Urbanization enhances channel and surface runoff: A quantitative analysis using both physical and empirical models over the Yangtze River basin

Journal of Hydrology, Год журнала: 2024, Номер 635, С. 131194 - 131194

Опубликована: Апрель 6, 2024

Язык: Английский

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Remote sensing of root zone soil moisture: A review of methods and products

Journal of Hydrology, Год журнала: 2025, Номер 656, С. 133002 - 133002

Опубликована: Март 5, 2025

Язык: Английский

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Regional Soil Moisture Estimation Leveraging Multi-Source Data Fusion and Automated Machine Learning

Remote Sensing, Год журнала: 2025, Номер 17(5), С. 837 - 837

Опубликована: Фев. 27, 2025

Soil moisture (SM) monitoring in farmland at a regional scale is crucial for precision irrigation management and ensuring food security. However, existing methods SM estimation encounter significant challenges related to accuracy, generalizability, automation. This study proposes an integrated data fusion method systematically assess the potential of three automated machine learning (AutoML) frameworks—tree-based pipeline optimization tool (TPOT), AutoGluon, H2O AutoML—in retrieving SM. To evaluate impact input variables on six scenarios were designed: multispectral (MS), thermal infrared (TIR), MS combined with TIR, auxiliary data, TIR comprehensive combination MS, data. The research was conducted winter wheat cultivation area within People’s Victory Canal Irrigation Area, focusing 0–40 cm soil layer. results revealed that scenario incorporating all types (MS + auxiliary) achieved highest retrieval accuracy. Under this scenario, AutoML frameworks demonstrated optimal performance. AutoGluon superior performance most scenarios, particularly excelling scenario. It accuracy Pearson correlation coefficient (R) value 0.822, root mean square error (RMSE) 0.038 cm3/cm3, relative (RRMSE) 16.46%. underscores critical role strategies enhancing highlights advantages regional-scale retrieval. findings offer robust technical foundation theoretical guidance advancing efficient monitoring.

Язык: Английский

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Two-step fusion framework for generating 10 m resolution soil moisture with high accuracy in the cotton fields of southern Xinjiang

Industrial Crops and Products, Год журнала: 2025, Номер 226, С. 120582 - 120582

Опубликована: Янв. 31, 2025

Язык: Английский

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Mapping snow depth distribution from 1980 to 2020 on the tibetan plateau using multi-source remote sensing data and downscaling techniques

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2023, Номер 205, С. 246 - 262

Опубликована: Окт. 18, 2023

Язык: Английский

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A Spatial Downscaling Framework for SMAP Soil Moisture Based on Stacking Strategy

Remote Sensing, Год журнала: 2024, Номер 16(1), С. 200 - 200

Опубликована: Янв. 3, 2024

Soil moisture (SM) data can provide guidance for decision-makers in fields such as drought monitoring and irrigation management. Moisture Active Passive (SMAP) satellite offers sufficient spatial resolution global-scale applications, but its utility is limited regional areas due to lower resolution. To address this issue, study proposed a downscaling framework based on the Stacking strategy. The integrated extreme gradient boosting (XGBoost), light machine (LightGBM), categorical (CatBoost) generate 1 km SM using 15 high-resolution factors derived from multi-source datasets. In particular, test influence of terrain partitioning results, Anhui Province, which has diverse features, was selected area. results indicated that performance three base models varied, developed strategy maximized potential each model with encouraging results. Specifically, we found that: (1) achieved highest accuracy all regions, order was: XGBoost > CatBoost LightGBM. (2) Compared measured at 87 sites, downscaled outperformed other products well without partitioning, an average ubRMSE 0.040 m3/m3. (3) responded positively rainfall events mitigated systematic bias SMAP. It also preserved trend original SMAP, higher levels humid region relatively semi-humid region. Overall, provided new soil revealed some interesting findings related effectiveness impact accuracy.

Язык: Английский

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The important role of reliable land surface model simulation in high-resolution multi-source soil moisture data fusion by machine learning

Journal of Hydrology, Год журнала: 2024, Номер 630, С. 130700 - 130700

Опубликована: Янв. 23, 2024

Язык: Английский

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A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

Remote Sensing, Год журнала: 2023, Номер 15(22), С. 5361 - 5361

Опубликована: Ноя. 15, 2023

Root zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution processes plays a key role in energy water exchange between land surface atmosphere; hence, accurate estimation of RZSM is crucial for agricultural irrigation management practices. Traditional methods to measure at stations are laborious spatially uneven, making it difficult obtain data on large scale. Remote sensing techniques can provide large-scale range, but they only (SSM) with depth approximately 5–10 cm. In order range deeper layers, especially the crop root about 100–200 cm, numerous based remote inversion have been proposed. This paper analyzes summarizes research progress sensing-based past few decades classifies these into four categories: empirical methods, semi-empirical physics-based machine learning methods. Then, advantages disadvantages various outlined. Additionally an outlook future development made discussed.

Язык: Английский

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Integration of microwave satellite soil moisture products in the contextual surface temperature-vegetation index models for spatially continuous evapotranspiration estimation

ISPRS Journal of Photogrammetry and Remote Sensing, Год журнала: 2023, Номер 203, С. 211 - 229

Опубликована: Авг. 9, 2023

Язык: Английский

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Mapping 100 m multi-depth soil moisture with WRF-Hydro over Tibetan Plateau

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132884 - 132884

Опубликована: Фев. 1, 2025

Язык: Английский

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