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• W3202956203 endingPage "112706" @default.
• W3202956203 startingPage "112706" @default.
• W3202956203 abstract "The lack of proper understanding of multi-layer soil moisture (SM) profile (signals) remains a persistent challenge in sustainable agricultural water management and food security, especially during drought conditions. We develop a machine-learning algorithm using the concept of learning from patterns to estimate the multi-layer SM information in ungauged locations firmly based on local knowledge of the climatic and landscape controls. The Contiguous United States (CONUS) is clustered into homogeneous regions based on the association between SM and climate and landscape controls. Extreme Gradient Boosting (XGBoost) algorithm is applied to homogenous regions to capture the complex relationship between appropriate predictor variables and in-situ SM at multiple layers over the CONUS. Soil Moisture Active Passive (SMAP) Level 4 (L4) surface (0–5 cm) and rootzone (0–100 cm) SM along with climate and landscape datasets are used as predictor variables. In-situ multi-layer SM recorded by Soil Climate Analysis Network (SCAN), Snow Telemetry (SNOTEL), and U.S. Climate Reference Network (USCRN) networks are utilized as predictands. XGBoost models are then trained region-wise and layer-wise to estimate multi-layer SM information at 5, 10, 20, 50, and 100 cm depths (five layers) at 1-km spatial resolution. Results indicate that the predictor variables have varying levels of influence on SM with changing soil depth, and meteorological variables have the least importance. Validation at 79 independent locations indicates the multi-layer SM estimates successfully capture temporal dynamics of SM, with most locations achieving ubRMSE less than 0.04 m 3 /m 3 . The high-resolution SM estimates offer spatial sub-grid heterogeneity compared to SMAP L4 SM. • New method proposed to estimate multi-layer daily Soil Moisture (SM) over the CONUS. • SM estimated at 1 km resolution across 5 soil depths 5, 10, 20, 50, and 100 cm. • Machine learning model trained with landscape and climate variables to estimate SM. • Relative importance of predictors assessed across the five soil layers. • Spatio-temporal SM estimates at five layers are produced with reasonable accuracy." @default.
• W3202956203 created "2021-10-11" @default.
• W3202956203 creator A5004900883 @default.
• W3202956203 creator A5034822358 @default.
• W3202956203 date "2021-12-01" @default.
• W3202956203 modified "2023-10-17" @default.
• W3202956203 title "Multi-layer high-resolution soil moisture estimation using machine learning over the United States" @default.
• W3202956203 cites W1057815050 @default.
• W3202956203 cites W1484907367 @default.
• W3202956203 cites W1926971900 @default.
• W3202956203 cites W1967400048 @default.
• W3202956203 cites W1969616272 @default.
• W3202956203 cites W1969958218 @default.
• W3202956203 cites W1987552279 @default.
• W3202956203 cites W1994900091 @default.
• W3202956203 cites W1995598225 @default.
• W3202956203 cites W1996747841 @default.
• W3202956203 cites W2001281641 @default.
• W3202956203 cites W2003578656 @default.
• W3202956203 cites W2013173853 @default.
• W3202956203 cites W2015294247 @default.
• W3202956203 cites W2023517822 @default.
• W3202956203 cites W2025042814 @default.
• W3202956203 cites W2025363077 @default.
• W3202956203 cites W2028979033 @default.
• W3202956203 cites W2029064186 @default.
• W3202956203 cites W2033211927 @default.
• W3202956203 cites W2038184996 @default.
• W3202956203 cites W2039348932 @default.
• W3202956203 cites W2044276473 @default.
• W3202956203 cites W2045309803 @default.
• W3202956203 cites W2063122920 @default.
• W3202956203 cites W2063972793 @default.
• W3202956203 cites W2069026003 @default.
• W3202956203 cites W2079990215 @default.
• W3202956203 cites W2080107859 @default.
• W3202956203 cites W2094667211 @default.
• W3202956203 cites W2110479306 @default.
• W3202956203 cites W2121435998 @default.
• W3202956203 cites W2122324724 @default.
• W3202956203 cites W2132549823 @default.
• W3202956203 cites W2138102892 @default.
• W3202956203 cites W2139682450 @default.
• W3202956203 cites W2146149230 @default.
• W3202956203 cites W2147241431 @default.
• W3202956203 cites W2154272608 @default.
• W3202956203 cites W2157526647 @default.
• W3202956203 cites W2167461326 @default.
• W3202956203 cites W2168622160 @default.
• W3202956203 cites W2261645655 @default.
• W3202956203 cites W2275832037 @default.
• W3202956203 cites W2316416257 @default.
• W3202956203 cites W2338345836 @default.
• W3202956203 cites W2477091463 @default.
• W3202956203 cites W2485158535 @default.
• W3202956203 cites W2500468883 @default.
• W3202956203 cites W2513522454 @default.
• W3202956203 cites W2528132721 @default.
• W3202956203 cites W2563913128 @default.
• W3202956203 cites W2567819444 @default.
• W3202956203 cites W2586443140 @default.
• W3202956203 cites W2599868771 @default.
• W3202956203 cites W2605803866 @default.
• W3202956203 cites W2608196101 @default.
• W3202956203 cites W2622143148 @default.
• W3202956203 cites W2734508657 @default.
• W3202956203 cites W2752871607 @default.
• W3202956203 cites W2755735529 @default.
• W3202956203 cites W2770849281 @default.
• W3202956203 cites W2789899211 @default.
• W3202956203 cites W2792192198 @default.
• W3202956203 cites W2801864473 @default.
• W3202956203 cites W2811382599 @default.
• W3202956203 cites W2895301872 @default.
• W3202956203 cites W2895348292 @default.
• W3202956203 cites W2901299800 @default.
• W3202956203 cites W2904758662 @default.
• W3202956203 cites W2910604596 @default.
• W3202956203 cites W2910661958 @default.
• W3202956203 cites W2911446993 @default.
• W3202956203 cites W2911563861 @default.
• W3202956203 cites W2913323966 @default.
• W3202956203 cites W2951791366 @default.
• W3202956203 cites W2965545576 @default.
• W3202956203 cites W2966684393 @default.
• W3202956203 cites W2967660021 @default.
• W3202956203 cites W2971467437 @default.
• W3202956203 cites W2972262484 @default.
• W3202956203 cites W2975602688 @default.
• W3202956203 cites W2983549251 @default.
• W3202956203 cites W2989206286 @default.
• W3202956203 cites W2996012742 @default.
• W3202956203 cites W3000283034 @default.
• W3202956203 cites W3006331223 @default.
• W3202956203 cites W3010720430 @default.
• W3202956203 cites W3013767593 @default.
• W3202956203 cites W3014983389 @default.
• W3202956203 cites W3023439803 @default.

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