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

Castaldi, Fabio; Chabrillat, Sabine; Don, Axel; Wesemael, Bas van

doi:10.3390/rs11182121

Cited by 86 publications

(84 citation statements)

References 30 publications

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“…Studies of Sentinel-2 potential for soil monitoring have been undertaken. Soil organic carbon (SOC) can be successfully mapped in croplands using Sentinel-2 bands [63][64][65][66]. SOC prediction models were found to be overall better when formulated using the visible B4, B5, near infrared B8A, and two SWIR bands (B11, B12) [67].…”

Section: Sentinel-2 For Precision Agriculturementioning

confidence: 99%

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

et al. 2020

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The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

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Section: Sentinel-2 For Precision Agriculturementioning

confidence: 99%

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

et al. 2020

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“…Then the bare soil composite was composed by aggregating the multitemporal bare soil pixels by their median value. More detailed information about GEOS3, the spectral indices and sensitivity analysis of spectral indices thresholds are described in [18] or elsewhere [13,[24][25][26].…”

Section: Bare Soil Compositesmentioning

confidence: 99%

“…Thus, as the uncertainty is standardized to the median estimate, we can compare both the spatial patterns and the differences between the uncertainty maps. Additionally, to visually asses the regional variability of the predicted clay map, four different locations were selected based on the spatial variability of soils and previous works developed at the same sites [13,[42][43][44][45].…”

Section: Spatial Prediction and Uncertaintymentioning

confidence: 99%

“…Protocols for soil reflectance acquisition from other domains (field, air and space) are not yet available, while the laboratory measurements (e.g., the SSL) are considered the most accurate; however, they cannot be directly applied to data collected from air or space [10]. Whereas laboratory conditions allow for better control of reflectance measurements, airborne and satellite sensors are critically influenced by in situ conditions and other factors [11][12][13]. However, detailed mapping of large spatial extents can be performed using measurements made from large remote-sensing spectral surveys.…”

Section: Introductionmentioning

confidence: 99%

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Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe

et al. 2020

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Reflectance of light across the visible, near-infrared and shortwave infrared (VIS-NIR-SWIR, 0.4–2.5 µm) spectral region is very useful for investigating mineralogical, physical and chemical properties of soils, which can reduce the need for traditional wet chemistry analyses. As many collections of multispectral satellite data are available for environmental studies, a large extent with medium resolution mapping could be benefited from the spectral measurements made from remote sensors. In this paper, we explored the use of bare soil composites generated from the large historical collections of Landsat images for mapping cropland topsoil attributes across the European extent. For this task, we used the Geospatial Soil Sensing System (GEOS3) for generating two bare soil composites of 30 m resolution (named synthetic soil images, SYSI), which were employed to represent the median topsoil reflectance of bare fields. The first (framed SYSI) was made with multitemporal images (2006–2012) framed to the survey time of the Land-Use/Land-Cover Area Frame Survey (LUCAS) soil dataset (2009), seeking to be more compatible to the soil condition upon the sampling campaign. The second (full SYSI) was generated from the full collection of Landsat images (1982–2018), which although displaced to the field survey, yields a higher proportion of bare areas for soil mapping. For evaluating the two SYSIs, we used the laboratory spectral data as a reference of topsoil reflectance to calculate the Spearman correlation coefficient. Furthermore, both SYSIs employed machine learning for calibrating prediction models of clay, sand, soil organic carbon (SOC), calcium carbonates (CaCO3), cation exchange capacity (CEC), and pH determined in water, using the gradient boosting regression algorithm. The original LUCAS laboratory spectra and a version of the data resampled to the Landsat multispectral bands were also used as reference of prediction performance using VIS-NIR-SWIR multispectral data. Our results suggest that generating a bare soil composite displaced to the survey time of soil observations did not improve the quality of topsoil reflectance, and consequently, the prediction performance of soil attributes. Despite the lower spectral resolution and the variability of soils in Europe, a SYSI calculated from the full collection of Landsat images can be employed for topsoil prediction of clay and CaCO3 contents with a moderate performance (testing R2, root mean square error (RMSE) and ratio of performance to interquartile range (RPIQ) of 0.44, 9.59, 1.77, and 0.36, 13.99, 1.54, respectively). Thus, this study shows that although there exist some constraints due to the spatial and temporal variation of soil exposures and among the Landsat sensors, it is possible to use bare soil composites for mapping key soil attributes of croplands across the European extent.

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“…[167][168][169] The availability of increasing amounts of remote sensing data, notably the Sentinel satellite series, is promising for enabling not only updates to existing maps but also a wider coverage of digital soil maps of soil properties. Remote sensing reflectance spectra of bare soil pixels are likely to predict several topsoil properties 170 while reproducing spatial structure [171][172][173][174] and may also contribute to infer uncertainty. 175 To date, few authors have incorporated satellite imagery as covariates within DSM models.…”

Section: Digital Soil Mapping (Dsm)mentioning

confidence: 99%

Soil Science Challenges in a New Era: A Transdisciplinary Overview of Relevant Topics

Rodrigo‐Comino

López‐Vicente

Kumar³

et al. 2020

Air, Soil and Water Research

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Transdisciplinary approaches that provide holistic views are essential to properly understand soil processes and the importance of soil to society and will be crucial in the future to integrate distinct disciplines into soil studies. A myriad of challenges faces soil science at the beginning of the 2020s. The main aim of this overview is to assess past achievements and current challenges regarding soil threats such as erosion and soil contamination related to different United Nations sustainable development goals (SDGs) including (1) sustainable food production, (2) ensure healthy lives and reduce environmental risks (SDG3), (3) ensure water availability (SDG6), and (4) enhanced soil carbon sequestration because of climate change (SDG13). Twenty experts from different disciplines related to soil sciences offer perspectives on important research directions. Special attention must be paid to some concerns such as (1) effective soil conservation strategies; (2) new computational technologies, models, and in situ measurements that will bring new insights to in-soil process at spatiotemporal scales, their relationships, dynamics, and thresholds; (3) impacts of human activities, wildfires, and climate change on soil microorganisms and thereby on biogeochemical cycles and water relationships; (4) microplastics as a new potential pollutant; (5) the development of green technologies for soil rehabilitation; and (6) the reduction of greenhouse gas emissions by simultaneous soil carbon sequestration and reduction in nitrous oxide emission. Manuscripts on topics such as these are particularly welcomed in Air, Soil and Water Research.

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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

Cited by 86 publications

References 30 publications

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Multispectral Models from Bare Soil Composites for Mapping Topsoil Properties over Europe

Soil Science Challenges in a New Era: A Transdisciplinary Overview of Relevant Topics

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