Solar energy is one of the most widely used renewable energy sources in the world and its development and utilization are being integrated into people’s lives. Therefore, accurate solar radiation data are of great significance for site-selection of photovoltaic (PV) power generation, design of solar furnaces and energy-efficient buildings. Practically, it is challenging to get accurate solar radiation data because of scarce and uneven distribution of ground-based observation sites throughout the country. Many artificial neural network (ANN) estimation models are therefore developed to estimate solar radiation, but the existing ANN models are mostly based on conventional meteorological data; clouds, aerosols, and water vapor are rarely considered because of a lack of instrumental observations at the conventional meteorological stations. Based on clouds, aerosols, and precipitable water-vapor data from Moderate Resolution Imaging Spectroradiometer (MODIS), along with conventional meteorological data, back-propagation (BP) neural network method was developed in this work with Levenberg-Marquardt (LM) algorithm (referred to as LM-BP) to simulate monthly-mean daily global solar radiation (M-GSR). Comparisons were carried out among three M-GSR estimates, including the one presented in this study, the multiple linear regression (MLR) model, and remotely-sensed radiation products by Cloud and the Earth’s radiation energy system (CERES). The validation results indicate that the accuracy of the ANN model is better than that of the MLR model and CERES radiation products, with a root mean squared error (RMSE) of 1.34 MJ·m⁻² (ANN), 2.46 MJ·m⁻² (MLR), 2.11 MJ·m⁻² (CERES), respectively. Finally, according to the established ANN-based method, the M-GSR of 36 conventional meteorological stations for 12 months was estimated in 2012 in the study area. Solar radiation data based on the LM-BP method of this study can provide some reference for the utilization of solar and heat energy.

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.