期刊
GEODERMA
卷 409, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.geoderma.2021.115597
关键词
Digital soil mapping; Multiple linear regression; Principal component analysis; Spectral indices; Sustainable land management; Uncertainty analysis
类别
资金
- Tokyo Metropolitan University
In this study, digital soil assessment using principal component analysis and multiple linear regression was conducted to create a digital map of soil fertility for jasmine rice. The map revealed that over half of the Thung Kula Ronghai region had low to very low soil fertility levels, which corresponded well with actual rice production in the area.
Digital soil assessment (DSA) is an application for the interpretation of digital soil mapping (DSM) outputs for evaluating soil and related environmental factors. However, DSA has not been widely applied to assess soil fertility in the humid tropics due to the complexity of integrating numerous soil chemical properties into one map. In this study, principal component analysis was applied to extract the most appropriate soil indicator for crop production. A digital map of soil fertility for jasmine rice was created using a multiple linear regression (MLR) model. Finally, the digital map was confirmed by comparing it with actual rice yield in the Thung Kula Ronghai (TKR) region of Thailand. The calculated soil fertility was significantly expressed by soil pH, electrical conductivity, organic matter, and contents of macronutrients (P, K, Ca, and Mg). The obtained MLR model for this region's soil fertility consisted of spectral indices (brightness, coloration, normalized difference water, and moisture stress) and topographic indices (slope and topographic wetness). The digital map indicated that more than half of the TKR region contained very low to low soil fertility. The soil fertility map fits well to actual rice production in the TKR region.
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