Soil Order-Land Use Index Using Field-Satellite Spectroradiometry in the Ecuadorian Andean Territory for Modeling Soil Quality

• Published in: Sustainability Vol. 14; no. 12; p. 7426
• Main Authors: Arciniegas-Ortega, Susana, Molina, Iñigo, Garcia-Aranda, Cesar
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.06.2022
• Subjects: Discriminant analysis; Electrical conductivity; Electrical resistivity; Laboratories; Laboratory methods; Land use; Land use management; Land use planning; Loam soils; Organic matter; Prediction models; Remote monitoring; Remote sensing; Satellites; Soil contamination; Soil degradation; Soil moisture; Soil quality; Spectroscopy; Statistical analysis; Sustainability; Vegetation; Ecuador
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su14127426

Land use conversion is the main cause for soil degradation, influencing the sustainability of agricultural activities in the Ecuadorian Andean region. The possibility to identify the quality based on the spectral properties allows remote sensing methods to offer an alternative form of monitoring the environment. This study used laboratory spectroscopy and multi-spectral images (Sentinel 2) with environmental covariates (physicochemical parameters) to find an affordable method that can be used to present spatial prediction models as a tool for the evaluation of the quality of Andean soils. The models were developed using statistical techniques of logistic regression and linear discriminant analysis to generate an index based on soil order and three indexes based on the combination of soil order and land use. This combined approach offers an effective method, relative to traditional laboratory methods, to derive estimates of the content and composition of soil constituents, such as electrical conductivity (CE), organic matter (OM), pH, and soil moisture (HU). For Mollisol index.3 with Páramo land use, a value of organic matter (OM) ≥8.6% was obtained, whereas for Mollisol index.4 with Shrub land use, OM was ≥6.1%. These results reveal good predictive (estimation) capabilities for these soil order–land use groups. This provides a new way to monitor soil quality using remote sensing techniques, opening promising prospects for operational applications in land use planning.