Soil moisture (SM) is a crucial component for understanding, modeling, and forecasting terrestrial water cycles and energy budgets. However, estimating field-scale SM based on thermal infrared remote-sensing data is still a challenging task. In this study, an improved Flexible Spatiotemporal DAta Fusion (FSDAF) method based on land-surface Diurnal Temperature Cycle (DTC) model (DFSDAF) was proposed to fuse Moderate Resolution Imaging Spectroradiometer (MODIS) and Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) land-surface temperature (LST) data to generate ASTER-like LST during the night. The reconstructed diurnal LST data at a high spatial resolution (90 m) was then utilized to drive a two-source normalized soil thermal inertia model (TNSTI) for the vegetated surfaces to estimate field-scale SM. The results of the proposed methods were validated at different observation depths (2, 4, 10, 20, 40, 60, and 100 cm) over the Zhangye oasis in the middle region of the Heihe River basin in the northwest of China and were compared with the SM estimates from the TNSTI model and other SM products, including AMSR2/AMSR-E, GLDAS-Noah, and ERA5-land. The results showed the following: (1) The DFSDAF method increased the accuracy of LST prediction, with the determination coefficient (R2) increasing from 0.71 to 0.77, and root mean square error (RMSE) decreasing from 2.17 to 1.89 K. (2) the estimated SMs had the best correlation with the observations at the 10 cm depth (with R2 of 0.657; RMSE of 0.069 m3/m3), but the worst correlation with observations at the 40 cm depth (with R2 of 0.262; RMSE of 0.092 m3/m3); meanwhile, the modeled SMs were significantly underestimated above 40 cm (2, 4, 10, and 20 cm) and slightly overestimated below 40 cm (60 and 100 cm); in addition, the field-scale SM series at high spatial resolution (90 m) showed significant spatiotemporal variation. (3) The SM estimates based on the TNSTI for the vegetated surfaces are more capable of characterizing the SM status in the root zone (~80 cm) or even deeper, while the SMs from AMSR2/AMSR-E, GLDAS-Noah, or ERA5-land products are closer to the SM in the surface layer (the depth is less than 5 cm). The TNSTI provided favorable data supports for hydrological model simulations and showed potential advantages for agricultural refinement managements and smart agriculture.

Understanding the spatiotemporal change of NDVI and the main climatic factors affecting vegetation can provide an effective theoretical basis for ecological environment protection and restoration. In this paper, GIMMS NDVI 3g was used to analyze the vegetation changes in the China's Loess Plateau (CLP) before and after the implementation of Grain for Green Program (GGP). The implementation of GGP is conducive to improving the fragile ecological environment of the CLP. The vegetation of CLP is increasing at 0.0014/a (p < 0.01). After the implementation of GCP, the climate conditions showed a warm and humid trend. The proportion of vegetation affected by precipitation increases gradually, and the proportion affected by temperature decreases. The main controlling factor of vegetation changed from temperature to precipitation before and after the implementation of GGP.

It is meaningful to explore the influence of land use change on urban thermal environment for urban sustainable development and city livability improvement etc. Based on one pixel component arranging comparing algorithm (PCACA), this study estimates three instantaneous heat fluxes of Beijing city from meteorological data and the NDVI, land surface temperature (LST), and albedo products retrieved from Landsat data on September 21, 1997, September 22, 2009 and September 28, 2017. Then the temporal and spatial variation in the heat fluxes of the Beijing with land use change is discussed. The following key points have been found: 1) LST and heat fluxes have distinctly spatial heterogeneity, and significant differences between mountainous and plain areas, and among different land use types in plain area; 2) both LST and heat fluxes have a consistent order of arrangement for different land use types at different times. Forest has the highest latent heat flux (LHF) with an average of 265.7 W/m(2), followed by cropland and grassland, and building land has the smallest average of 158.4 W/m(2). LST has the reverse order, cropland has the highest average of 24.8 degrees C, followed by grassland and cropland, water body has the lowest average of 19.2 degrees C; 3) temporally, urban thermal fluxes vary greatly with land use transition. LHF and sensible heat flux (SHF) will respectively drastically reduce and increase when natural surface changes into building land. These analyses suggest that green spaces play an important role in easing urban heat environment.

A "two times thermal irradiance and four times measuring method" was proposed to measure the surface emissivity of any object. Compared with the previous methods, the proposed method can completely eliminate the interference of the lens and cavity walls of the sensor to measure accurately the irradiance of the observed object, implying its ability to improve the accuracy of surface emissivity measurement and making the equipment of emissivity measurement portable. The designed 1 000 + w/m(2) strong heat radiation source considerably improved the signal to noise ratio of the equipment. To compensate the warming effect of the measured object under the strong heat radiation source, we proposed a universal expression to solve the emissivity in the non-isothermal system and a "Process Method" to reduce warming. The comparison of three measurement results showed that the proposed method outperformed the others.

Lakes, especially the inland lakes, are sensitive to global climate change, which are the indicator of environmental varia-tion. The area of lakes can reflect local climate change information. Thus, the rapid and accurate monitor of the dynamic change of the lake area is of great significance to analyze regional ecological environment. Based on MODIS data, this study used ESTARFM to simulate the Landsat data which are unavailable after 2000, and utilized two types of water index assisted by DEM data to ana-lyze the dynamic area change of Siling Co Lake in Tibet from 1976 to 2014. Then, we analyzed the reasons of lake area change and its respond to climate change using the meteorological data acquired by six adjacent meteorological stations from 1976 to 2014. Conclusions can be made according to the results as the following statements. (1) The Landsat-like data acquired by ESTARFM was consistent to the real Landsat data in water information extraction, whose determination coefficient can reach a value of greater than 0.93. So, the fused data can be applied to extract the information of lakes. (2) Siling Co kept expanding from 1976 to 2014, the area of which increased approximately 711.652 km2, which is 42.36%larger. The average annual growth was about 18.728 km2, and the largest annual increase was up to 55.954 km2. The whole process of lake area change can be divided into three stages: the smooth change, the rapid change, and the smooth change again. The northern region changed most obviously, extending northward for about 22.812 km2. From 2003 to 2005, the southern region was integrated with Ya Gencuo Lake, and then they expanded togeth-er. (3) The snow-ice melting water supply caused by global warming might be the main reason for lake spread, and the decrease of wind speed was the secondary factor. However, the amount of precipitation and sunshine duration were poorly related to the lake ar-ea change.

Vegetation phenology change is one of the most sensitive and direct indicators of global climate change. It is very important to make an accurate understanding of the spatial and temporal of vegetation phenology variation in the Tibetan Plateau. Based on MODIS(Moderate-Resolution Imaging Spectroradiometer) NDVI (Normalized Difference Vegetation Index) data from 2001 to 2013, we studied the extraction models of the vegetation phenology in the Tibetan Plateau with maximum change slope method. After the analysize of 13 years vegetation phenology, the results showed that(1) the vegetation phenology in the Tibetan plateau showed obvious spatial distribution rule from the southeast to the northwest.(2) alpine steppe started to grow when the temperature reached 5°C(3) there was no obvious change in the the start of growing season(SOS) in the Tibetan Plateau over the past 13 years as the end of seanson(EOS) following a more complex change.(4) Vegetation phenology obtaining from remote sensing were different from the traditional sense of the phenology.