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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 moisture and salinity are both important environmental variables for crop growth in agricultural production areas. Optical remote-sensing datasets from different sensors are available for estimating soil moisture and salinity from different spatial-temporal scales. Given the co-regulation of soil spectral reflectance (SR) by soil moisture and salinity, the simultaneous estimation of moisture and salinity in saline soil may result in great bias and uncertainty. To address this problem, soil samples were collected in the salinized area during irrigation. Synchronously, processed multi-spectral images were acquired from Sentinel-2 satellite. The spectrum mechanism responsive to soil moisture and salinity was verified by statistical tests, and its corresponding mathematical model (MSS model) was developed to identify the dominant factors affecting SR and to inverse moisture and salinity. The result showed that the effects of moisture and salinity were temporally constant (facilitation) and changing (from inhibition to facilitation), respectively, during the irrigation stages. The dominant factors in the variation of SR shifted from salinity and moisture-salinity interaction to moisture. Reliable accuracy was achieved in the moisture and salinity estimation using inverse MSS model. The profile from the series of estimations can further reveal the dynamic changes of soil moisture and salinity content during irrigation, and provide guidance for local irrigation management. • MSS model simulates response of soil spectrum to soil moisture and salinity. • Inverse MSS model achieves the simultaneous estimation at regional scale. • Spatial-temporal change of soil moisture and salinity is observed during irrigation. • Land-vegetation alternation causes soil salt aggregation in crop field. [ABSTRACT FROM AUTHOR]

• Sensetivity of SIF to soil salt varies globally with land uses. • SIF has limitations on herbaceous cover and sparse vegetation. • Globally, 54.98 Mha salt-affected soils were observed during 2000～ 2020. • The United States and China are the two countries most affected by soil salinity. The accumulation of soil salt becomes a worldwide widespread phenomenon, being a major threat to global production. As an environmental stress, soil salinity can reduce the vegetation photosynthetic activity. Solar-induced chlorophyll fluorescence (SIF) is an electromagnetic signal actively released by vegetation during photosynthesis. SIF not only can capture lower vegetation photosynthetic activity due to environmental stress promptly, but also is less affected by atmosphere and soil background. However, the ability of SIF observation to detect soil salinity remains unclear. Here, we use standardized solar-induced chlorophyll iluorescence index (SIFI) from SIF observation time series (2000 ∼ 2020) of global OCO-2 based SIF product (GOSIF) to develop soil salinity model. The results show that: SIF observation can identify salt affected soil (EC ≥ 2 ∼ 4 dS m−1) and salinity class a global scale. The SIFI calculated from SIF observation at May ∼ August (hereafter SIFI 5-8) is the optimal sensitivity indices for rainfed cropland, herbaceous cover, irrigated cropland, shrubland, grassland. SIFI 10-11 is the optimal sensitivity indices for forest and sparse vegetation; (2) By comparison, the ovrerall classification accuracy of predicted salinity class is above 70 %. The order of classification accuracy is rainfed cropland > irrigated cropland > bare area > forest > grassland > shrubland > herbaceous cover > sparse vegetation; (3) During at least three-quarters of the period from 2000 to 2020, the area of salt affected soil (EC ≥ 2 ∼ 4 dS m−1) was 4.9 Mkm2; (4) The annual change rate of global soil salt content is generally between −0.05 and 0.05 dS m−1 yr−1. Soil salinity in South Africa and West Asia increased greatly with an annual change rate of 0.02 ∼ 0.03 dS m−1 yr−1. These results demonstrate the ability of SIF observation to estimate soil salinity, providing a new perspective for explaining and evaluating soil salinity variation. [ABSTRACT FROM AUTHOR]

• Modified biochar decreased pH of salt-affected soils in an intercropping system. • Oversupply of biochar had negative impact on soil water and crop yield. • A 10 t ha−1 biochar application rate was recommended to improve soil and yields. Biochar application in agricultural salt-affected soils has shown strong potential to amend soil and promote production. The effect of biochar on soil properties and crop yield varies with crop, soil, biochar properties and climate. Thus, it is critical to select suitable biochar and their application amounts for ameliorating salt-affected soils while improving its conditions and crop yields. The objectives of this study were to investigate the effect of biochar application rates on soil properties, water and temperature conditions and crop yields in saline-alkali soils under cotton-sugarbeet intercropping. We used a three-year (2018–2020) field experiment with biochar application rates of 0, 10, 50 and 100 t ha−1 in 2018, 0, 10, 25, 50 and100 t ha−1 in 2019 and 0, 10, 25 and 30 t ha−1 in 2020. Soil bulk density decreased and thus porosity increased with application of biochar. Soil pH decreased with increasing biochar application amount and the rate of change ranged from −0.003 to −0.004 per ton of biochar. Biochar application at 10 t ha−1 increased soil water content (SWC) and weighted planar soil water storage (WPSWS) in all three experimental years, while oversupply of biochar decreased SWC. Application of biochar moderated soil temperature fluctuations. The yield of cotton and sugarbeet first increased and then decreased with increasing amount of biochar. Considering the effects of biochar on soil water, temperature, and crop yields, 10 t ha−1 biochar amount was proposed as an appropriate application rate for saline-alkali soils. The results provided valuable information on appropriate biochar rates to amend salt-affected soils and their properties while increase crop yields. [ABSTRACT FROM AUTHOR]