Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km(2): dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of similar to 15 g.kg(-1) and a range of 30 g.kg(-1) in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information

Emissivity of soil attributes via terrestrial and satellite sensors

A textura e o conteúdo do carbono orgânico (CO) influenciam na resposta espectral dos solos. O estudo desses atributos é de grande importância para a preservação e o manejo adequado da terra na busca de uma agricultura sustentável. O uso de sensores de laboratório e satélites tem se mostrado como uma ferramenta no auxílio para o estudo destes, porém a análise dos atributos do solo com esses sensores tem focado principalmente nas regiões do espectro eletromagnético do visível (Vis), infravermelho próximo (NIR) e infravermelho de ondas curtas (SWIR), com poucos estudos no infravermelho médio (MIR). O objetivo deste trabalho foi identificar o padrão espectral do solo com diferentes granulometrias (areia e argila) e teores de CO utilizando sensores de laboratório e satélite na região do MIR, especificamente na faixa do infravermelho termal (TIR). O estudo teve uma avaliação qualitativa e quantitativa da argila, CO e das frações de areia (fina e grossa). A área de estudo está localizada na região de Piracicaba, São Paulo, Brasil. Foram coletadas 150 amostras de solo a uma profundidade de 0-20 cm. A textura do solo foi determinada pelo método da pipeta e a porcentagem de CO via combustão seca. Dados espectrais em refletância e emissividade (ε) foram adquiridos com o sensor Fourier Transform Infrared (FT-IR) Alpha (Bruker optics Corporation). Uma imagem \"ASTER_05\" foi adquirida em 15 de julho de 2017 em valores de ε. As amostras foram separadas por classes texturais e o comportamento espectral no TIR foi descrito. Os dados obtidos pelo sensor de laboratório foram reamostrados para as bandas do sensor de satélite. O comportamento entre os espectros de ambos sensores foi semelhante e teve correlação significativa com os atributos estudados, principalmente para areia. Para os modelos de regressão por mínimos quadrados parciais (PLSR), foram utilizadas seis estratégias (MIR, MIR_ASTER, ASTER, Termal, Termal IDC e MIR IDC) que consistiram no uso de todas as bandas de sensores, ou pela seleção das mesmas que apresentaram as correlações mais significativas com cada um dos atributos. Os modelos apresentaram um bom desempenho na predição de todos os atributos usando o MIR inteiro. No TIR, o modelo para areia total e para as frações fina e grossa foi bom. No caso dos modelos criados com os dados do sensor ASTER, não foram tão promissores quanto os de laboratório. O uso de bandas específicas ajudou a estimar alguns atributos no MIR e no TIR, aumentando o desempenho preditivo melhorando a validação dos modelos. Portanto, a discriminação dos atributos do solo com sensores de satélite pode ser melhorada com a identificação de bandas específicas, como observado nos resultados com sensores de laboratório.Soil texture and organic carbon (OC) content influence its spectral response. The study of these attributes is relevant for the preservation and proper management of land in pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a useful tool for studying soil attributes, but their analysis with these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The objective of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range. This study had qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil. 150 soil samples were collected at a depth of 0-20 cm. Soil texture was determined by the pipette method and the percentage of OC via dry combustion. Reflectance and emissivity (ε) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image \"ASTER_05\" from July 15, 2017 was acquired with values of ε. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, Thermal, Thermal IDC and MIR IDC), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR, models for total sand content and for fine and coarse fractions were good. In the case of models created with ASTER sensor data, they were not as promising as those with laboratory data. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors

Emissivity of soil attributes via terrestrial and satellite sensors

A textura e o conteúdo do carbono orgânico (CO) influenciam na resposta espectral dos solos. O estudo desses atributos é de grande importância para a preservação e o manejo adequado da terra na busca de uma agricultura sustentável. O uso de sensores de laboratório e satélites tem se mostrado como uma ferramenta no auxílio para o estudo destes, porém a análise dos atributos do solo com esses sensores tem focado principalmente nas regiões do espectro eletromagnético do visível (Vis), infravermelho próximo (NIR) e infravermelho de ondas curtas (SWIR), com poucos estudos no infravermelho médio (MIR). O objetivo deste trabalho foi identificar o padrão espectral do solo com diferentes granulometrias (areia e argila) e teores de CO utilizando sensores de laboratório e satélite na região do MIR, especificamente na faixa do infravermelho termal (TIR). O estudo teve uma avaliação qualitativa e quantitativa da argila, CO e das frações de areia (fina e grossa). A área de estudo está localizada na região de Piracicaba, São Paulo, Brasil. Foram coletadas 150 amostras de solo a uma profundidade de 0-20 cm. A textura do solo foi determinada pelo método da pipeta e a porcentagem de CO via combustão seca. Dados espectrais em refletância e emissividade (ε) foram adquiridos com o sensor Fourier Transform Infrared (FT-IR) Alpha (Bruker optics Corporation). Uma imagem \"ASTER_05\" foi adquirida em 15 de julho de 2017 em valores de ε. As amostras foram separadas por classes texturais e o comportamento espectral no TIR foi descrito. Os dados obtidos pelo sensor de laboratório foram reamostrados para as bandas do sensor de satélite. O comportamento entre os espectros de ambos sensores foi semelhante e teve correlação significativa com os atributos estudados, principalmente para areia. Para os modelos de regressão por mínimos quadrados parciais (PLSR), foram utilizadas seis estratégias (MIR, MIR_ASTER, ASTER, Termal, Termal IDC e MIR IDC) que consistiram no uso de todas as bandas de sensores, ou pela seleção das mesmas que apresentaram as correlações mais significativas com cada um dos atributos. Os modelos apresentaram um bom desempenho na predição de todos os atributos usando o MIR inteiro. No TIR, o modelo para areia total e para as frações fina e grossa foi bom. No caso dos modelos criados com os dados do sensor ASTER, não foram tão promissores quanto os de laboratório. O uso de bandas específicas ajudou a estimar alguns atributos no MIR e no TIR, aumentando o desempenho preditivo melhorando a validação dos modelos. Portanto, a discriminação dos atributos do solo com sensores de satélite pode ser melhorada com a identificação de bandas específicas, como observado nos resultados com sensores de laboratório.Soil texture and organic carbon (OC) content influence its spectral response. The study of these attributes is relevant for the preservation and proper management of land in pursuit of a sustainable agriculture. Laboratory and satellite sensors have been applied as a useful tool for studying soil attributes, but their analysis with these sensors has mainly focused on the visible (Vis), near infrared (NIR) and shortwave infrared (SWIR) regions of the electromagnetic spectrum, with few studies in the Medium Infrared (MIR). The objective of this study was to identify the spectral pattern of soils with different granulometry (sand and clay) and OC content using laboratory and satellite sensors in the MIR region, specifically in the Thermal Infrared (TIR) range. This study had qualitative and quantitative analyses of clay, OC and sand fractions (fine and coarse). The study area is located in the region of Piracicaba, São Paulo, Brazil. 150 soil samples were collected at a depth of 0-20 cm. Soil texture was determined by the pipette method and the percentage of OC via dry combustion. Reflectance and emissivity (ε) spectral data were obtained with the Fourier Transform Infrared (FT-IR) Alpha sensor (Bruker Optics Corporation). An image \"ASTER_05\" from July 15, 2017 was acquired with values of ε. Samples were separated by textural classes and the spectral behavior in the TIR region was described. The data obtained by the laboratory sensor were resampled to the satellite sensor bands. The behavior between spectra of both sensors was similar and had significant correlation with the studied attributes, mainly sand. For the partial least squares regression (PLSR) models, six strategies were used (MIR, MIR_ASTER, ASTER, Thermal, Thermal IDC and MIR IDC), which consisted in the use of all sensors bands, or by the selection of bands that presented the most significant correlations with each one of the attributes. Models presented a good performance in the prediction of all attributes using the whole MIR. In the TIR, models for total sand content and for fine and coarse fractions were good. In the case of models created with ASTER sensor data, they were not as promising as those with laboratory data. The use of specific bands was useful in estimating some attributes in the MIR and TIR, improving the predictive performance and validation of models. Therefore, the discrimination of soil attributes with satellite sensors can be improved with the identification of specific bands, as observed in the results with laboratory sensors

Satellite time series contribution to organic carbon mapping in cultivated soils at various regional scales

Le carbone organique du sol (COS) dans les zones agricoles joue un rôle clé dans la sécurité alimentaire et l'atténuation du changement climatique. La quantification du COS est nécessaire pour mettre en œuvre des techniques et des pratiques de stockage. Cependant, l'échantillonnage du COS dans un monde qui couvre environ 1,5 milliard d'hectares de sols agricoles est un véritable défi. C'est pourquoi l'utilisation de technologies telles que les capteurs satellitaires constitue une alternative prometteuse pour quantifier et cartographier le COS dans différents types d'agroécosystèmes à travers le monde. L'objectif de cette thèse est d'évaluer le potentiel des images satellitaires Sentinel-2 (S2) et Sentinel-1 (S1) pour la cartographie du COS dans les agro-écosystèmes de la France métropolitaine en utilisant des modèles spectraux et spatio-spectraux. Le chapitre 1 aborde l'état d'avancement de la cartographie du COS en France et présente les principales limitations et méthodes actuellement utilisées avec les données d'images satellitaires pour la prédiction du COS. Le chapitre 2 présente les zones d'étude situées dans les régions Bretagne, Occitanie et Centre Val de Loire. De plus, les principaux ensembles de données utilisés sont décrits et une analyse préliminaire de l'une des zones d'étude est présentée. Le troisième chapitre évalue le potentiel des images S2 et des produits dérivés de S1 et S2 pour prédire le SOC à l'aide d'images à date unique. Dans ce chapitre comme dans le second, des limitations liées principalement aux conditions de surface du sol ont été observées ; et les meilleures dates d'image pour détecter le SOC ont été identifiées. Dans la quatrième au lieu d'images à date unique, l'utilisation de mosaïques temporelles S2 de sol nu (S2Bsoil) par périodes est abordée comme l'utilisation de covariables dérivées de l'imagerie satellitaire et du terrain. Ce chapitre traite de l'importance de la sélection des périodes de production de S2Bsol et de l'utilisation de covariables pertinentes pour comprendre la variabilité spatiale du COS à l'échelle régionale. Enfin, le dernier chapitre aborde les principaux constats et perspectives à envisager dans un futur proche.Soil organic carbon (SOC) in agricultural areas plays a key role in food security and climate change mitigation. SOC quantification is necessary in order to implement storage techniques and practices. However, SOC sampling in a world that covers approximately 1.5 billion hectares of agricultural soils is quite challenging. Therefore, the use of technologies such as satellite sensors are a promising alternative to quantify and map SOC in various types of agroecosystems around the world. The objective of this thesis is to evaluate the potential of Sentinel-2 (S2) and Sentinel-1 (S1) satellite imagery for SOC mapping in agro-ecosystems in mainland France using spectral and spatio-spectral models. Chapter 1 addresses the progress of SOC mapping in France and presents the main limitations and methods currently used with satellite image data for SOC prediction. Chapter 2 presents the study areas located in the Brittany, Occitanie and Centre Val de Loire regions. In addition, the main datasets used are described and a preliminary analysis of one of the study areas is shown. The third chapter evaluates the potential of S2 images and derived products of S1 and S2 to predict SOC using single-date images. In this chapter as in the second, limitations related mainly to soil surface conditions were observed; and the best image dates for detecting SOC were identified. In the fourth instead of single date images, the use of S2 temporal mosaics of bare soil (S2Bsoil) by periods are addressed as the use of covariates derived from satellite imagery and terrain. This chapter deals the importance of selecting periods for the production of S2Bsoil and the use of relevant covariates to understand the spatial variability of SOC on a regional scale. Finally, the last chapter discusses the main findings and perspectives to be considered in the near future

Combined use of Sentinel-2 images and Sentinel-1-derived moisture maps for soil organic carbon content mapping in croplands, South-western France

International audienceIn terms of agronomy, soil organic carbon (SOC) content is important for crop growth and development. From the environmental viewpoint, SOC sequestration is essential to mitigate the emission of greenhouse gases into the atmosphere. The use of sensors for carbon monitoring over croplands is a key issue in recent works. Sentinel-1/2 (S1, S2) satellites acquire data with regular frequency (weekly) and high spatial resolution (10 and 20 meters). Previous studies have demonstrated their potential for quantification of soil attributes including topsoil organic carbon content on single dates. Soil surface roughness and soil moisture influence the performance of spectral models according to acquisition date, particularly surface soil moisture (SM), as shown by multidate models of predicted SOC content (Vaudour et al., 2021). Still, the sensitivity of Sentinel-1/2 to SM must be better understood and exploited for a given single date. A possible solution to determine the influence of SM on single date model performance consists of including it as a covariate.In order to predict the topsoil SOC content over croplands in the Pyrenees region, France (22177 km²), this study addresses: (i) the influence of the Sentinel image date and that of the soil sampling year; (ii) the contribution of SM products derived from the Sentinel-1/2 data (El Hajj et al., 2017) in the spectral models.The influence of the image date and soil sampling date was analyzed for springs 2017 and 2018. Clouds, shadows and NDVI (> 0.35) values were excluded from the images. Best single performances (RPD ≥ 1.3) were scored for soil sampling sets collected in 2016-2018. The same dates were analyzed using either SM maps, or signal values of VV and VH polarizations from S1 images. SM or polarization values were extracted for each sample and integrated into the partial least squares regression (PLSR) models, respectively. The best performance (RPD = 1.57) was obtained with SM as a covariate in 2017, with lowest mean SM throughout the map

Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands

International audienceThe spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 to predict SOC content over temperate agroecosystems characterized with annual crops. However, because spectral models are only applicable on bare soils, the mapping of SOC is often obtained on limited areas. A possible improvement for increasing the number of pixels on which SOC can be retrieved by inverting bare soil reflectance spectra, consists of using optical images acquired at several dates. This study compares different approaches of Sentinel–2 images temporal mosaicking to produce a composite multi-date bare soil image for predicting SOC content over agricultural topsoils. A first approach for temporal mosaicking was based on a per-pixel selection and was driven by soil surface characteristics: bare soil or dry bare soil with/without removing dry vegetation. A second approach for creating composite images was based on a per-date selection and driven either by the models performance from single-date, or by average soil surface indicators of bare soil or dry bare soil. To characterize soil surface, Sentinel-1 (S1)-derived soil moisture and/or spectral indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio 2 (NBR2), bare soil index (BSI) and a soil surface moisture index (S2WI) were used either separately or in combination. This study highlighted the following results: i) none of the temporal mosaic images improved model performance for SOC prediction compared to the best single-date image; ii) of the per-pixel approaches, temporal mosaics driven by the S1-derived moisture content, and to a lesser extent, by NBR2 index, outperformed the mosaic driven by the BSI index but they did not increase the bare soil area predicted; iii) of the per-date approaches, the best trade-off between predicted area and model performance was achieved from the temporal mosaic driven by the S1-derived moisture content (R2 ~ 0.5, RPD ~ 1.4, RMSE ~ 3.7 g.kg-1) which enabled to more than double (*2.44) the predicted area. This study suggests that a number of bare soil mosaics based on several indicators (moisture, bare soil, roughness…), preferably in combination, might maintain acceptable accuracies for SOC prediction whilst extending over larger areas than single-date images

Global Digital Analysis for Science Diplomacy on Climate Change and Sustainable Development

Addressing climate change requires innovative, collective action and robust international collaboration. Through joint efforts, nations can significantly reduce greenhouse gas emissions, pioneer sustainable technologies, and implement effective adaptation measures. Science diplomacy and knowledge sharing hold the potential to bolster global stability and peace by directly confronting climate change challenges. Therefore, it becomes imperative to evaluate a country’s alignment of its scientific knowledge system (SKS) with international guidelines. This study delineates the global scientific discourse on climate change and juxtaposes the alignment between an individual nation’s research endeavors and United Nations resolutions concerning climate change and sustainable development. Our methodology integrates data extraction from scientific research databases with advanced textual analysis tools, highlighting this study’s unique focus on the intersection of climate change and UN resolutions. To deliver an empirical analysis, we leveraged complex network theory and advanced text-processing techniques. Our findings demonstrate the trajectory of global scientific output related to these themes, segmented by countries and coupled with CO2 emissions data, key disciplines, and collaboration networks. These insights are instrumental for leaders, policymakers, and stakeholders, highlighting areas of convergence and divergence in national research initiatives essential for achieving global climate goals. Such knowledge is strategically useful for crafting purpose-driven public policies and honoring enduring multilateral pledges to address the climate crisis proactively

Sentinel-2 and Sentinel-1 Bare Soil Temporal Mosaics of 6-Year Periods for Soil Organic Carbon Content Mapping in Central France

Satellite-based soil organic carbon content (SOC) mapping over wide regions is generally hampered by the low soil sampling density and the diversity of soil sampling periods. Some unfavorable topsoil conditions, such as high moisture, rugosity, the presence of crop residues, the limited amplitude of SOC values and the limited area of bare soil when a single image is used, are also among the influencing factors. To generate a reliable SOC map, this study addresses the use of Sentinel-2 (S2) temporal mosaics of bare soil (S2Bsoil) over 6 years jointly with soil moisture products (SMPs) derived from Sentinel 1 and 2 images, SOC measurement data and other environmental covariates derived from digital elevation models, lithology maps and airborne gamma-ray data. In this study, we explore (i) the dates and periods that are preferable to construct temporal mosaics of bare soils while accounting for soil moisture and soil management; (ii) which set of covariates is more relevant to explain the SOC variability. From four sets of covariates, the best contributing set was selected, and the median SOC content along with uncertainty at 90% prediction intervals were mapped at a 25-m resolution from quantile regression forest models. The accuracy of predictions was assessed by 10-fold cross-validation, repeated five times. The models using all the covariates had the best model performance. Airborne gamma-ray thorium, slope and S2 bands (e.g., bands 6, 7, 8, 8a) and indices (e.g., calcareous sedimentary rocks, “calcl”) from the “late winter–spring” time series were the most important covariates in this model. Our results also indicated the important role of neighboring topographic distances and oblique geographic coordinates between remote sensing data and parent material. These data contributed not only to optimizing SOC mapping performance but also provided information related to long-range gradients of SOC spatial variability, which makes sense from a pedological point of view

Using Sentinel-2 Images for Soil Organic Carbon Content Mapping in Croplands of Southwestern France. The Usefulness of Sentinel-1/2 Derived Moisture Maps and Mismatches between Sentinel Images and Sampling Dates

International audienceIn agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km²), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017–2018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD ≥ 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyrénées

Mapping topsoil organic carbon content over temperate cropland regions. The contribution of the Sentinel-2 and Sentinel 1 satellites for Beauce in central France.

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