In this study,a rapid and non-invasive technology for predicting soil moisture content(SMC)was presented based on hyperspectral imaging(HSI).Firstly,a set of HSI system was developed to collect both spectral(400-1000 ...In this study,a rapid and non-invasive technology for predicting soil moisture content(SMC)was presented based on hyperspectral imaging(HSI).Firstly,a set of HSI system was developed to collect both spectral(400-1000 nm)and spatial(1620×841 pixels)information from sandy soil samples with variable SMC levels in the laboratory.Principal component analysis(PCA)transformation,K-means clustering,and several other image processing methods were performed to obtain a region of interest(ROI)of soil sample from the original HSI data.Then,256 optimal spectral wavelengths were selected from the average reflectance of the ROI,and 28 textural features were extracted using a gray-level co-occurrence matrix(GLCM).Data dimensionality reduction was conducted on both the spectral information and textural information by using a partial least square algorithm.Six latent variables(LVs)extracted from the spectral information,four LVs extracted from the textural information and fused data were used to build regression models with a three-layer BPNN,respectively.The results showed that all of the three calibration models achieved high prediction accuracy,particularly when using spectral information with R^(2)_(C)=0.9532 and RMSEC=0.0086.However,validation models demonstrate that predicting SMC using fused data is more effective than using spectral reflectance and textural features separately,with a R^(2)_(P)=0.9350 and RMSEP=0.0141,thus proving that the HSI technique is capable of detecting SMC.展开更多
基金This research was financially supported by International Science and Technology Cooperation Project of China Ministry of Agriculture(2015-Z44).
文摘In this study,a rapid and non-invasive technology for predicting soil moisture content(SMC)was presented based on hyperspectral imaging(HSI).Firstly,a set of HSI system was developed to collect both spectral(400-1000 nm)and spatial(1620×841 pixels)information from sandy soil samples with variable SMC levels in the laboratory.Principal component analysis(PCA)transformation,K-means clustering,and several other image processing methods were performed to obtain a region of interest(ROI)of soil sample from the original HSI data.Then,256 optimal spectral wavelengths were selected from the average reflectance of the ROI,and 28 textural features were extracted using a gray-level co-occurrence matrix(GLCM).Data dimensionality reduction was conducted on both the spectral information and textural information by using a partial least square algorithm.Six latent variables(LVs)extracted from the spectral information,four LVs extracted from the textural information and fused data were used to build regression models with a three-layer BPNN,respectively.The results showed that all of the three calibration models achieved high prediction accuracy,particularly when using spectral information with R^(2)_(C)=0.9532 and RMSEC=0.0086.However,validation models demonstrate that predicting SMC using fused data is more effective than using spectral reflectance and textural features separately,with a R^(2)_(P)=0.9350 and RMSEP=0.0141,thus proving that the HSI technique is capable of detecting SMC.
