{'fraction_non_alphanumeric': 0.026299311208515967, 'fraction_numerical': 0.029430181590482152, 'mean_word_length': 5.2421875, 'pattern_counts': {'":': 0, '<': 2, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
[Research on remote sensing monitoring of soil salinization based on measured hyperspectral and EM38 data].

In the present study, the delta oasis between the Weigan River and the Kuqa River was selected as our study area. Firstly, the measured hyperspectral data related to different soil salinization extent was combined with electromagnetic induction instrument (EM38) in order to establish a soil salinization monitoring model; Secondly, by using the scaling transformation method, the model was adopted to calibrate the soil salinity index calculated from Landsat-TM images. Thirdly, the calibrated Landsat-TM images were used for the retrieval of regional soil salinity, and the retrieved data was verified based on the measured data. We found that at wavelengths of 456, 533, 686 and 1 373 nm, the interpretated data of EM38 were highly correlated with soil spectral reflectance (obtained via first order differentiation transformation of the spectra). Additionally, the soil salinity index model constructed from the combination of 456, 686 and 1 373 nm waveband was the best model among the different saliniza tion monitoring models. The authors' conclusion is that with R2 = 0.799 3 (p < 0.01), extracting the salinity information at regional scale by combining the electromagnetic and multispectral data performed better than those monitoring models with only salinity index extracted from multispectral remote sensing method (R2 = 0.587 4, p < 0 01). Our findings provides scientific bases for the future studies related to more accurate monitoring and prediction of soil salinization.