Accurate diagnosis of water and salt information in saline agricultural lands is crucial for long-term soil quality improvement and arable land conservation. In this study, we extracted field-scale vegetation canopy spectral information by UAV hyperspectral information, transforming the reflectance (R) to standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative of reflectance (FDR) and second derivative of reflectance (SDR). We determined the optimal spectral transformation forms of soil water content (SWC), soil pH, and soil salt content (SSC) by the maximum absolute correlation coefficient (MACC), and extracted the feature bands by competitive adaptive reweighted sampling (CARS). We constructed an inversion model of soil water and salt information by partial least squares regression (PLSR), random forest (RF), and extreme gradient boosting (XGBoost). The results showed that R, FDR and MSC were the best spectral transformation types for soil water content, soil pH, and soil salt content, and the corresponding MACC were 0.730, 0.472 and 0.654, respectively. The CARS algorithm effectively eliminated the irrelevant variables, optimally selecting 16-17 feature bands from 150 spectral bands. Both soil water content and soil pH performed best with XGBoost model, achieving determination coefficient of validation (Rp2) 0.927 and 0.743, and the relative percentage difference (RPD) amounted to 3.93 and 2.45. For soil salt content, the RF model emerged as the best inversion method with Rp2 and RPD of 0.427 and 1.64, respectively. The study could provide a reference solution for the integrated remote sensing monitoring of soil water and salt information in space and sky, serving as a scientific guide for the amelioration and sustainable management of saline lands.
精准诊断盐碱农田水盐信息有助于保护耕地面积、长效提升土壤地力。本研究基于无人机高光谱数据提取田块尺度植被冠层光谱信息,利用标准正态变量(SNV)、多元散射校正(MSC)、一阶微分(FDR)和二阶微分(SDR)分别对原始光谱反射率(R)进行数学变换,通过最大相关系数绝对值(MACC)确定土壤含水量(SWC)、pH值和含盐量(SSC)的最优光谱变换形式,并采用竞争性自适应重加权采样法(CARS)对其进行特征波段提取,基于偏最小二乘回归(PLSR)、随机森林(RF)和极端梯度提升(XGBoost)建立土壤水盐信息反演模型。结果表明: 土壤含水量、pH值和含盐量分别以R、FDR和MSC为最佳光谱变换形式,所对应的MACC分别为0.730、0.472和0.654。CARS算法能有效剔除无关变量,从150个光谱波段中优选出16～17个特征波段。土壤含水量和pH值均以XGBoost模型表现最佳,模型验证决定系数(Rp2)分别达0.927和0.743,相对分析误差(RPD)分别达3.93和2.45;土壤含盐量以RF模型为最优反演方法,Rp2和RPD分别为0.427和1.64。本研究结果可为土壤水盐信息空天地一体化遥感监测提供参考方案,为盐碱地改良和保护性耕作提供科学依据。.
Keywords: UAV remote sensing; competitive adaptive reweighted sampling; extreme gradient boosting.; hyperspectrum; random forest.