Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology

Author(s)

Gallo, Bruna C. ^{[1, 2, 3]} ; Dematte, Jose A. M. ^{[1, 2, 3]} ; Rizzo, Rodnei ^[1] ; Safanelli, Jose L. ^[1] ; Mendes, Wanderson de S. ^[1] ; Lepsch, Igo F. ^[1] ; Sato, Marcus V. ^[1] ; Romero, Danilo J. ^[1] ; Lacerda, Marilusa P. C. ^[4]

Affiliation

^[1] Univ Sao Paulo, Dept Soil Sci, Coll Agr Luiz de Queiroz, Rua Padua Dias 11, Cx Postal 09, BR-13416900 Sao Paulo - Brazil

^[2] Univ Estadual Campinas, Univ Sao Paulo, Interdisciplinary Program Bioenergy, Rua Monteiro Lobato 80, BR-13083852 Campinas, SP - Brazil

^[3] Sao Paulo State Univ UNESP, Rua Monteiro Lobato 80, BR-13083852 Campinas, SP - Brazil

^[4] Univ Brasilia, Fac Agron & Vet Med, ICC Sul, Campus Univ Darcy Ribeiro, Cx Postal 4508, BR-70910960 Brasilia, DF - Brazil

• Document type: Journal article
• Source: REMOTE SENSING; v. 10, n. 10 OCT 2018.
• Web of Science Citations: 6

Abstract

The mapping of soil attributes provides support to agricultural planning and land use monitoring, which consequently aids the improvement of soil quality and food production. Landsat 5 Thematic Mapper (TM) images are often used to estimate a given soil attribute (i.e., clay), but have the potential to model many other attributes, providing input for soil mapping applications. In this paper, we aim to evaluate a Bare Soil Composite Image (BSCI) from the state of SAo Paulo, Brazil, calculated from a multi-temporal dataset, and study its relationship with topsoil properties, such as soil class and geology. The method presented detects bare soil in satellite images in a time series of 16 years, based on Landsat 5 TM observations. The compilation derived a BSCI for the agricultural sites (242,000 hectare area) characterized by very complex geology. Soil properties were analyzed to calibrate prediction models using 740 soil samples (0-20 cm) collected of the area. Partial least squares regression (PLSR) based on the BSCI spectral dataset was performed to quantify soil attributes. The method identified that a single image represents 7 to 20% of bare soil while the compilation of the multi-temporal dataset increases to 53%. Clay content had the best soil attribute prediction estimates (R-2 = 0.75, root mean square error (RMSE) = 89.84 g kg(-1), and accuracy = 74%). Soil organic matter, cation exchange capacity and sandy soils also achieved moderate predictions. The BSCI demonstrates a strong relationship with legacy geological maps detecting variations in soils. From a single composite image, it was possible to use spectroscopy to evaluate several environmental parameters. This technique could greatly improve soil mapping and consequently aid several applications, such as land use planning, environmental monitoring, and prevention of land degradation, updating legacy surveys and digital soil mapping. (AU)

• FAPESP's process: 16/26124-6 - Precision pedology: soil characterisation and mapping in real time using geotechnologies
• Grantee: Wanderson de Sousa Mendes
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• FAPESP's process: 14/22262-0 - Geotechnologies on a detailed digital soil mapping and the Brazilian soil spectral library: development and applications
• Grantee: José Alexandre Melo Demattê
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 16/01597-9 - Pedotransfer functions by geotecnologies associated with photopedology for pedological mapping in agricultural areas of São Paulo State
• Grantee: José Lucas Safanelli
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)