Modelling soil moisture using climate data and normalized difference vegetation index based on nine algorithms in alpine grasslands

Abstract

Soil moisture (SM) is closely correlated with ecosystem structure and function. Examining whether climate data (temperature, precipitation and radiation) and the normalized difference vegetation index (NDVI) can be used to estimate SM variation could benefit research related to SM under climate change and human activities. In this study, we evaluated the ability of nine algorithms to explain potential SM (SMp) variation using climate data and actual SM (SMa) variation using climate data and NDVI. Overall, climate data and the NDVI based on the constructed random forest models led to the best estimated SM (R2 ≥ 94%, RMSE ≤ 2.98, absolute value of relative bias: ≤ 3.45%). Randomness, and the setting values of the two key parameters (mtry and ntree), may explain why the random forest models obtained the highest accuracy in predicating SM. Therefore, the constructed random forest models of SMp and SMa in this study can be thus be applied to estimate spatiotemporal variations in SM and for other related scientific research (e.g., differentiating the relative effects of climate change and human activities on SM), at least for Tibetan grassland region.

Asymmetric warming among elevations may homogenize plant α-diversity and aboveground net primary production of alpine grasslands

“…This phenomenon was strengthened by this study (Figures 2, 3). Second, climate change can significantly affect soil moisture (Wang and Fu, 2023) and asymmetric warming among elevations can homogenize temperature and water availability among elevations (Wang et al, 2021b). Ambient temperature and humidity conditions can influence plant phenology (Han et al, 2023), which are closely correlated with ANPP in alpine grasslands of the "Third Pole of the Earth" (Fu and Shen, 2022;Wang et al, 2022).…”

“…This phenomenon might be consequent to the fact that the effects of asymmetric warming among elevations on water availability, plant species and phylogenetic β-diversity, soil nitrogen and phosphorus availability, and soil pH varied with years (Wang et al, 2021b). Moreover, the effects of warming on plant production and α-diversity can be related to the background values of climatic conditions Fu and Sun, 2022), and climate conditions generally change with years (Wang et al, 2022;Han et al, 2022a;Wang and Fu, 2023). Third, the effects of warming on plant production and α-diversity can be also related to warming duration, and warming may have lagging effects on plant production and α-diversity (Fu et al, 2019;Wang et al, 2021a;Fu and Shen, 2022;Han et al, 2023).…”

“…Grasslands, as an important vegetation type in global ecosystems, are key resources for high-quality development of livestock industry and an important place for wildlife protection Zha et al, 2022;Han et al, 2022a;Wang and Fu, 2023). Alpine grasslands are important sections of global grasslands, and may be the most sensitive and fragile grassland types to global change (Zhang et al, 2015(Zhang et al, , 2022Fu and Sun, 2022).…”

Affecting Factors of Plant Phyllosphere Microbial Community and Their Responses to Climatic Warming—A Review

“…Climatic conditions mainly affect the structure of the phyllosphere microbial community in the following ways: (1) Climatic conditions may change the relative influence of various ecological processes on the assembly of the phyllosphere microbial community [ 36 ]. (2) Climate variables such as temperature and precipitation can affect the physicochemical properties of leaves [ 45 , 46 ] and soil [ 47 , 48 ], and thus affect the structure of the phyllosphere microbial community. Precipitation is also an important reservoir of phyllospheric microorganisms [ 49 ].…”

Identifying favourable conditions for farm scale trafficability and grass growth using a combined Sentinel-2 and soil moisture deficit approach

“…Such technologies can help bridge the gap in existing decision support systems (e.g., hybrid SMD model coupled with farmer knowledge of paddock specific soil drainage class) by providing real or near real time spatial and temporal information to farmers about on-farm optimal operational conditions. However, both optical and radar satellites have limited penetration capabilities (Mohanty et al, 2017) and have been evaluated for soil moisture estimation at coarser spatial scales, especially with microwave sensors (S. Wang and Fu, 2023). Thus, they have restricted abilities at finer spatial and temporal scales, and therefore cannot be used for precisionagricultural applications (Babaeian et al, 2019).…”