The canopy-layer urban heat island (CLHI) and the surface-layer urban heat island (SLHI) of Beijing, the capital city of China, were compared on the spatial scale of a city and the temporal scale of a year in this study. A differential temperature vegetation index (DTVX) method was improved by suggesting a new parameterization scheme for estimating daytime air temperature (T_a); a binary linear regression equation was developed for estimating night-time T_a from Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (T_s) and vegetation indices data during 2009–2010. Validations using weather station observations show that the spatially distributed T_a can be obtained with an accuracy of approximately 2 K. Comparisons between the CLHI and the SLHI indicate that the CLHI agrees well with the SLHI during night-time, but they have a greater difference during daytime either in heat island intensity or in spatial distribution pattern. The SLHI−CLHI intensity difference during daytime has a noticeable seasonal variation, which is small and negative in cold seasons, but large and positive in warm seasons, whereas that at night-time has no significant seasonal variations. The difference in the evapotranspiration cooling effects between urban and rural areas may be the predominant factor that drives the SLHI−CLHI difference.

The vegetation health index (VHI) is a widely utilized remote-sensing-based index for monitoring agricultural drought on the regional or global scale. However, the validity of VHI as a drought detection tool relies on the assumption that the normalized difference vegetation index (NDVI) and land-surface temperature (T_s) at a given pixel will vary inversely over time. This assumption may introduce large uncertainties in VHI for drought monitoring over areas with complex landforms, such as China. In order to monitor agricultural drought over the whole of China, a new drought detection index is suggested in this article, termed the vegetation drought index (VDI). VDI is developed from the classical VHI by substituting NDVI and T_s with the normalized difference water index (NDWI) and day–night T_s difference (∆T_s), respectively. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11C3 and MOD13C2 products from 2001 to 2011, monthly precipitation data from 1970 to 2010, and yearly winter wheat yield data from 2000 to 2012 were utilized to evaluate VDI. Results indicated that (1) many areas in China show a positive correlation between NDVI and T_s, especially in the cold season, whereas most areas have a negative correlation between NDWI and ∆T_s; (2) VDI has a significant linear correlation with VHI in areas and periods where the NDVI–T_s correlation and NDWI–∆T_s correlation are both negative; (3) VDI presents a significant correlation with 3 and 6 month standardized precipitation indices, which is comparable to VHI; and (4) VDI has a significant correlation with normalized crop yield, and is better than VHI. As an example, the extreme drought event over southwestern China from winter 2009 to spring 2010 was successfully explored by VDI. It is concluded that the new index, VDI, has the potential to monitor agricultural drought over the whole of China, including areas and periods where the NDVI–T_s correlation is non-negative.

The land surface temperature and fractional vegetation coverage (LST/FVC) space has been widely used in modeling evapotranspiration and monitoring soil moisture. The triangle and trapezoid are two classical interpretations of the LST/FVC space. Within the triangle, the fully vegetated surface temperature (T _v ) is assumed to be invariant, whereas the bare soil surface temperature (T _s ) is variable. Within the trapezoid, both T _v and T _s are assumed to be variable. However, they are assumed to vary simultaneously in the conventional trapezoid. A two-stage trapezoid is suggested in this paper by considering that T _v should vary after T _s because vegetation can absorb deep soil moisture to maintain transpiration. To validate the rationality of the two-stage trapezoid, the SimSphere model is utilized to simulate the LST/FVC space. Simulations indicate that the two-stage trapezoid is more consistent with the simulated LST/FVC space. Additionally, both the two-stage trapezoid and the conventional trapezoid are compared in how well they estimate the evaporative fraction (EF) over a crop area in Yingke along the middle reaches of Heihe River Basin in China. More than 110 scenes of MODIS LST and vegetation index products were processed. Comparisons at six eddy covariance systems all indicate that the two-stage trapezoid is more effective at estimating EF. The two-stage trapezoid clearly demonstrates the evolution of the LST/FVC space from the triangular to the trapezoidal form. An uncertainty analysis indicates that it is more suitable for the areas and periods where water is the limiting factor for vegetation growth.

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.