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Soil salinization is one of the main problems hindering the agricultural development in the arid and semi-arid regions. However, many previous studies of soil salinization monitoring failed to consider the impact of irrigation on soil salinity. To explore such impact, we used a UAV with a portable spectrometer to monitor the dynamic change of soil salinity before and after spring irrigation. In this study, 120 soil samples at the surface (0–10 cm) were taken in Shahaoqu Irrigation Area, Inner Mongolia, China, in mid-April (before spring irrigation) and mid-June (after spring irrigation), and the images of the four study zones A, B, C and D in this area were obtained from the UAV system. Based on these images, 25 spectral covariates (6 spectral bands, 16 spectral indices, and 3 two-dimensional indices) were calculated. Then, the sensitive spectral covariates were selected with such different variable selection methods as variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and genetic algorithm (GA). Finally, Multiple Linear Regression (MLR) and BP neural network algorithm (BPNN) was used to establish models for soil salinity inversion, based on which salinity maps of the study area were plotted. The results showed that the soil salinity of the study zone A (covered with vegetation) increased while that of the other three zones decreased after the spring irrigation. The general tendency was: the higher the original soil salinity was, the more obvious decrease the soil salinity had after the irrigation. All the three variable selection methods improved the inversion model accuracy, but the GA-BPNN model had the best performance (RP2 = 0.78, RMSEP = 0.16, and RPD = 2.13 before spring irrigation; and RP2 = 0.80, RMSEP = 0.14, and RPD = 2.26 after spring irrigation). It indicated that relatively accurate regional salinity maps could be plotted based on the spectral indices selected by GA and the salinity inversion model built on BPNN. These results have certain reference for soil salinization monitoring and farmland irrigation using UAV multispectral remote sensing. [ABSTRACT FROM AUTHOR]

Soil salinization is a serious restrictive factor affecting sustainable agricultural development. In order to explore the effect of Fractional Vegetation Cover (FVC), we monitored soil salinization in sites different vegetation coverage in Jiefangzha Irrigation District in Inner Mongolia using satellite remote sensing. From May to August 2018, we carried out field sampling at different depths in each month, and calculated FVC and spectral covariates using GF‐1 satellite images in the corresponding sampling period. Based on the FVC division criteria for Inner Mongolia, we took the following steps: (a) setting up a control treatment A (the full data with undivided FVC, TA) and experimental treatments B (bare land, TB), C (mid‐low FVC, TC), D (mid FVC, TD) and E (high FVC, TE); (b) conducting the Best Subset Selection (BSS) for all spectral covariates at different depths of each treatment; and (c) constructing the Soil Salt Content (SSC) inversion models using partial least square regression (PLSR), Cubist, and Extreme Learning Machine (ELM). The results indicated that (a) classifying FVC could improve the stability and predictive ability of the models; (b) the performance of the three modeling methods were different (Cubist was the best, ELM next and PLSR the poorest); (c) the optimal inversion models for TB, TC and TE were constructed by Cubist at 0–20, 0–40 and 0–20 cm, and for TD was constructed by ELM at 0–60 cm, respectively. The results can provide references for soil salinization prevention and agricultural production in Jiefangzha Irrigation District and other areas with the similar vegetation cover. [ABSTRACT FROM AUTHOR]

• Fractional-order derivative algorithm was used to process soil VIS-NIR spectroscopy. • The correlation coefficient with most soil salt ions were best at 0.80–0.90 order. • Three-dimensional indices were used to magnify the soil salinity information. • Iteratively retaining informative variables algorithm had the best effect. • Mg2+ showed the highest estimation accuracy, with an RPD and RPIQ up to 3.81 and 3.71 respectively. This study aimed to improve the potential of visible-near infrared (VIS-NIR) spectroscopy in predicting the contents of salt and major soluble ions in the topsoil at Hetao Irrigation District in Inner Mongolia, China. A total of 120 soil samples (sampling depth is 0–20 cm) were collected from the field. Fractional-order derivatives (FODs) (with intervals of 0.05 and range of 0–2) were used for soil spectral pretreatment. Coefficient of determination (R2) was used to determine the optimal FOD spectrums of the soil salt and major soluble ions. Extreme learning machine (ELM) models were calibrated with such spectral parameters as band reflectance, newly constructed two-band indices, and three-band indices from the optimal FOD spectrum, and the models were subsequently applied to estimate the contents of soil salt and soluble ions. Different algorithms including Monte Carlo uninformative variable elimination (MCUVE), iteratively retaining informative variables (IRIV), and bootstrapping soft shrinkage (BOSS) were applied to variable selection. The results indicated that the correlation between the contents of soil salt and soluble ions and FOD spectra increased as the derivative order increased. The optimal FOD spectrum of soil salt content (SSC), K+, Na+ and SO 4 2− were the 0.9-order derivative spectrum while the optimal FOD spectrum of Ca2+, Mg2+, CO 3 2–, HCO 3 – and Cl− were 1.65, 0.85, 1.90, 0.80 and 1.55 order derivative spectrum, respectively. The newly proposed three-band index (TBI4) showed a better correlation with ions content (e.g., TBI4 of Mg2+ had the best effect, and its R2 was 0.92). In most cases, the IRIV-ELM models outperformed MCUVE-ELM and BOSS-ELM models in estimating the contents of soil salt and soluble ions. Difference existed among the optimal prediction accuracy of the contents of soil salt and different ions: the prediction accuracy of SSC, Ca2+, Na+, Mg2+, Cl− and SO 4 2− was very high while that of CO 3 2− was high and K+ and HCO 3 2− was relatively lower. However, these models have reached and even exceeded the quantitative prediction level. Therefore, we concluded that the contents of soil salt and soluble ions could be estimated using FOD algorithm and the optimal combination of spectral indices. [ABSTRACT FROM AUTHOR]

[Display omitted] • Different window sizes will affect the monitoring accuracy of soil moisture; • It is feasible to select the optimal window size with the local variogram; • The window size of 13 * 13 is the optimal window size applicable in the study area. Soil moisture content monitoring with UAV remote sensing always involves the selection of an appropriate window size for spectral information extraction, but research on the effect of window size on the accuracy of soil moisture content monitoring models and the selection of the optimal window size has been rarely reported. To solve these problems, an experiment was conducted on three typical bare plots in Shahaoqu Experimental Station at Hetao Irrigation District, Inner Mongolia, China. First, remote sensing images were obtained from the three bare plots from April 15 through 17, 2019, with a six-rotor UAV equipped with a six-channel multispectral camera. Synchronously, the moisture content at 0–10 cm of the surface soil was measured using the drying method. Then, the spectral information was extracted through windows of 16 different sizes (ranging from 1 * 1 to 31 * 31). Followed was the construction of thirty spectral indices using the ratio and normalized ratio methods, and the processing of the constructed indices using principal component analysis. The principal components accounting for 95% of the cumulative contribution rate were selected as the input variables for the construction of the monitoring models based on BP neural network. Finally, the model accuracy was tested using ANOVA, and the local variogram of the spectrum was used to explore the optimal window size selection. The results demonstrated: (1) There are differences in the spectral information extracted from different sizes of windows, which affects the accuracy of soil moisture monitoring model; (2) The spatial autocorrelation threshold of the plots at the local variogram was 13 * 13, resembling the window size with the highest accuracy, so it is feasible to select the optimal window size with the local variogram; (3) As the window size of spectral information increased, R2 first increased and then decreased, reaching the maximum value of 0.261 at the size of 13 * 13, and RMSE first decreased and then increased, reaching the minimum value of 0.017 when at the size of 7 * 7. These results can provide some reference for window size selection in spectral information extraction to monitor soil moisture content. [ABSTRACT FROM AUTHOR]