Plastic film mulching has been widely used to improve crop yield and water use efficiency, although the effects of plastic film mulching on water, heat, nitrogen dynamics, and crop growth are rarely presented comprehensively. This study investigated a large number of studies in film mulching fields from the past 10 years (mostly from 2019 to 2023) and summarized the impact of plastic film mulching, progress in modeling with film mulching, and future research directions. The effects of plastic film mulching were intricate and were influenced by film mulching methods, irrigation systems, crop types, crop growth stages, etc. Overall, plastic film mulching showed a positive effect on improving soil water, temperature, and nitrogen status, enhancing crop transpiration and photosynthetic rates, and promoting crop growth and yield, although film mulching may have negative effects, such as increasing rainfall interception, blocking water entering the soil, and reducing net radiation income. The crop yield and water use efficiency could increase by 39.9–84.7% and 45.3–106.4% under various film mulching methods. Coupled models of soil water and heat transport and crop growth under plastic film mulching conditions have been established by considering the effects of plastic film mulching on the upper boundary conditions of soil water and heat, energy budget and distribution processes, and the exchange of latent and sensible heat between soil and atmosphere. The models have good applicability in film mulched farmland of maize, rice, and potato for different regions of China. Further development is needed for soil water, heat, nitrogen migration, and crop growth models under different plastic film mulching methods, and the acquisition of soil and crop indicators under plastic film mulching conditions based on big data support. The study will provide reference for the subsequent development and innovation of plastic film mulching technology.

Maize production in the arid and semi-arid regions of northwest China is limited by water scarcity, while the abundance of brackish water highlights the opportunity for its effective and sustainable utilization. A 2-year field experiment (2022–2023) was conducted in the Shiyang River Basin to investigate the impacts of deficit irrigation with brackish water on soil moisture, salinity, temperature, crop growth index, yield, and water productivity. Six treatments were implemented, consisting of two irrigation levels (W1: 75%I, W2: 100%I) and three water quality gradients (S0: 0.7 g L⁻¹, S1: 3.7 g L⁻¹, S2: 5.7 g L⁻¹ in 2022 and 7.7 g L⁻¹ in 2023). Results indicated that brackish irrigation (except S0) increased soil salinity, keeping the soil water storage at higher levels, while decreased maize yield, and water productivity (WP). Compared with full irrigation at the same salinity level, deficit irrigation decreased soil salinity, keeping the soil water storage at lower levels, while increasing soil temperature, which led to lower maize yield but resulted in higher WP. Path analysis of soil hydrothermal salinity and crop growth indicators demonstrated that soil salinity changes play a crucial role in determining maize plant height and yield. S0W2 (100% irrigation, 0.7 g L⁻¹) achieved the highest maize yield, with S0W1 yielding 5.15% less. However, the water productivity (WP) of S0W1 was 17.66% higher than that of S0W2. Therefore, considering the combined factors of maize yield, water productivity, and water-saving benefits, the use of S0W1 (75% irrigation, 0.7 g L⁻¹) is recommended.

Soil salinization has become one of the main factors restricting sustainable development of agriculture. Field spectrometry provides a quick way to predict the soil salinization. However, soil moisture content (SMC) seriously interferes with the spectral information of saline soil in arid areas. It is vital to establish a model that is insensitive to SMC for potential in situ field applications. The soil spectral reflectance exponential model (SSREM) has been widely employed for reflectance simulation and SSC inversion. However, its reliability for saline soils with high SMC has not been verified yet. Based on hyperspectral remote sensing data (400~1000 nm) on 459 saline soil samples in Shiyang River Basin of Northwest China, we investigated the role of SMC and SSC in soil spectral reflectance from 29 October 2020 to 22 January 2021. Targeted at saline soils, soil spectral moisture threshold (MT) was introduced to improve the SSREM toward a modified spectral reflectance exponential model (MT-SSREM). The bands that are sensitive to SSC but not sensitive to SMC were obtained based on a method of correlation analysis between original spectra, four kinds of spectral data, and SSC. SSREM and MT-SSREM were finally applied to inversely estimate SSC. Results show that wavelengths at 658~660, 671~685, 938 nm were suitable for SSC estimation. Furthermore, although SSREM was able to simulate the spectral reflectance of most saline soils, its simulation accuracy was low for saline soil samples with high SMC (SMC > MT(i), $400 \mathrm{nm}\le i\le 1000 \mathrm{nm}$), while MT-SSREM performed well over the whole range of SMC. The simulated spectral reflectance from MT-SSREM agreed well with the measured reflectance, with the R² being generally larger than 0.9 and RMSE being less than 0.1. More importantly, MT-SSREM performed substantially better than SSREM for SSC estimation; in the statistical performance of the former case, R² was in range of 0.60~0.66, RMSE was in range of 0.29~0.33 dS m⁻¹; in the latter case, R² was in range of 0.10~0.16, RMSE was in the range of 0.26~0.29 dS m⁻¹. MT-SSREM proposed in this study thus provides a new direction for estimating hyperspectral reflectance and SSC under various soil moisture conditions at wavelengths from 400 to 1000 nm. It also provides an approach for SSC and SMC mapping in salinization regions by incorporating remote sensing data, such as GF-5.