Soil degradation index developed by multitemporal remote sensing images, climate variables, terrain and soil atributes

Author(s)

Nascimento, Claudia Maria ^[1] ; Mendes, Wanderson de Sousa ^[1] ; Quinonez Silvero, Nelida Elizabet ^[1] ; Poppiel, Raill Roberto ^[1] ; Sayao, Veridiana Maria ^[1] ; Dotto, Andre Carnieletto ^[1] ; dos Santos, Natasha Valadares ^[1] ; Accorsi Amorim, Merilyn Taynara ^[1] ; Dematte, Jose A. M. ^[1]

Affiliation

^[1] Univ Sao Paulo, Coll Agr Luiz de Queiroz, Dept Soil Sci, Padua Dias Ave 11, CP 9, BR-13418900 Piracicaba, SP - Brazil

• Document type: Journal article
• Source: Journal of Environmental Management; v. 277, JAN 1 2021.
• Web of Science Citations: 0

Abstract

Studies on soil degradation are essential for environmental preservation. Since almost 30% of the global soils are degraded, it is important to study and map them for improving their management and use. We aimed to obtain a Soil Degradation Index (SDI) based on multi-temporal satellite images associated with climate variables, land use, terrain and soil attributes. The study was conducted in a 2598 km(2) area in Sao Paulo State, Brazil, where 1562 soil samples (0-20 cm) were collected and analyzed by conventional methods. Spatial predictions of soil attributes such as clay, cation exchange capacity (CEC) and soil organic matter (OM) were performed using machine learning algorithms. A collection of 35-year Landsat images was used to obtain a multi-temporal bare soil image, whose spectral bands were used as soil attributes predictors. The maps of clay, CEC, climate variables, terrain attributes and land use were overlaid and the K-means clustering algorithm was applied to obtain five groups, which represented levels of soil degradation (classes from 1 to 5 representing very low to very high soil degradation). The SDI was validated using the predicted map of OM. The highest degradation level obtained in 15% of the area had the lowest OM content. Levels 1 and 4 of SDI were the most representative covering 24% and 23% of the area, respectively. Therefore, satellite images combined with environmental information significantly contributed to the SDI development, which supports decision-making on land use planning and management. (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: 18/09656-0 - Relationship between soil degradation and soil classes evaluated by a 27 years Landsat image (Vis-Nir-Swir-Tir), derived from climate and digital elevation model, as to assit on digital soil mapping
• Grantee: Claudia Maria Nascimento
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• 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