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Journal Article

Leveraging the application of Earth observation data for mapping cropland soils in Brazil


Safanelli,  José L.
External Organizations;

Demattê,  José A.M.
External Organizations;


Chabrillat,  S.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Poppiel,  Raul R.
External Organizations;

Rizzo,  Rodnei
External Organizations;

Dotto,  André C.
External Organizations;

Silvero,  Nélida E.Q.
External Organizations;

Mendes,  Wanderson de S.
External Organizations;

Bonfatti,  Benito R.
External Organizations;

Ruiz,  Luis F.C.
External Organizations;

ten Caten,  Alexandre
External Organizations;

Dalmolin,  Ricardo S.D.
External Organizations;

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Safanelli, J. L., Demattê, J. A., Chabrillat, S., Poppiel, R. R., Rizzo, R., Dotto, A. C., Silvero, N. E., Mendes, W. d. S., Bonfatti, B. R., Ruiz, L. F., ten Caten, A., Dalmolin, R. S. (2021): Leveraging the application of Earth observation data for mapping cropland soils in Brazil. - Geoderma, 396, 115042.

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007300
Despite the natural spatial variability, cropland soils are subject to many interventions that can lead to alterations of soil functioning. As the cropland expansion took place in Brazil the last decades, leading to significant land-use change and environmental impacts, detailed information about soils is fundamental for sustainable development. Thus, considering the lack of spatially explicit information about cropland soils in Brazil, we aimed at performing high-resolution mapping of key topsoil attributes using spectral and terrain features extracted from Earth observation data (EOD). With the resulting information, we also aimed at performing a general examination of the main agricultural regions and estimate the total organic carbon stocks on croplands soils. For this, we obtained environmental predictors from the historical collection of Landsat data and the digital elevation model from Shuttle Radar Topographic Mission at the cloud-based platform of Google Earth Engine. The environmental predictors (30 m spatial resolution) with georeferenced soil samples (n = 5097) were used for predicting the topsoil content (0–20 cm) of clay, sand, silt, cation exchange capacity, pH, soil organic carbon (SOC) and SOC stock. Prediction models of clay, sand, SOC content, and SOC stocks had the best performance metrics, achieving a R2 ranging from 0.44 to 0.74 and ratio of performance to the interquartile range higher than 1.5. The predicted maps revealed the variability of topsoil among the cropped areas, indicating that the agricultural expansion took place on sandy soils. The SOC stock map provided consistent estimates compared to previous datasets but revealed additional information at the local and regional scales. Thus, this study supports the proposition that EOD is a valuable source for extracting environmental features for mapping and monitoring cropland soils at finer resolutions, assisting the evaluation of soil spatial distribution and the historical agriculture expansion over large geographical areas.