Spatial estimation of surface soil texture using remote sensing data

• Published in: Soil science and plant nutrition (Tokyo) Vol. 59; no. 4; pp. 488 - 500
• Main Authors: Liao, Kaihua, Xu, Shaohui, Wu, Jichun, Zhu, Qing
• Format: Journal Article
• Language: English
• Published: Kyoto Taylor & Francis 01-08-2013; Taylor & Francis Ltd
• Subjects: agricultural management; clay fraction; Contamination; environmental protection; geostatistics; kriging; land use planning; Landsat; landscapes; multiple stepwise regression; Remote sensing; soil heterogeneity; soil sampling; Soil sciences; soil texture; spatial estimation
• ISSN: 1747-0765; 0038-0768; 1747-0765
• DOI: 10.1080/00380768.2013.802643

Understanding the spatial distribution and variability of soil texture is essential for land use planning and other activities related to agricultural management and environmental protection. This study was conducted to evaluate Landsat Enhanced Thematic Mapper (ETM) remote sensing data as auxiliary variables for spatial estimation of surface soil texture using a limited number of soil samples taken from a site located in the city of PingduShandong ProvinceChina. Three methods of evaluating variability in surface soil texture were evaluated: (1) multiple stepwise regression (MSR) based on the relationship between surface soil sandsilt and clay contents and remote sensing data; (2) kriging of surface soil sandsilt and clay contents; (3) cokriging with remote sensing data. Correlation analysis showed that surface soil sandsilt and clay contents were significantly correlated with Landsat ETM digital number (DN) of six bands (Bands 1–5 and Band 7)and the DN of Band 7 explained most of the variability in soil sandsilt and clay contents. The DN of Band 7 was selected as auxiliary data for the estimation of surface soil texture. The cross-validation results indicated that both MSR and kriged estimates had low reliability due to the variations in landscape and the low-density sampling in the study area. Cokriging with remote sensing data significantly improves estimates of surface soil texture compared with MSR and kriging.