Evaporation (E) and transpiration (T) information is crucial for precise water resources planning and management in arid and semiarid areas. Two-source energy balance (TSEB) methods based on remotely-sensed land surface temperature provide an important modeling approach for estimating evapotranspiration (ET) and its components of E and T. Approaches for accurate decomposition of the component temperature and E/T partitioning from ET based on TSEB requires careful investigation. In this study, three TSEB models are used: (i) the TSEB model with the Priestley-Taylor equation, i.e., TSEB-PT; (ii) the TSEB model using the Penman-Monteith equation, i.e., TSEB-PM, and (iii) the TSEB using component temperatures derived from vegetation fractional cover and land surface temperature (VFC/LST) space, i.e., TSEB-T_C-T_S. These models are employed to investigate the impact of component temperature decomposition on E/T partitioning accuracy. Validation was conducted in the large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research-Multi-Scale Observation Experiment on Evapotranspiration (HiWATER-MUSOEXE) in the northwest of China, and results showed that root mean square errors (RMSEs) of latent and sensible heat fluxes were respectively lower than 76 W/m² and 50 W/m² for all three approaches. Based on the measurements from the stable oxygen and hydrogen isotopes system at the Daman superstation, it was found that all three models slightly overestimated the ratio of E/ET. In addition, discrepancies in E/T partitioning among the three models were observed in the kernel experimental area of MUSOEXE. Further intercomparison indicated that different temperature decomposition methods were responsible for the observed discrepancies in E/T partitioning. The iterative procedure adopted by TSEB-PT and TSEB-PM produced higher LE_C and lower T_C when compared to TSEB-T_C-T_S. Overall, this work provides valuable insights into understanding the performances of TSEB models with different temperature decomposition mechanisms over semiarid regions.

Climate change and anthropogenic activities, including agricultural irrigation have significantly altered the global and regional hydrological cycle. However, human-induced modification to the natural environment is not well represented in land surface models (LSMs). In this study, we utilize microwave-based soil moisture products to aid the detection of under-represented irrigation processes throughout China. The satellite retrievals used in this study include passive microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and its successor AMSR2, active microwave observations from the Advanced Scatterometer (ASCAT), and the blended multi-sensor soil moisture product from the European Space Agency (i.e., ESA CCI product). We first conducted validations of the three soil moisture retrievals against in-situ observations (collected from the nationwide agro-meteorological network) in irrigated areas in China. It is found that compared to the conventional Spearman’s rank correlation and Pearson correlation coefficients, entropy-based mutual information is more suitable for evaluating soil moisture anomalies induced by irrigation. In general, around 60% of uncertainties in the anomaly of “ground truth” time series can be resolved by soil moisture retrievals, with ASCAT outperforming the others. Following this, the potential utility of soil moisture retrievals in mapping irrigation patterns in China is investigated by examining the difference in probability distribution functions (detected by two-sample Kolmogorov-Smirnov test) between soil moisture retrievals and benchmarks of the numerical model ERA-Interim without considering the irrigation process. Results show that microwave remote sensing provides a promising alternative to detect the under-represented irrigation process against the reference LSM ERA-Interim. Specifically, the highest performance in detecting irrigation intensity is found when using ASCAT in Huang-Huai-Hai Plain, followed by advanced microwave scanning radiometer (AMSR) and ESA CCI. Compared to ASCAT, the irrigation detection capabilities of AMSR exhibit higher discrepancies between descending and ascending orbits, since the soil moisture retrieval algorithm of AMSR is based on surface temperature and, thus, more affected by irrigation practices. This study provides insights into detecting the irrigation extent using microwave-based soil moisture with aid of LSM simulations, which has great implications for numerical model development and agricultural managements across the country.

Reclaimed water is useful for replenishing dried up rivers in North China, although changes in water quality could be an issue. Therefore, it is essential to understand the spatio-temporal variation and the controlling factors of water quality. Samples of Yongding River water were collected seasonally, and 24 water quality parameters were analyzed in 2015. All waters were alkaline, and nitrate-nitrogen was the main form of nitrogen, while phosphorus was mostly below detection level. The water quality parameters varied in time and space. Cluster analysis showed a distinct difference between winter and the other seasons and between the natural river section and the section with reclaimed water. Based on the analysis of Gibbs plots, principal component analysis, and ionic relationships, the water chemistry was controlled by dissolution of rocks in natural river section, the quality of replenished water, the effects of dilution, and the reaction of aqueous chemistry in the reclaimed water section. The positive oxidation environment in most of the river water was conducive to the formation of nitrate-nitrogen by nitrification, and not conducive to denitrification.

Poyang Lake is a typical lake in the middle and lower reaches of the Yangtze River and is the largest freshwater lake in China. The habitat quality of Poyang Lake has been declining in recent years, leading to a series of ecological problems. An ecological risk evaluation, based on land use, is important in order to promote a coordinated development of land use and the ecological environment. In this paper, land use data from the Poyang Lake basin in the corresponding years are interpreted based on the images from the Landsat satellite mission in seven periods from 1980 to 2020. The lake surface and the 1 km lakeshore zone of Poyang Lake are extracted based on the interpreted land use data. Finally, the ecological service value per unit area of the area is measured by combining it with the Chinese terrestrial ecosystem service value equivalent table, and then with the value of each ecological factor and the value of the changes to land use type. The research results show that: (1) from 1980 to 2000, the lake area of Poyang Lake had an overall decreasing trend (the area slightly increased from 1980 to 1990); from 2000 to 2020, the lake area of Poyang Lake gradually increased (the area slightly decreased from 2015 to 2020). (2) The farmland, forest, grassland and desert areas gradually increased and the wetlands gradually decreased over 40 years; the area of the water body gradually increased from 1980 to 2010, and gradually decreased from 2010 to 2020. (3) The ecosystem service value of the lakeshore zone of Poyang Lake fluctuated around 15,000 × 10⁶ Yuan from year to year.

The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST) is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (T_aero) poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML) to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC/LST trapezoid model and the electrical analog formula of sensible heat flux (H) to analytically estimate the radiometric-convective resistance (r_ae) via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD) of H and latent heat flux (LE) from OSML are 34.5 W/m² and 46.5 W/m² at SMACEX site and 50.1 W/m² and 67.0 W/m² at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System). Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation over heterogeneous areas.

A trapezoid interpolation thermal disaggregation (TI_DisTrad) model was proposed in this study. This model can disaggregate coarse resolution land surface temperature (LST) to fine resolution LST based on fractional vegetation cover (FVC) versus LST space. The proposed model assumes that the quantitative relationships among the Bowen ratio, FVC and LST can work for the pixels inside the FVC-LST space at both coarser and finer resolutions. Pixels that were outside the FVC-LST space were addressed with a support vector machine regression. We evaluated the TI_DisTrad model over an agricultural region in central Iowa (USA) and an urban region in Beijing (China). The performance of the TI_DisTrad model was assessed by comparing results against those of five other popular benchmark models. The results show that the TI_DisTrad model was slightly superior to three of the benchmark models over the agricultural regions and achieved more accurate LST compared to two of the benchmark models over the urban region. When using two surface energy balance models (the one-source model and the two-source model), the estimated evapotranspiration (ET) from the TI_DisTrad disaggregated LST data was more accurate than the estimated ET from the disaggregated LST obtained using the other benchmark approaches, corresponding to an increase in average accuracy of the TI_DisTrad model.