基于无人机的冬小麦拔节期表层土壤有机质含量遥感反演

快速监测大面积分布的盐渍化麦田土壤有机质含量,可为推进盐渍土改良和促进碳循环研究提供数据支撑。通过野外采样与获取无人机遥感影像,分别基于裸土和植被情况,采用多元线性回归(MLR)、偏最小二乘回归(PLSR)和支持向量机回归(SVR)3种方法,建立区域有机质含量遥感模型,并进行检验和对比,确定最优的土壤有机质含量反演模型;最后基于最优模型进行研究区表层土壤有机质的反演,并与插值结果进行比较。结果表明: 经5×5的中值滤波处理后的光谱与土壤表层有机质对应最优;3种模型中,SVR模型的预测精度最高,PLSR次之,MLR效果最差。对比两种变量的建模效果,基于植被的SVR建模效果最好,其建模决定系数(R²)、均方根误差(RMSE)分别为0.89、0.20,验证R²、RMSE分别为0.82、0.24;基于裸土的建模效果不理想,最优的也是SVR模型,其建模R²、RMSE分别为0.63、0.26,验证R²、RMSE分别为0.61、0.25。根据最优模型反演得到该区域有机质含量为17.51～22.53 g·kg^-1,平均值为19.51 g·kg^-1,与实地调查结果较为一致;插值结果与反演结果相比,精度受到限制。综上,基于无人机多光谱可以对盐渍土冬小麦拔节期土壤有机质含量进行快速、大范围精准估测。

Remote sensing inversion of surface soil organic matter at jointing stage of winter wheat based on unmanned aerial vehicle multispectral

The rapid monitoring of soil organic matter (SOM) content in large-scale salinized wheat fields can provide data for promoting research in saline soils and carbon cycle. Based on field sampling and remote sensing images of unmanned aerial vehicle, we established remote sensing prediction models of regional SOM using three methods, i.e., multiple linear regression (MLR), partial least squares (PLSR), and support vector machine regression (SVR) for bare land and wheat field, respectively. The models were validated and compared to identify the optimal inversion model of SOM. Moreover, the SOM in the area was inverted using the optimal model, with the inversion results being compared with the data by interpolation. The results showed that the spectrum after the filtering of 5×5 median was best related to surface SOM. Among the three models, the SVR model had the highest prediction accuracy, followed by the PLSR, while the MLR lowest. The SVR model was the best one for estimating wheat field, with coefficient of determination (R²) and root mean square error (RMSE) of 0.89 and 0.20, respectively, and the validated R² and RMSE were 0.82 and 0.24, respectively. The bare land SOM was also best fitted by the SVR model, with R² and RMSE were 0.63, 0.26, respectively, and the verified R² and RMSE were 0.61, 0.25, respectively, but without statistical significance. The inversion of the optimal model revealed that SOM content in this region ranged from 17.51 to 22.53 g·kg^-1, with an average of 19.51 g·kg^-1, which was generally consistent with the field measurement. Compared with the inversion results, the interpolation data were limited in accuracy. Overall, our study suggested that the unmanned aerial vehicle-based multi-spectral analysis could be applied to quick and accurate estimation of SOM content in saline soil at the jointing stage of winter wheat.