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

Autor(es)

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]

Afiliação do(s) autor(es)

^[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

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 10, n. 10 OCT 2018.
• Citações Web of Science: 6

Resumo

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)

• Processo FAPESP: 16/26124-6 - Pedologia de precisão: caracterização e mapeamento de solos em tempo real por geotecnologias
• Beneficiário: Wanderson de Sousa Mendes
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 14/22262-0 - Geotecnologias no mapeamento digital pedológico detalhado e biblioteca espectral de solos do Brasil: desenvolvimento e aplicações
• Beneficiário: José Alexandre Melo Demattê
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 16/01597-9 - Pedotransferência por geotecnologias associada à fotopedologia com vistas ao mapeamento pedológico de áreas agrícolas do estado de São Paulo
• Beneficiário: José Lucas Safanelli
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto