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

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.

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Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007300 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5007300_1
Genre: Journal Article


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Safanelli, José L.1, Author
Demattê, José A.M.1, Author
Chabrillat, S.2, Author              
Poppiel, Raul R.1, Author
Rizzo, Rodnei1, Author
Dotto, André C.1, Author
Silvero, Nélida E.Q.1, Author
Mendes, Wanderson de S.1, Author
Bonfatti, Benito R.1, Author
Ruiz, Luis F.C.1, Author
ten Caten, Alexandre1, Author
Dalmolin, Ricardo S.D.1, Author
1External Organizations, ou_persistent22              
21.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028              


Free keywords: Digital soil mapping; Google Earth engine;Machine learning; Croplands; Remote sensing; Bare soil composite
 Abstract: 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.


Language(s): eng - English
 Dates: 20212021
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.geoderma.2021.115042
GFZPOF: p4 T5 Future Landscapes
 Degree: -



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Title: Geoderma
Source Genre: Journal, SCI, Scopus
Publ. Info: -
Pages: - Volume / Issue: 396 Sequence Number: 115042 Start / End Page: - Identifier: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals162
Publisher: Elsevier