[Hyperspectral remote sensing monitoring of grassland degradation].

The distributing of China's grassland is abroad and the status of grassland degradation is in serious condition. So achieving real-time and exactly grassland ecological monitoring is significant for the carbon cycle, as well as for climate and on regional economies. With the field measured spectra data as data source, hyperspectral remote sensing monitoring of grassland degradation was researched in the present article. The warm meadow grassland in Hulunbeier was chosen as a study object. Reflectance spectra of leaves and pure canopies of some dominant grassland species such as Leymus chinensis, Stipa krylovii and Artemisia frigid, as well as reflectance spectra of mixed grass community were measured. Using effective spectral feature parametrization methods, the spectral feature of leaves and pure canopies were extracted, so the constructive species and degenerate indicator species can be exactly distinguished. Verification results showed that the accuracy of spectral identification was higher than 95%. Taking it as the foundation, the spectra of mixed grass community were unmixed using linear mixing models, and the proportion of all the components was calculated, and the errors were less than 5%. The research results of this article provided the evidence of hyperspectral remote sensing monitoring of grassland degradation.

[Review of monitoring soil water content using hyperspectral remote sensing].

Soil water content is a key parameter in monitoring drought. In recent years, a lot of work has been done on monitoring soil water content based on hyperspectral remotely sensed data both at home and abroad. In the present review, theories, advantages and disadvantages of the monitoring methods using different bands are introduced first. Then the unique advantages, as well as the problems, of the monitoring method with the aid of hyperspectral remote sensing are analyzed. In addition, the impact of soil water content on soil reflectance spectrum and the difference between values at different wavelengths are summarized. This review lists and summarizes the quantitative relationships between soil water content and soil reflectance obtained through analyzing the physical mechanism as well as through statistical way. The key points, advantages and disadvantages of each model are also analyzed and evaluated. Then, the problems in experimental study are pointed out, and the corresponding solutions are proposed. At the same time, the feasibility of removing vegetation effect is discussed, when monitoring soil water content using hyperspectral remote sensing. Finally, the future research trend is prospected.

A spatio-temporal continuous soil moisture dataset over the Tibet Plateau from 2002 to 2015.

Surface soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, and critical to meteorology, hydrology, and ecology. The Tibetan Plateau (TP), known as "The third pole of the world" and "Asia's water towers", exerts huge influences on and sensitive to global climates. In this situation, longer time series of soil moisture can provide sufficient information to understand the role of the TP. This paper presents the first comprehensive dataset (2002-2015) of spatio-temporal continuous soil moisture at 0.25° resolution, based on satellite-based optical (i.e. MODIS) and microwave (ECV) products using a machine learning method named general regression neural network (GRNN). The dataset itself reveals significant information on the soil moisture and its changes over the TP, and can aid to understand the potential driven mechanisms for climate change over the TP.

A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001-2015.

Lake surface water temperature (LSWT) is sensitive to long-term changes in thermal structure of lakes and regional air temperature. In the context of global climate change, recent studies showed a significant warming trend of LSWT based on investigating 291 lakes (71% are large lakes, ≥50 km2 each) globally. However, further efforts are needed to examine variation in LSWT at finer regional spatial and temporal scales. The Tibetan Plateau (TP), known as 'the Roof of the World' and 'Asia's water towers', exerts large influences on and is sensitive to regional and even global climates. Aiming to examine detailed changing patterns and potential driven mechanisms for temperature variations of lakes across the TP region, this paper presents the first comprehensive data set of 15-year (2001-2015) nighttime and daytime LSWT for 374 lakes (≥10 km2 each), using MODIS (Moderate Resolution Imaging Spectroradiometer) Land Surface Temperature (LST) products as well as four lake boundary shapefiles (i.e., 2002, 2005, 2009, and 2014) derived from Landsat/CBERS/GaoFen-1 satellite images. The data set itself reveals significant information on LSWT and its changes over the TP and is an indispensable variable for numerous applications related to climate change, water budget analysis (particularly lake evaporation), water storage changes, glacier melting and permafrost degradation, etc.