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Obtaining accurate precipitation data in mountainous regions is important but challenging. In ungauged areas, remotely sensed precipitation products are useful supplements and alternatives to measured precipitation products. However, their ability to detect solid precipitation and light precipitation in mountain areas is still unclear. The primary objective of this study is to evaluate two satellite precipitation products, Global Precipitation Measurement (GPM) and Tropical Precipitation Measuring Mission (TRMM), in the headwaters of an inland river on the northeastern Tibetan Plateau (the Heihe river basin), with a specific focus on their performance regarding light precipitation and solid precipitation. The achieved results reveal that both GPM and TRMM perform poorly over the Heihe river basin, with low Correlation Coefficient value and Critical Success Index value, particularly in winter. Based on the coupled Time-Variant Gain Model-Degree Day Factor Model (TVGM-DDF) initiated in this paper, the GPM is more applicable in terms of running hydrological models. With the aim of detecting solid precipitation, the GPM is more capable of detecting solid precipitation but still unsatisfactory at two stations. In the case of light precipitation, both products underestimate light precipitation. In general, the performance of the two products in the Heihe river basin is not satisfactory and should be enhanced in upcoming explorations. This study provides a strong foundation for choosing alternate precipitation data for related research in the mountain basin.

The content of soil water affects the physiological activities of vegetation, and the type of vegetation also affects the soil water balance. It is of great significance to study the response of vegetation to soil moisture change, which is helpful for understanding the vulnerability of vegetation for regional and environmental protections. The response of vegetation to soil moisture in Guangdong Province from mid-October 2015 to the end of March 2017 was studied by using cloudy region drought index (CRDI) as the drought index and volumetric soil water content (VSWC) as the soil moisture index to measure the level of water stress on vegetation. Taking the peak and valley positions of CRDI and VSWC as characteristic points, the lag time of vegetation to volumetric soil water content was obtained by judging the difference between the peak and valley positions of the two indexes. The results indicate that the response of vegetation to volumetric soil water content in Guangdong lagged 3.33 periods (9–35 days) on average. When VSWC is sufficient, there is no obvious difference in time-lag between different types of vegetation. However, when VSWC is relatively insufficient, grass shows the fastest response to the change of volumetric soil water content. Both longitude and soil moisture affect the lag time of vegetation. Under the same conditions, the higher the soil humidity is, the longer the lag time is, and the longer the delay time is with the greater longitude. CRDI can reflect the time-lag effect between vegetation and VSWC in Guangdong, indicating it is a sensitive and applicable index for characterizing the time-lag phenomena of vegetation to soil moisture.

Widespread and long-lasting drought disasters can aggravate environmental degradation. They can lead to significant economic losses and even affect social stability. The existing drought index mostly chose arid and semi-arid regions as study areas, because cloudy weather in humid and semi-humid regions hindered the satellite in its attempts to obtain the surface reflectivity. In order to solve this problem, a cloudy region drought index (CRDI) is proposed to estimate the drought of the clouded pixels. Due to the cumulative effect of drought, the antecedent drought index (ADI) has a certain impact on the calculation of the current drought. Furthermore, cloud is the only source of natural precipitation, and it also affects the evaporation and emission process on the ground. Therefore, based on the remote sensing drought index, ADI and cloud optical thickness (COT) are used to estimate the drought of pixels with missing data due to cloud occlusion. In this paper, a case study of the cloudy Guangdong, which is located in a humid area, is presented. First, we calculated the CRDI using Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2003 to 2017, and then discussed the effect of CRDI with the data from 2016 as examples. Through the analysis of the parameters of regression equation, filling efficiency, rationality of the estimated value, the continuity of CRDI and the rationality of CRDI spatial distribution results, it is concluded that CRDI can effectively estimate the drought severity of the cloud-covered pixels, and more comprehensive drought data can be obtained by using CRDI. The successful application of CRDI in Guangdong shows it is robust and flexible, suggesting high efficiency and great potential for further utilization.

Satellite remote sensing offers an effective approach to estimate indicators of air quality on a large scale. It is critically significant for air quality monitoring in areas experiencing rapid urbanization and consequently severe air pollution, like the Pearl River Delta (PRD) in China. This paper starts with examining ground observations of particulate matter (PM) and the relationship between PM10 (particles smaller than 10 μm) and aerosol optical thickness (AOT) by analyzing observations on the sampling sites in the PRD. A linear regression (R2 = 0.51) is carried out using MODIS-derived 500 m-resolution AOT and PM10 concentration from monitoring stations. Data of atmospheric boundary layer (ABL) height and relative humidity are used to make vertical and humidity corrections on AOT. Results after correction show higher correlations (R2 = 0.55) between extinction coefficient and PM10. However, coarse spatial resolution of meteorological data affects the smoothness of retrieved maps, which suggests high-resolution and accurate meteorological data are critical to increase retrieval accuracy of PM. Finally, the model provides the spatial distribution maps of instantaneous and yearly average PM10 over the PRD. It is proved that observed PM10 is more relevant to yearly mean AOT than instantaneous values.