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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]

Accurate, timely, and continuous soil moisture information is helpful for crop stress diagnosis and irrigation management decision. OPtical TRApezoid Model (OPTRAM) based on optical satellite data has been proven to be an effective method for assessing soil moisture status. However, the applicability of OPTRAM to small-scale field soil moisture assessment remains to be explored. In this study, we propose a strategy for the genetically parameterized OPTRAM and evaluate its applicability on Unmanned Aerial Vehicle (UAV) high-resolution hyspectral data. The results showed that: (1) When OPTRAM was used to genetically parameterized with PROSAIL generated dataset, 46 characteristic narrowband bands (|R|= 0.52–0.78) were determined in the spectral region of near infrared (NIR) (750–850 nm) and SWIR (1060–1080 and 1450–1500 nm); (2) By fine-tuned soil moisture estimation model using transfer learning strategy, the reliable soil moisture estimation was achieved in three crops (R2=0.57–0.64; RMSE=0.008–0.022 m3m−3);(3) Compared to soil moisture estimation model using a single spectral region (NIR or SWIR), the DSWC model that combine NIR and SWIR was more effective for tracking soil moisture; (4) The scale effect was observed when the fine-tuned soil moisture estimation model was applied on the high-resolution UAV images. The model performance was stable in pixel size of 1–7 cm and began to drop at pixel size of 11 cm. The above results advance the application of OPTRAM on small farmland soil moisture assessment and demonstrate the application potential of OPTRAM on narrow-band hyperspectral data. This study provides a new candidate for the use of hyperspectral data to estimate soil moisture, and scientific support for precision agriculture and irrigation scheduling. • OPtical TRApezoid Model was genetically parameterized by PROSAIL. • Estimation model effectively integrates deep network with OPtical TRApezoid Model. • Transfer learning presents opportunity for cross-species soil moisture estimation. [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]

• 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]

• The root water uptake in water-repellent soils of summer maize were simulated using HYDRUS-1D. • The root water uptake rates and cumulative RWU decreased with the increase of initial WDPT values. • After 30 DAS, higher E a was observed in WR soils compared to CK. Soil water repellency (SWR) is an ubiquitous soil property, that has major effects on surface and subsurface water flow, soil erosion, and therefore also affects plant growth and development. Soil water repellency has been recently observed to decrease summer maize growth, especially when the initial persistence of soil water repellency was high. However, mechanics implicated in maize biomass limitations are still unclear due to limited field observations and measurements of plant water use. This research aims to explain the possible mechanisms that impede the growth of summer maize in water repellent soils by examining soil water content, soil water evaporation and root water uptake (RWU) and comparing the measurements to simulations using HYDRUS-1D. Data from five increasing levels of SWR (CK, WR1, WR2, WR3 and WR4) from a two-year experiment were used. The soil hydraulic parameters described with the van-Genuchten model were calibrated inversely based on the observed volumetric soil water content at 26, 47 and 68 days after sowing (DAS) in 2016 and validated by water content values on 33, 62 and 91 DAS in 2017. The results showed the daily and cumulative RWU values ranked in an order of CK > WR1 > WR2 > WR3 > WR4. The RWU rates and cumulative RWU decreased with an increase of initial water droplet penetration time (persistence), which indicated weaker RWU ability of summer maize in the water repellent treatments. Thirty days after sowing, higher evaporation was observed in water repellent soils compared to CK. Weak RWU of summer maize grown in the water repellent soils and strong soil water evaporation were found. This research demonstrates the possible mechanisms that impede summer maize growth in water repellent soils could include weak RWU and high evaporation. [ABSTRACT FROM AUTHOR]

• The working pressure head of subsurface irrigation system with ceramic emitters must be higher than 20 cm. • It is better to use the soil water content uniformity to evaluate the irrigation quality of SICE. • The discharge deviation in soil is less than that in air when working pressure head is higher than 20 cm. A subsurface irrigation system with ceramic emitters (SICE) without a pump has been developed to limit energy consumption and reduce greenhouse gas emissions. Yet whether SICE can be used in low-pressure conditions has not been tested; moreover, there is no index for evaluating the irrigation quality of SICE. Laboratory experiments, with six treatments, were conducted to study ceramic emitter hydraulic characteristics in the air and soil under different working pressure heads and emitter types. The results indicated that when H increased, the emitter discharge increased linearly, and the discharge deviation decreased in the air. With increased H in the soil, the emitter discharge, soil water content, and soil water content uniformity increased, and the discharge deviation decreased. When H was greater than or equal to 20 cm, the discharge deviation in the soil was less than that in the air, and the soil water content uniformity was higher than 80 %. The soil water content uniformity could be used in the evaluation of the irrigation quality of SICE based on the reliability and convenience of observation. To make the best use of soil water potential on the outflow of the emitter, reduce the discharge deviation, and improve soil water content uniformity, the working pressure head of SICE should be higher than 20 cm. [ABSTRACT FROM AUTHOR]