Department of Soil Science, College of Agriculture, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
Soil Conservation and Watershed Management Research Department, Isfahan Agricultural and Natural Resources Research and Education Center, AREEO, Isfahan, 19395-1113, Iran.
Environ Monit Assess. 2023 Dec 18;196(1):51. doi: 10.1007/s10661-023-12212-7.
Soil quality (SQ) modeling and mapping is a leading research field aiming to provide reproducible and cost-effective yet accurate SQ predictions at the landscape level. This endeavor was conducted in a complex watershed in northern Iran. We classified the region into spectrally and topographically homogenous land units (average area of 48 ± 23 ha) using object-based segmentation analysis. Following the physicochemical analysis of soil samples from 98 stations, the Nemoro soil quality index (SQIn) was produced using the minimum dataset procedure and a non-linear sigmoid scoring function. SQIn values averaged 0.21 ± 0.06 and differed statistically between major land uses. To predict and map SQIn for each land unit, the best-performing regression model (F(3, 84) = 45.57, p = 0.00, R = 0.62) was built based on the positive contribution of the mean Landsat 8-OLI band 5, and negative influence of land surface temperature retrieved from Landsat 8-OLI band 10 and surface slope (t-test p-values < 0.01). Results showed that dense-canopy woodlands located in low-slope land units exhibit higher SQIn while regions characterized by either low-vegetation or steep-sloped land units had SQ deficits. This study provides insights into SQ prediction and mapping across spatially complex large-scale landscapes.
土壤质量 (SQ) 建模和制图是一个领先的研究领域，旨在以具有成本效益且准确的方式在景观水平上提供可重现的土壤质量预测。本研究在伊朗北部的一个复杂流域进行。我们使用基于对象的分割分析将该区域划分为光谱和地形同质的土地单元（平均面积为 48 ± 23 公顷）。在对 98 个站点的土壤样本进行理化分析后，使用最小数据集程序和非线性 S 型评分函数生成了 Nemoro 土壤质量指数 (SQIn)。SQIn 值的平均值为 0.21 ± 0.06，并且在主要土地利用类型之间存在统计学差异。为了预测和绘制每个土地单元的 SQIn，我们基于 Landsat 8-OLI 波段 5 的正贡献和 Landsat 8-OLI 波段 10 提取的地表温度和地表坡度的负影响（t 检验 p 值 < 0.01），构建了表现最佳的回归模型（F(3, 84) = 45.57，p = 0.00，R = 0.62）。结果表明，位于低坡度土地单元的茂密林地表现出较高的 SQIn，而植被较少或坡度较陡的区域则存在 SQ 缺陷。本研究为在空间复杂的大尺度景观中进行 SQ 预测和制图提供了思路。
