Soil moisture is a key factor in the water and heat exchange and energy transformation of the ecological systems and is of critical importance to the accurate obtainment of the soil moisture content for supervising water resources and protecting regional and global eco environments. In this study, we selected the soil moisture monitoring networks of Naqu, Maqu, and Tianjun on the Qinghai–Tibetan Plateau as the research areas, and we established a database of surface microwave scattering with the AIEM (advanced integral equation model) and the mathematical expressions for the backscattering coefficient, soil moisture, and surface roughness of the VV and VH polarizations.We proposed the soil moisture retrieval models of empirical and machine learnings algorithms (backpropagation neural network (BPNN), support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) for the ascending and descending orbits using Sentinel-1 and measurement data, and we also validated the accuracies of the retrieval model in the research areas. According to the results, there is a substantial logarithmic correlation among the backscattering coefficient, soil moisture, and combined roughness. Generally, we can use empirical models to estimate the soil moisture content, with an R² of 0.609, RMSE of 0.08, and MAE of 0.064 for the ascending orbit model and an R² of 0.554, RMSE of 0.086, and MAE of 0.071 for the descending orbit model. The soil moisture contents are underestimated when the volumetric water content is high. The soil moisture retrieval accuracy is improved with machine learning algorithms compared to the empirical model, and the performance of the RF algorithm is superior to those of the other machine learning algorithms. The RF algorithm also achieved satisfactory performances for the Maqu and Tianjun networks. The accuracies of the inversion models for the ascending orbit in the three soil moisture monitoring networks were better than those for the descending orbit.

Investigating the energy and water vapor exchange in oasis riparian forest ecosystems is of significant importance to improve scientific understanding of land surface processes in extreme arid regions. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) provided many observations of water vapor and heat fluxes from riparian forest ecosystem by using a network of eddy-covariance (EC) systems installed over representative surfaces in the Ejina Oasis, which is located in the downstream areas of the Heihe River Basin, northwestern China. Based on EC flux measurements and meteorological data performed at five stations and covering representative surface types of Populus euphratica tree with associated Tamarix chinensis shrub, Tamarix chinensis shrubland, cantaloupe cropland, and barren-land, this study explored the spatio-temporal patterns of heat and water vapor fluxes over the Ejina Oasis riparian forest ecosystem with five different surface types over the course of a growing season in 2014. Energy balance closure of the flux data was evaluated; footprint analysis for each EC site was also performed. Results showed that energy balance closure for the flux data was reasonably good, with average energy balance ratio (EBR) of 1.03. The seasonal variations in net radiation (R_n), latent (LE), and sensible heat flux (H) over the five contrasting surfaces were similar, and a reverse seasonal change was observed in energy partitioning into LE and H. Remarkable differences in R_n, LE, and H between the five surfaces were explored preliminarily, associated closely with the soil properties and foliage phenology. Over the growing season (May–October) in 2014, the total ET ranged 622–731 mm for mixed forest of P. euphratica trees with associated T. chinensis shrubs with average daily ET of 3.6–4.2 mm; ET from T. chinensis shrubland was about 541 mm, with average daily ET of 3.6 mm. ET for barren-land was 195 mm. The total ET in irrigated cantaloupe cropland with plastic mulch was 431 mm for its four-month growing period with a total average of 3.8 mm d⁻¹. Determination of ET over riparian forest ecosystem helps to improve reasonable use of limited water resource in the Ejina Oasis.

Downward surface shortwave radiation (DSSR) plays an important role in the energy balance of the earth’s surface. Accurate estimate of DSSR is of great significance for the rational and effective use of solar energy. Some parameterization schemes were proposed to estimate DSSR using meteorological measurements given ground-based radiation observation sites are scare and uneven. With the development of remote sensing technique, remotely sensed data can be applied to obtain continuous DSSR in space. Commonly, the spatial resolution of most radiation products is relatively low and cannot meet the needs of certain fields. Moreover, some retrieval algorithms based on the radiation transfer models are complicated for non-professionals. In this study, a back-propagation (BP) neural network method with Levenberg–Marquardt (LM) algorithm (hereafter referred to as LM-BP) was applied to predict DSSR by building the relationship between measured DSSR and high-resolution remote sensing data from the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER). The DSSR observations from the four-component radiation sensor installed at the land covered by vegetable, village, maize, orchard, Gobi, sandy desert, desert steppe, and wetland were used to validate the model estimates. The results showed that the estimates of DSSR from LM-BP agreed well with the site measurements, with the root mean square error (RMSE) and the mean bias error (MBE) values of 27.34 W/m² and −1.59 W/m², respectively. This indicates that by combining the LM-BP network model and ASTER images can obtain precise DSSR in heterogenous surface. The DSSR results of this study can provide accurate high-spatial resolution input data for hydrological, evapotranspiration, and crop models.

Accurate quantification of evaporation loss from irrigation canals at an irrigation district scale is very useful for developing efficient irrigation strategies, particularly in water-deficient regions. The double-deck surface air layer (DSAL) model, a new aerodynamic method proposed by Kobayashi et al. (2013), is used for estimating the evaporation loss from a running water surface in irrigation canals. In this study, based on the long-term meteorological measurements made at automatic weather stations in 2013 and the field experiment conducted at - midstream areas of the Heihe River Basin (HRB), northwestern China, the DSAL model was applied to estimate the long-term evaporation loss from irrigation canals, which was the remarkable highlight of the study. The results showed that the rate of evaporation from irrigation canals exhibited a concave-up trend for the period from June to September, with higher values in June and September (20 to 50 mm day⁻¹) and lower values in July and August (around 10 mm day⁻¹). During the four months, for the Yingke and Daman irrigation districts in the Zhangye Oasis, the total water losses from irrigation canals due to evaporation were approximately 23.9 × 10⁵ m³ and 36.6 × 10⁵ m³, or 3.2% and 4.8% of the total amount of irrigation water, respectively. Results of the study are not only important for improving the irrigation water use efficiency, but also are beneficial to develop sustainable water resource management in the midstream areas of the HRB.

Soil moisture and salinity are crucial parameters of the Earth’s ecosystem; how to understand the radiation properties of them is of great significance for remote sensing monitoring. In this study, the application of mixed soil dielectric models (Dobson and generalized refractive mixing dielectric model (GRMDM)) and saline soil dielectric models (Dobson-S, HQR (Qingrong Hu), and WYR (Yueru Wu)) were analyzed to select the optimal models to simulate brightness temperature based on observational data. The brightness temperature of the soil moisture and multilevel salinity was simulated by using the Q-H (parameter of polarization mixing and parameter of characterizing height) model and Holmes parameterization scheme of soil effective temperature. The results show that both the Dobson model and the GRMDM model can well reproduce the real part and imaginary part of the dielectric constant of non-saline soil, and the GRMDM model was better. With the increase of the frequency, the simulation error of the dielectric constant of the saline soil by using the Dobson-S model, HQR model, and WYR model also increased, and the simulation result of the WYR model was better in the L band. The simulated result of the brightness temperature of soil moisture between the observation value and simulation value presented a high correlation both in the horizontal polarization and vertical polarization, with R greater than 0.967 and 0.948, and the root mean square error smaller than 3.998 K and 2.766 K, respectively. Meanwhile, the correlation coefficients of the brightness temperature of the saline soil in the horizontal polarization and vertical polarization were 0.935 and 0.971, and the root mean square errors were 5.808 K and 4.65 K, respectively. The brightness temperature decreased as the soil salinity increased, and the higher the salinity content was, the quicker the brightness temperature decreased. We expect that the experimental results can be used as a reference for algorithm developers to further enhance the accuracy of soil moisture and soil salinity retrievals.