Hyperspectral Estimation Of Soil Salt Content In Coastal Saline-Alkali Farmland

The detection of soil salinity is the key to the evaluation of soil salinization.The conventional method of salt detection is drying method,but it is difficult to meet the needs of modern precision agriculture rapid large-scale monitoring.Optical remote sensing provides a means for rapid and large-scale measurement of soil surface salinity,but it is limited by satellite cycle,weather and other factors,which affect the timeliness and accuracy of monitoring.Near-earth Hyper spectral technique brings a feasible method to solve the above problems.At present,the research on Hyper spectral inversion of soil properties is mainly focused on the surface layer of soil,and a lot of research results have been obtained,it can not meet the requirements of precision agriculture for multi-layer soil properties.In this study,Wudi County of Shandong province was taken as the research area,and the soil spectra were processed by coupling conventional mathematical transform and continuous wavelet method,using PLS,BP neural network and random forest method,a Hyper spectral direct estimation model of soil salinity in different depth layers of saline-alkali cultivated land was constructed and screened,and based on the correlation of soil salinity in surface layer(0-20cm),middle layer(20-40cm)and bottom layer(40-60cm),to explore the indirect estimation method of salt content in middle and bottom soil.The main findings of the study are as follows:(1)The Hyper spectral characteristics of different soil layers of cultivated land were analyzed.By performing breakpoint correction and SG smoothing on the spectrum,the reflectance of the surface,middle,and bottom layers of soil is mainly concentrated between 0.1-0.6,0.1-0.7,and 0.1-0.8,with a trend of increasing from top to bottom.The trend of spectral reflectance curves in each soil layer is basically consistent,showing an upward trend in the451-1350nm wavelength range,a gentle trend in the 1460-1480nm range,and a downward trend in the 2100-2200nm range.(2)The correlation between soil spectral reflectance and salt content was analyzed.After conventional mathematical transformation,the correlation between soil spectra and salt content was obviously enhanced,and the correlation coefficient was obviously increased,and there were more intensive reflection peaks and absorption valleys The results show that the conventional transformation can effectively enlarge the absorption characteristics of visible and near infrared spectra,and enhance the correlation between soil spectral reflectance and salinity.(3)The research scale and characteristic factors of salt content in different soil layers were determined.After performing continuous wavelet transform on the spectra obtained from conventional mathematical transformations,the optimal decomposition scale for each layer of soil spectra was determined through correlation analysis,and then the characteristic bands were selected.The optimal decomposition scale for the surface layer is the 1st,2nd,7th,and8th scales,with characteristic bands of 404nm,458nm,837nm,841nm,1102nm,and1749nm;The optimal decomposition scale for the middle layer is the 4th,5th,and 8th scales,with characteristic bands of 805nm,816nm,825nm,827nm,834nm,837nm,838nm,and839nm;The optimal decomposition scale for the bottom layer is the third scale,with characteristic bands of 191nm,192nm,and 196nm.(4)Establish and screen the best direct and indirect estimation models of salt content in different soil layers.PLS,BP neural network and random forest methods are used to directly and indirectly invert the salt content of each soil layer,so as to select the best estimation model of salt content of different soil layers.The modeling accuracy of random forest method is the highest,and the R~2of the best model for direct estimation of surface,middle and bottom layers is0.9100,0.9047 and 0.9114 respectively;The R~2of the optimal model for indirect estimation in the middle and bottom layers is 0.8802 and 0.8737,respectively.The research results indicate that the soil salinity of coastal saline alkali farmland can be directly modeled and inverted on the surface layer,and indirectly estimated on the middle and bottom layers.Without considering other factors,this provides a feasible method for exploring time-saving and labor-saving rapid monitoring of farmland salinization.