数字农科院2.0

基于无人机影像分层建模的土壤深层盐渍化空间预测

文献类型： 中文期刊

关键词： 无人机遥感;;土壤盐渍化;;深度;;混合模型;;随机森林;;克里金插值;;盐分预测

期刊名称：中国农业科学

ISSN： 0578-1752

年卷期： 2025 年

页码：

收录情况： 北大核心（2023版） ; ; CSCD（2025-2026年度） ; ; 科技核心（2024版） ; ; 农林核心（2024版）

摘要： 摘要：【背景】土壤盐渍化严重制约作物生长和生态平衡，精准监测对盐碱地改良利用、产量预测及农田管理至关重要。在自然和人为等多种因素影响下，土壤盐渍化主要受水分和盐分在土体中迁移与再分配的水盐运动过程控制，呈现垂直迁移显著、空间异质性大的特点。目前无人机遥感技术已经广泛用于田块尺度的土壤盐渍化监测，但是，遥感技术多进行表层监测，难以表征深层盐分梯度。【目的】融合机器学习与克里金插值方法，建立无人机影像分层建模的土壤深层盐渍化空间预测方法。【方法】首先，利用无人机搭载多光谱传感器获取试验田高分辨率影像，并同步测量不同深度土壤盐分数据，辅以实时动态差分定位技术确保空间精度；随后，构建包含红边波段在内的光谱特征集，并基于随机森林算法进行特征优选；在此基础上，融合机器学习与克里金插值方法，建立分层土壤盐分预测模型，生成高分辨率盐分分布图；最后，通过与三次拟合深度函数预测方法对比验证本方法在深层盐渍化空间表征中的优势。【结果】混合模型分层建模的土壤深层盐渍化空间预测各深度预测精度R2分别为 0.68（0—10 cm）、0.51（10—20 cm）、0.58（20—40 cm）、0.56（40—60 cm）与 0.52（60—80 cm），其中 0—10 cm 表层预测效果最佳。红边盐分指数在各深度均表现为重要预测因子，验证了所构建红边指数的适用性与有效性。通过混合模型与三次拟合深度函数预测的结果对比，混合模型分层建模的土壤深层盐渍化空间预测精度更高，更能真实地反映试验区不同深度的盐渍化程度。【结论】无人机遥感技术在浅层（0—10 cm）土壤盐分预测最优，土壤性质的预测精度随深度的增加而下降，深层精度仍需提升；从预测结果来看，随着深度增加，土壤盐分均值逐渐升高，表明盐分在土壤剖面中存在累积现象；相较于三次拟合深度函数方法，本研究提出的基于随机森林分层建模与克里金残差校正的混合模型在各土层展现出更高的空间预测精度，更具合理性与实用性，为区域土壤盐渍化动态监测及分层盐分精准制图提供了科学依据。

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