The Normalized Difference Snow Index (NDSI) is an effective index for snow-cover mapping at large scales, but in forested regions the identification accuracy for snow using the NDSI is low because of forest cover effects. In this study, typical evergreen coniferous forest zones on Qilian Mountain in the Upper Heihe River Basin (UHRB) were chosen as example regions. By analyzing the spectral signature of snow-covered and snow-free evergreen coniferous forests with Landsat Operational Land Imager (OLI) data, a novel spectral band ratio using near-infrared (NIR) and shortwave infrared (SWIR) bands, defined as (ρ_nir − ρ_swir)/(ρ_nir + ρ_swir), is proposed. Our research shows that this band ratio, named the normalized difference forest snow index (NDFSI), can be used to effectively distinguish snow-covered evergreen coniferous forests from snow-free evergreen coniferous forests in UHRB.

Lakes on the Tibetan Plateau (TP) are an indicator of global climate change. The study on the factors driving lake change on the TP can help us understand its response to climate change. In this study, Landsat and ICESat data were used to obtain the variations of area, water level, and storage of Hala Lake and the area of glaciers in the Hala Lake Basin during 1987–2018. Combined with meteorological data, climate change trends and the factors driving Hala Lake change in the last 30 years were analyzed. The contribution of glacier mass loss to lake recharge was estimated by the water balance of Hala Lake. The results showed that Hala Lake has experienced three stages: slight expansion (1987–1994), shrinkage (1995–2001) and rapid expansion (2002–2018) during the study period. The rate of glacial melting continued to decline during the study period. Precipitation was the main factor that drove the hydrological characteristic changes in Hala Lake. The step change points of annual precipitation and temperature occurred in 2001, almost the same time that Hala Lake began expanding rapidly.

The spatial scale mismatch between satellite and in-situ -based measurements can be reduced by deploying multiple in-situ sites within the coarse pixel. However, upscaling in-situ measurements from the ground-support scale to the coarse pixel scale is still necessary due to their “point” measurement characteristics. The previous upscaling methods were generally developed merely for the in-situ measurements. Nevertheless, the uncertainty of in-situ measurements such as measurement errors and spatial representativeness errors was not dealt with. Consequently, the upscaling results inevitably suffer from errors, which will finally propagate into the pixel scale ground “truth”. For the first time, this study presents an improved upscaling method with the consideration of the uncertainty of in-situ measurements based on the error theory and measurement adjustment theory. The effectiveness of the corrected upscaling coefficients was evaluated by comparing the accuracy of the corrected upscaling results with those based on the upscaling coefficients without considering the uncertainty of in-situ measurements. The results indicate that the accuracy of the upscaling results can be enhanced by 11.06% in the condition in which in-situ measurements suffer from large uncertainty. However, if the uncertainty of in-situ measurements is negligible, the corrected upscaling model is not necessary because it does not bring many benefits. Although the effectiveness of this method was only tested on a limited study area, it makes an important first step toward a higher precision pixel-scale ground “truth”, especially when the uncertainty of in-situ measurements is non-negligible.

China’s largest desert freshwater lake, Hongjian Nur (HN), which is the largest habitat of relict gull (Larus relictus), has rapidly changed in recent years. However, it is difficult to quantitatively monitor the dynamics of the lake and determine the causes of its changes due to the lack of in situ observation. In this study, a remote sensing-based approach was utilized to overcome these limitations. The monthly water areas during 1990–2017 were first extracted from Landsat multispectral images via an improved method based on the floating algae index (FAI). Then, lake surface elevations measured by real-time kinematics (RTK) were used to calculate the variations in the water storage of HN. Finally, the driving factors of the rapidly changed HN in different periods were investigated by correlation analysis. The result indicated that the drivers affecting the water storage of HN in different periods were not the same. Climate change was the main driving factor of lake level fluctuation during the HN relatively stable stage (1990–1998). Drought and the intensification of human activities were the main factors for the rapid shrinkage of the HN during 1999–2010. Human activities, especially coal-related industries and reservoir impoundment, likely was the primary factors driving the decrease in the water storage of HN from 2010 to 2015. After 2015, the policies that decreased the water consumed by human activities formulated by the government and humid climate were the main factor for the expansion of HN.