In order to invert the surface soil moisture in the underground mining disturbance area,taking the 52501 working face of Daliuta Coal Mine as an example.An unmanned aerial vehicle(UAV)equipped with an imaging spectrometer was used to acquire hyperspectral images.The obtained spectral data underwent logarithmic,reciprocal logarithmic,first-order,and envelope removal transformations.Combining these data with 128 ground-collected soil moisture samples,partial least squares regression(PLSR)and least squares support vector machine(LSSVM)models were constructed to predict soil moisture content and validate their predictive accuracy.The results indicate that the PLSR model and LSSVM model based on the first-order transformation exhibit relatively good predictive accuracy.For the first-order transformation PLSR model,the coefficient of determination for the modeling set R2c is 0.702 1,and for the prediction set R2p is 0.640 5,with root mean square errors of calibration(RMSEc)and prediction(RMSEp)being 1.638 4%and 1.103 4%,respectively,and relative prediction error(RPDp)of 1.726 3.For the first-order transformation LSSVM model,the modeling set R2c is 0.812 5,and the prediction set R2p is 0.597 9,with RMSEc and RMSEp being 1.275 5%and 1.345 9%,respectively,and RPDp of 1.632 3.Ultimately,based on the PLSR and LSSVM models,soil moisture mapping was completed,achieving spatial prediction of soil moisture.This provides spatial data support for the precise enhancement of soil moisture in vegetation-guided restoration efforts in the study area.
关键词
土壤含水量/高光谱/偏最小二乘回归/最小二乘支持向量机/无人机/干旱阈值/引导修复
Key words
soil moisture content/hyperspectral/partial least squares regression/least squares support vector machine/unmanned aerial vehicle/aridity threshold/guided restoration
维普
万方数据