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.

数字土壤评估 (DSA) 是一种用于解释数字土壤测绘 (DSM) 输出以评估土壤和相关环境因素的应用程序。然而，由于将众多土壤化学特性整合到一张地图中的复杂性，DSA 尚未广泛应用于评估潮湿热带地区的土壤肥力。在这项研究中，应用主成分分析来提取最适合作物生产的土壤指标。使用多元线性回归 (MLR) 模型创建了茉莉花水稻土壤肥力的数字地图。最后，通过与泰国 Thung Kula Ronghai (TKR) 地区的实际水稻产量进行比较，确认了数字地图。计算的土壤肥力通过土壤 pH 值、电导率、有机质和常量营养素（P、K、Ca 和 Mg）含量显着表示。获得的该地区土壤肥力的MLR模型由光谱指数（亮度、着色、归一化差水和水分胁迫）和地形指数（坡度和地形湿度）组成。数字地图显示，TKR 地区一半以上的土壤肥力“非常低”到“低”。土壤肥力图非常适合 TKR 地区的实际水稻生产。