstrate that band 12 had a better performance for FVC estimation than the green band (RMSE = 0.097). However, the newly added red-edge bands, with low scores in the RFR model, have little significance for improving FVC estimation accuracy compared with the Red, NIR2 and SWIR2 bands. Full article

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27 pages, 5307 KiB

Open AccessArticle

Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

by Dezhi Wang, Bo Wan, Penghua Qiu, Yanjun Su, Qinghua Guo, Run Wang, Fei Sun and Xincai Wu

Remote Sens. 2018, 10(9), 1468; https://doi.org/10.3390/rs10091468 - 14 Sep 2018

Cited by 153 | Viewed by 11670

Abstract

Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) [...] Read more.

Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species. Full article

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17 pages, 5863 KiB

Open AccessEditor’s ChoiceTechnical Note

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

by Najib Djamai and Richard Fernandes

Remote Sens. 2018, 10(6), 926; https://doi.org/10.3390/rs10060926 - 12 Jun 2018

Cited by 39 | Viewed by 13570

Abstract

Sentinel-2 is a constellation of two satellites launched by the European Space Agency (ESA), respectively on 23 June 2015 and 7 March 2017, to map geophysical parameters over land surfaces. ESA provides Level 2 bottom-of-atmosphere reflectance (BOA) products (ESA-L2A) for Europe, with plans [...] Read more.

Sentinel-2 is a constellation of two satellites launched by the European Space Agency (ESA), respectively on 23 June 2015 and 7 March 2017, to map geophysical parameters over land surfaces. ESA provides Level 2 bottom-of-atmosphere reflectance (BOA) products (ESA-L2A) for Europe, with plans for operational global coverage, as well as the Sen2Cor (S2C) offline processor. In this study, aerosol optical thickness (AOT), precipitable water vapour (WVP) and surface reflectance from ESA-L2A products are compared with S2C output when using identical input Level 1 radiance products. Additionally, AOT and WVP are validated against reference measurement. As ESA and S2C share the same input and atmospheric correction algorithm, it was hypothesized that they should show identical validation performance and that differences between products should be negligible in comparison to the uncertainty of retrieved geophysical parameters due to radiometric uncertainty alone. Validation and intercomparison was performed for five clear-sky growing season dates for each of three ESA-L2A tiles selected to span a range of vegetation and topography as well as to be close to the AERONET measurement site. Validation of S2C (ESA) products using AERONET site measurements indicated an overall root mean square error (RMSE) of 0.06 (0.07) and a bias of 0.05 (0.09) for AOT and 0.20 cm (0.22 cm) and the bias was −0.02 cm (−0.10 cm) for WVP. Intercomparison of S2C-L2A and ESA-L2A showed an overall agreement higher than 99% for scene classification (SCL) maps and negligible differences for WVP (RMSE under 0.09 and R² above 0.99). Larger disagreement was observed for aerosol optical thickness (AOT) (RMSE up to 0.04 and R² as low as 0.14). For BOA reflectance, disagreement between products depends on vegetation cover density, topography slope and spectral band. The largest differences were observed for red-edge and infrared bands in mountainous vegetated areas (RMSE up to 4.9% reflectance and R² as low as 0.53). These differences are of similar magnitude to the radiometric calibration requirements for the Sentinel 2 imager. The differences had minimal impact of commonly used vegetation indices (NDVI, NDWI, EVI), but application of the Sentinel Level 2 biophysical processor generally resulted in proportional differences in most derived vegetation parameters. It is recommended that the consistency of ESA and S2C products should be improved by the developers of the ESA and S2C processors. Full article

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22 pages, 12316 KiB

Open AccessArticle

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

by Wenyan Ge, Qiuming Cheng, Yunwei Tang, Linhai Jing and Chunsheng Gao

Remote Sens. 2018, 10(4), 638; https://doi.org/10.3390/rs10040638 - 20 Apr 2018

Cited by 77 | Viewed by 11340

Abstract

As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A [...] Read more.

As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A imager was utilized to assess its ability to perform lithological classification in the Shibanjing ophiolite complex in Inner Mongolia, China. Five conventional machine learning methods, including artificial neural network (ANN), k-nearest neighbor (k-NN), maximum likelihood classification (MLC), random forest classifier (RFC), and support vector machine (SVM), were compared in order to find an optimal classifier for lithological mapping. The experiment revealed that the MLC method offered the highest overall accuracy. After that, Sentinel-2A image was compared with common multispectral data ASTER and Landsat-8 OLI (operational land imager) for lithological mapping using the MLC method. The comparison results showed that the Sentinel-2A imagery yielded a classification accuracy of 74.5%, which was 2.5% and 5.08% higher than those of the ASTER and OLI imagery, respectively, indicating that Sentinel-2A imagery is adequate for lithological discrimination, due to its high spectral resolution in the VNIR to SWIR range. Moreover, different data combinations of Sentinel-2A + ASTER + DEM (digital elevation model) and OLI + ASTER + DEM data were tested on lithological mapping using the MLC method. The best mapping result was obtained from Sentinel-2A + ASTER + DEM dataset, demonstrating that OLI can be replaced by Sentinel-2A, which, when combined with ASTER, can achieve sufficient bandpasses for lithological classification. Full article

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15 pages, 2857 KiB

Open AccessArticle

Explaining Leaf Nitrogen Distribution in a Semi-Arid Environment Predicted on Sentinel-2 Imagery Using a Field Spectroscopy Derived Model

by Abel Ramoelo and Moses Azong Cho

Remote Sens. 2018, 10(2), 269; https://doi.org/10.3390/rs10020269 - 9 Feb 2018

Cited by 31 | Viewed by 6493

Abstract

Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as [...] Read more.

Leaf nitrogen concentration (leaf N, %) is an essential component for understanding biogeochemical cycling. Leaf N is a good indicator of grass or forage quality, which is important for understanding the movements and feeding patterns of herbivores. Leaf N can be used as input for rangeland carrying capacity and stocking rate models. The estimation of leaf N has been successful using hyperspectral and commercial high spatial resolution satellite data such as WorldView-2 and RapidEye. Empirical methods have been used successfully to estimate leaf N, on the basis that it correlates with leaf chlorophyll. As such, leaf N was estimated using red edge based indices. The new Sentinel-2 sensor has two red edge bands, is freely available, and could further improve the estimation of leaf N at a regional scale. The objective of this study is to develop red edge based Sentinel-2 models derived from an analytical spectral device (ASD) spectrometer to map and monitor leaf N using Sentinel-2 images. Field work for leaf N and ASD data were collected in 2014 (December) in and around Kruger National Park, South Africa. ASD data were resampled to the Sentinel-2 spectral configuration using the spectral response function. The Sentinel-2 data for various dates were acquired from the European Space Agency (ESA) portal. The Sentinel-2 atmospheric correction (Sen2Cor) process was implemented. Simple empirical regression was used to estimate leaf N. High leaf N prediction accuracy was achieved at the ASD level and the best model was inverted on Sentinel-2 images to explain leaf N distribution at a regional scale over time. The spatial distribution of leaf N is influenced by the underlying geological substrate, fire frequency and other environmental variables. This study is a demonstration of how ASD data can be used to calibrate Sentinel-2 for leaf N estimation and mapping. Full article

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