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

In 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.

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

10.5194/egusphere-egu21-8836 ◽

2021 ◽

Author(s):

Diego Urbina Salazar ◽

Emmanuelle Vaudour ◽

Nicolas Baghdadi ◽

Eric Ceschia ◽

Dominique Arrouays

Keyword(s):

Soil Moisture ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Content ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Soil Sampling ◽

Organic Carbon Content ◽

Spectral Models ◽

Sentinel 2

In 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 km&#178;), 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 &#8805; 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.&#160;ReferencesEl Hajj, M.; Baghdadi, N.; Zribi, M.; Bazzi, H. Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas. Remote Sensing 2017, 9, 1292, doi:10.3390/rs9121292.Vaudour, E.; Gomez, C.; Lagacherie, P.; Loiseau, T.; Baghdadi, N.; Urbina-Salazar, D.; Loubet, B.; Arrouays, D. Temporal Mosaicking Approaches of Sentinel-2 Images for Extending Topsoil Organic Carbon Content Mapping in Croplands. International Journal of Applied Earth Observation and Geoinformation 2021, 96, 102277, doi:10.1016/j.jag.2020.102277.

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Soil Water Characteristics of Gleysols in the Bamenda (Cameroon) Wetlands and Implications for Agricultural Management Strategies

Applied and Environmental Soil Science ◽

10.1155/2021/6643208 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Alice Mufur Magha ◽

Primus Azinwi Tamfuh ◽

Lionelle Estelle Mamdem ◽

Marie Christy Shey Yefon ◽

Bertrand Kenzong ◽

...

Keyword(s):

Soil Moisture ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Water ◽

Total Variance ◽

Organic Carbon Content ◽

Available Water ◽

High Soil ◽

Local Soil ◽

Very High

Water budgeting in agriculture requires local soil moisture information as crops depend mainly on moisture available at root level. The present paper aims to evaluate the soil moisture characteristics of Gleysols in the Bamenda (Cameroon) wetlands and to evaluate the link between soil moisture content and selected soil characteristics affecting crop production. The work was conducted in the field and laboratory, and data were analyzed by simple descriptive statistics. The main results showed that the soils had a silty clayey to clayey texture, high bulk density, high soil organic carbon content, and high soil organic carbon stocks. The big difference between moisture contents at wilting point and at field capacity testified to very high plant-available water content. Also, the soils displayed very high contents of readily available water and water storage contents. The soil moisture characteristics give sigmoid curves and enabled noting that the Gleysols attain their full water saturation at a range of 57.68 to 91.70% of dry soil. Clay and SOC contents show a significant positive correlation with most of the soil moisture characteristics, indicating that these soil properties are important for soil water retention. Particle density, coarse fragments, and sand contents correlated negatively with the soil moisture characteristics, suggesting that they decrease soil water-holding capacity. The principal component analysis (PCA) enabled reducing 17 variables described to only three principal components (PCs) explaining 73.73% of the total variance; the first PC alone expressed 45.12% of the total variance, associating clay, SOC, and six soil moisture characteristics, thus portraying a deep correlation between these eight variables. Construction of contoured ditches, deep tillage, and raised ridges management techniques during the rainy season while channeling water from nearby water bodies into the farmland, opportunity cropping, and usage of water cans and other irrigation strategies are used during the dry season to combat water constraints.

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Sentinel-2 exposed soil composite for soil organic carbon prediction: the ‘greening-up’ method for detecting suitable images

10.5194/egusphere-egu21-11153 ◽

2021 ◽

Author(s):

Klara Dvorakova ◽

Bas van Wesemael

Keyword(s):

Remote Sensing ◽

Surface Roughness ◽

Soil Moisture ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Satisfactory Result ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Sentinel 2

Pilot studies have demonstrated the potential for remote sensing techniques for soil organic carbon&#160; (SOC) mapping in exposed croplands. However, the use of remote sensing for SOC prediction is often hindered by disturbing factors at the soil surface such as photosynthetic active and nonphotosynthetic active vegetation, variation in soil moisture or surface roughness. With the increasing amount of freely available satellite data, many studies have focused on stabilizing the soil reflectance by building image composites that are generated using a set of criteria. These composites tend to minimize and cancel out the disturbing effects. Here we aim to develop a robust method that allows selecting Sentinel-2 (S-2) pixels that are not affected by the following disturbing factors: crop residues, surface roughness and soil moisture. We selected all S-2 cloud-free images covering the Loam Belt of Belgium from January 2019 to December 2020 (in total 38 images). We then built four exposed soil composites based on four sets of criteria: (1) NDVI < 0.25, (2) NDVI < 0.25 & Normalized Burn Ratio (NBR2) < 0.07, (3) the &#8216;greening-up&#8217; period of a crop and (4) the &#8216;greening-up&#8217; period of a crop & NBR2 < 0.07. The &#8216;greening-up&#8217; period was selected based on the NDVI timeline, where &#8216;greening-up&#8217; is considered as the last date of acquisition where the soil is bare (NDVI < 0.25) before the crop develops (NDVI > 0.6).,We then built a partial least square regression (PLSR) model with 10-fold cross-validation to estimate the SOC content based on 137 georeferenced calibration samples on the four above described composites. We obtained a non-satisfactory result for composites (1) to (3): R&#178; = 0.22, RMSE = 3.46 g&#160;C kg-1 and RPD 1.12 for (1), R&#178; = 0.19, RMSE = 3.43 g C&#160;kg-1 and RPD 1.10 for (2) and R&#178; = 0.15, RMSE = 2.74 g&#160;C kg-1 and RPD 1.06 for (3). We, however, obtained a satisfactory result for composite (4): R&#178; = 0.54, RMSE = 2.09 g C kg-1 and RPD 1.68. Hence, the &#8216;greening-up&#8217; method combined with a strict NBR2 threshold allows selecting the purest exposed soil pixels suitable for SOC prediction. The limit of this method might be the surface coverage, which for a two-year period reached 47% of croplands, compared to 89% exposure if only the NDVI threshold is applied.

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Soil Organic Carbon Mapping Using LUCAS Topsoil Database and Sentinel-2 Data: An Approach to Reduce Soil Moisture and Crop Residue Effects

Remote Sensing ◽

10.3390/rs11182121 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2121 ◽

Cited By ~ 11

Author(s):

Fabio Castaldi ◽

Sabine Chabrillat ◽

Axel Don ◽

Bas van Wesemael

Keyword(s):

Soil Moisture ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Ad Hoc ◽

Crop Residues ◽

Prediction Models ◽

Bare Soil ◽

Large Area ◽

Red Edge ◽

Sentinel 2

Soil organic carbon (SOC) loss is one of the main causes of soil degradation in croplands. Thus, spatial and temporal monitoring of SOC is extremely important, both from the environmental and economic perspective. In this regard, the high temporal, spatial, and spectral resolution of the Sentinel-2 data can be exploited for monitoring SOC contents in the topsoil of croplands. In this study, we aim to test the effect of the threshold for a spectral index linked to soil moisture and crop residues on the performance of SOC prediction models using the Multi-Spectral Instrument (MSI) Sentinel-2 and the European Land Use/cover Area frame Statistical survey (LUCAS) topsoil database. The LUCAS spectral data resampled according to MSI/Sentinel-2 bands, which were used to build SOC prediction models combining pairs of the bands. The SOC models were applied to a Sentinel-2 image acquired in North-Eastern Germany after removing the pixels characterized by clouds and green vegetation. Then, we tested different thresholds of the Normalized Burn Ratio 2 (NBR2) index in order to mask moist soil pixels and those with dry vegetation and crop residues. The model accuracy was tested on an independent validation database and the best ratio of performance to deviation (RPD) was obtained using the average between bands B6 and B5 (Red-Edge Carbon Index: RE-CI) (RPD: 4.4) and between B4 and B5 (Red-Red-Edge Carbon Index: RRE-CI) (RPD: 2.9) for a very low NBR2 threshold (0.05). Employing a higher NBR2 tolerance (higher NBR2 values), the mapped area increases to the detriment of the validation accuracy. The proposed approach allowed us to accurately map SOC over a large area exploiting the LUCAS spectral library and, thus, avoid a new ad hoc field campaign. Moreover, the threshold for selecting the bare soil pixels can be tuned, according to the goal of the survey. The quality of the SOC map for each tolerance level can be judged based on the figures of merit of the model.

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Soil organic carbon content monitoring and mapping using airborne and Sentinel-2 spectral imaging

Sixth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2018) ◽

10.1117/12.2323820 ◽

2018 ◽

Author(s):

Asa Gholizadeh ◽

Daniel Zizala ◽

Mohammadmehdi Saberioon ◽

Lubos Boruvka

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Content ◽

Spectral Imaging ◽

Organic Carbon Content ◽

Soil Organic Carbon Content ◽

Sentinel 2

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3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning

Vadose Zone Journal ◽

10.1002/vzj2.20062 ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Tobias Rentschler ◽

Ulrike Werban ◽

Mario Ahner ◽

Thorsten Behrens ◽

Philipp Gries ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Content ◽

Organic Carbon Content ◽

3D Mapping ◽

Soil Organic Carbon Content

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Soil Organic Carbon Content Prediction Using Soil-Reflected Spectra: A Comparison of Two Regression Methods

Remote Sensing ◽

10.3390/rs13234752 ◽

2021 ◽

Vol 13 (23) ◽

pp. 4752

Author(s):

Sharon Gomes Ribeiro ◽

Adunias dos Santos Teixeira ◽

Marcio Regys Rabelo de Oliveira ◽

Mirian Cristina Gomes Costa ◽

Isabel Cristina da Silva Araújo ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Content ◽

Spectral Reflectance ◽

Arid Region ◽

Principal Component ◽

Organic Carbon Content ◽

Least Squares Regression ◽

Semi Arid Region ◽

Semi Arid

Quantifying the organic carbon content of soil over large areas is essential for characterising the soil and the effects of its management. However, analytical methods can be laborious and costly. Reflectance spectroscopy is a well-established and widespread method for estimating the chemical-element content of soils. The aim of this study was to estimate the soil organic carbon (SOC) content using hyperspectral remote sensing. The data were from soils from two localities in the semi-arid region of Brazil. The spectral reflectance factors of the collected soil samples were recorded at wavelengths ranging from 350–2500 nm. Pre-processing techniques were employed, including normalisation, Savitzky–Golay smoothing and first-order derivative analysis. The data (n = 65) were examined both jointly and by soil class, and subdivided into calibration and validation to independently assess the performance of the linear methods. Two multivariate models were calibrated using the SOC content estimated in the laboratory by principal component regression (PCR) and partial least squares regression (PLSR). The study showed significant success in predicting the SOC with transformed and untransformed data, yielding acceptable-to-excellent predictions (with the performance-to-deviation ratio ranging from 1.40–3.38). In general, the spectral reflectance factors of the soils decreased with the increasing levels of SOC. PLSR was considered more robust than PCR, whose wavelengths from 354 to 380 nm, 1685, 1718, 1757, 1840, 1876, 1880, 2018, 2037, 2042, and 2057 nm showed outstanding absorption characteristics between the predicted models. The results found here are of significant practical value for estimating SOC in Neosols and Cambisols in the semi-arid region of Brazil using VIS-NIR-SWIR spectroscopy.

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