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Dew has been considered a supplementary water resource as it constitutes an important water supply in many ecosystems, especially in arid and semiarid areas. Remote sensing allows large-scale surface observations, offering the possibility to estimate dew in such arid and semiarid regions. In this study, by screening and combining different remote sensing variables, we obtained a well-performing monthly scale dew yield estimation model based on the support vector machine (SVM) learning method. Using daytime and nighttime land surface temperatures (LST), the normalized difference vegetation index (NDVI), and three emissivity bands (3.929–3.989 µm, 10.780–11.280 µm, and 11.770–12.270 µm) as the model inputs, the simulated site-scale monthly dew yield achieved a correlation coefficient (CC) of 0.89 and a root mean square error (RMSE) of 0.30 (mm) for the training set, and CC = 0.59 and RMSE = 0.55 (mm) for the test set. Applying the model to the Heihe River Basin (HRB), the results showed that the annual dew yield ranged from 8.83 to 20.28 mm/year, accounting for 2.12 to 66.88% of the total precipitation, with 74.81% of the area having an annual dew amount of 16 to 19 mm/year. We expanded the model application to Northwest China and obtained a dew yield of 5~30 mm/year from 2011 to 2020, indicating that dew is a non-negligible part of the water balance in this arid area. As a non-negligible part of the water cycle, the use of remote sensing to estimate dew can provide better support for future water resource assessment and analysis. [ABSTRACT FROM AUTHOR]

Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data. [ABSTRACT FROM AUTHOR]

The Qaidam Basin is a unique and complex ecosystem, wherein elevation gradients lead to high spatial heterogeneity in vegetation dynamics and responses to environmental factors. Based on the remote sensing data of Moderate Resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM) and Global Land Data Assimilation System (GLDAS), we analyzed the spatiotemporal variations of vegetation dynamics and responses to precipitation, accumulative temperature (AT) and soil moisture (SM) in the Qaidam Basin from 2001 to 2016. Moreover, the contribution of those factors to vegetation dynamics at different altitudes was analyzed via an artificial neural network (ANN) model. The results indicated that the Normalized Difference Vegetation Index (NDVI) values in the growing season showed an overall upward trend, with an increased rate of 0.001/year. The values of NDVI in low-altitude areas were higher than that in high-altitude areas, and the peak values of NDVI appeared along the elevation gradient at 4400–4600 m. Thanks to the use of ANN, we were able to detect the relative contribution of various environmental factors; the relative contribution rate of AT to the NDVI dynamic was the most significant (35.17%) in the low-elevation region (<2900 m). In the mid-elevation area (2900–3900 m), precipitation contributed 44.76% of the NDVI dynamics. When the altitude was higher than 3900 m, the relative contribution rates of AT (39.50%) and SM (38.53%) had no significant difference but were significantly higher than that of precipitation (21.97%). The results highlight that the different environmental factors have various contributions to vegetation dynamics at different altitudes, which has important theoretical and practical significance for regulating ecological processes. [ABSTRACT FROM AUTHOR]

Highlights • A methodology based on remote sensing data for estimating reference evapotranspiration is proposed. • ET 0 estimation model based on remote sensing data achieved good applicability. • Model using land surface temperature achieved satisfactory results in ET 0 estimation. • Comparison showed the land surface temperature was more essential than surface reflectance. • SVM showed more robust and suitable in estimating ET 0 than BP and ANFIS. Abstract Estimating reference evapotranspiration (ET 0 ) is a fundamental requirement of agricultural water management. The FAO Penman–Monteith (FAO-PM) equation has been used as the standard for ET 0 estimation. However, the lack of necessary meteorological data makes it difficult to estimate spatially distributed ET 0 using the FAO-PM method in the wider ungauged areas. In this study, the aim is to explore the methodology for estimating reference evapotranspiration based on remote sensing data. In this method, remote sensing data are combined with machine learning algorithms to establish a model for spatially distributed ET 0 estimation. Three machine learning algorithms were tested, including support vector machine (SVM), back-propagation neural network (BP), and adaptive neuro fuzzy inference system (ANFIS). Results showed this method had good ability in estimating ET 0. Application of the method in Northwest China indicated that the land surface temperature (LST) can be used to accurately estimate ET 0 with high correlation coefficients (r2 of 0.897–0.915). The surface reflectance has potential for estimating ET 0 with LST and can slightly improve model accuracy based on LST. Evaluation showed LST was more essential than surface reflectance and the model only based on LST had satisfactory performance. This method could be applicability in worldwide with available remote sensing and meteorological data due to the relationship between LST and ET 0. [ABSTRACT FROM AUTHOR]

Rivers act as a source of fresh water for terrestrial life, yet the discharges are poorly documented since the existing direct observations are inadequate and some observation stations have been interrupted or discontinued. Discharge estimates using remote sensing thus have a great potential to supplement ground observations. There are remote sensing methods established to estimate discharge based on single parameter derived relationships; however, they are limited to specific sections due to their empirical nature. In this study, we propose an innovative method to estimate daily discharges for continental rivers (with river channel widths > 800 m (Birkett and Beckley, 2010)) using two satellite derived parameters. Multiple satellite altimetry data and Moderate Resolution Imaging Spectroradiometer (MODIS) data are used to provide a time series of river stages and effective river width. The derived MODIS and altimetry data are then used to optimize unknown parameters in a modified Manning's equation. In situ measurements are used to derive rating curves and to provide assessments of the estimated results. The Nash–Sutcliffe efficiency values for the estimates are between 0.60 and 0.97, indicating the power of the method and accuracy of the estimations. A comparison with a previously developed empirical multivariate equation for estimating river discharge shows that our method produces superior results, especially for large rivers. Furthermore, we found that discharge estimates using both effective river width and stage information consistently outperform those that only use stage data. [ABSTRACT FROM AUTHOR]