Matches in SemOpenAlex for { <https://semopenalex.org/work/W4310014076> ?p ?o ?g. }
- W4310014076 endingPage "128824" @default.
- W4310014076 startingPage "128824" @default.
- W4310014076 abstract "Many applications such as irrigation management and flood predictions require soil moisture (SM) estimates at fine-resolutions. Soil moisture estimates of current satellite missions are normally at coarse spatial resolutions, such as 25–36 km for Soil Moisture and Ocean Salinity (SMOS) SM data. Many spatial downscaling approaches have been developed to get finer resolution SM. Apparent thermal inertia (ATI), as a reliable approximation of thermal inertia (TI) that could be derived from daily fine-resolution optical/thermal remote sensing (RS) data, has been testified to be highly related to SM. However, the empirical relationship between ATI and SM varies with vegetation cover density. The main purpose of this study is to develop an ATI-based SM downscaling method that is free of vegetation cover effects. The study was conducted in Naqu areas in the central Tibetan Plateau (TP) in southwest China, where spatially intensive in-situ SM measurements were routinely obtained. European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture combined product (version 4.2) was used for this study. The fine-resolution (1 km) ATI data were derived from the Collection 6 MODerate resolution Imaging Spectroradiometer (MODIS) products. A modified quantile-quantile (Q-Q) adjustment method was applied to correct the bias of the CCI SM data before downscaling. A non-linear regression model between bias-corrected CCI SM and fine resolution ATI was built to disaggregate the bias-corrected SM to spatial resolution of 1 km. In this model, Enhanced Vegetation Index (EVI) was introduced to globally and quantitatively adjust the vegetation effect in the relationship between SM and ATI. The bias-corrected CCI SM and the downscaled 1 km resolution SM were evaluated using the in-situ SM measurements obtained from the Multi-scale Soil Moisture and Temperature Monitoring Network on the central TP (CTP-SMTMN) in the study area. The results indicate that the proposed modified Q-Q adjustment method effectively reduced the bias of the original CCI SM data (bias = 0 m3/m3). The downscaled 1 km resolution SM shows fine scale spatial variability of soil moisture and preserves the accuracy (R = 0.552) and temporal variability of the bias-corrected CCI SM. The accuracies of the downscaled SM over areas with different vegetation cover show quite good consistency. The uncertainties of the proposed SM spatial downscaling method were comprehensively analyzed. The proposed ATI-based SM downscaling algorithm is applicable to generating representative and consistent high spatial resolution SM over areas with different vegetation cover density." @default.
- W4310014076 created "2022-11-30" @default.
- W4310014076 creator A5013507207 @default.
- W4310014076 creator A5044883230 @default.
- W4310014076 creator A5061921172 @default.
- W4310014076 date "2023-01-01" @default.
- W4310014076 modified "2023-10-16" @default.
- W4310014076 title "Spatial downscaling of satellite soil moisture products based on apparent thermal inertia: Considering the effect of vegetation condition" @default.
- W4310014076 cites W1643664725 @default.
- W4310014076 cites W1926971900 @default.
- W4310014076 cites W1939351039 @default.
- W4310014076 cites W1963610170 @default.
- W4310014076 cites W1965229099 @default.
- W4310014076 cites W1965987528 @default.
- W4310014076 cites W1967135612 @default.
- W4310014076 cites W1968487307 @default.
- W4310014076 cites W1971876722 @default.
- W4310014076 cites W1978835122 @default.
- W4310014076 cites W1979137246 @default.
- W4310014076 cites W1986286235 @default.
- W4310014076 cites W1988810672 @default.
- W4310014076 cites W1991553523 @default.
- W4310014076 cites W1995598225 @default.
- W4310014076 cites W1998598671 @default.
- W4310014076 cites W2002076718 @default.
- W4310014076 cites W2007327218 @default.
- W4310014076 cites W2011156649 @default.
- W4310014076 cites W2012968254 @default.
- W4310014076 cites W2015294247 @default.
- W4310014076 cites W2021184758 @default.
- W4310014076 cites W2021765748 @default.
- W4310014076 cites W2025042814 @default.
- W4310014076 cites W2026608513 @default.
- W4310014076 cites W2029385052 @default.
- W4310014076 cites W2029422432 @default.
- W4310014076 cites W2035235584 @default.
- W4310014076 cites W2039348932 @default.
- W4310014076 cites W2046687004 @default.
- W4310014076 cites W2047366175 @default.
- W4310014076 cites W2053208952 @default.
- W4310014076 cites W2055976274 @default.
- W4310014076 cites W2056880116 @default.
- W4310014076 cites W2057996009 @default.
- W4310014076 cites W2061227954 @default.
- W4310014076 cites W2066705669 @default.
- W4310014076 cites W2072093516 @default.
- W4310014076 cites W2072792391 @default.
- W4310014076 cites W2073696851 @default.
- W4310014076 cites W2088068290 @default.
- W4310014076 cites W2094583577 @default.
- W4310014076 cites W2095519551 @default.
- W4310014076 cites W2101293653 @default.
- W4310014076 cites W2101394945 @default.
- W4310014076 cites W2113410727 @default.
- W4310014076 cites W2116779222 @default.
- W4310014076 cites W2120719801 @default.
- W4310014076 cites W2133831422 @default.
- W4310014076 cites W2139037815 @default.
- W4310014076 cites W2139119091 @default.
- W4310014076 cites W2146149230 @default.
- W4310014076 cites W2147252434 @default.
- W4310014076 cites W2148691574 @default.
- W4310014076 cites W2150708430 @default.
- W4310014076 cites W2150956979 @default.
- W4310014076 cites W2151359685 @default.
- W4310014076 cites W2153519472 @default.
- W4310014076 cites W2153629713 @default.
- W4310014076 cites W2154272608 @default.
- W4310014076 cites W2175379292 @default.
- W4310014076 cites W2267551192 @default.
- W4310014076 cites W2304445877 @default.
- W4310014076 cites W2440545289 @default.
- W4310014076 cites W2443939784 @default.
- W4310014076 cites W2516549561 @default.
- W4310014076 cites W2574027118 @default.
- W4310014076 cites W2599868771 @default.
- W4310014076 cites W2609446845 @default.
- W4310014076 cites W2737609272 @default.
- W4310014076 cites W2759163218 @default.
- W4310014076 cites W2762180336 @default.
- W4310014076 cites W2777003700 @default.
- W4310014076 cites W2777677818 @default.
- W4310014076 cites W2792923883 @default.
- W4310014076 cites W2795242773 @default.
- W4310014076 cites W2895301872 @default.
- W4310014076 cites W2920425503 @default.
- W4310014076 cites W2920977949 @default.
- W4310014076 cites W2947841542 @default.
- W4310014076 cites W2972262484 @default.
- W4310014076 cites W3040896947 @default.
- W4310014076 cites W3113187668 @default.
- W4310014076 cites W3129434521 @default.
- W4310014076 cites W65998287 @default.
- W4310014076 doi "https://doi.org/10.1016/j.jhydrol.2022.128824" @default.
- W4310014076 hasPublicationYear "2023" @default.
- W4310014076 type Work @default.
- W4310014076 citedByCount "1" @default.
- W4310014076 countsByYear W43100140762023 @default.