Global long term daily 1 km surface soil moisture dataset with physics informed machine learning

Han, Qianqian; Zeng, Yijian; Zhang, Lijie; Wang, Chao; Prikaziuk, Egor; Niu, Zhenguo; Su, Zhongbo

doi:10.1038/s41597-023-02011-7

Cited by 25 publications

(10 citation statements)

References 41 publications

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“…It is expected that areas frequently missing SM peaks may not yield high‐quality precipitation estimates through SM2RAIN. While assessing peak‐capturing capacity may not be highly suited for SM products derived from a single mission due to their coarse temporal resolution, it gains significant relevance with the increasing availability of merged SM products from multiple sensors, such as ESA CCI and SMOSSMAP‐IB, and the advent of sophisticated gap‐filling techniques (Dorigo et al., 2017; Li et al., 2022; K. Liu et al., 2023; Zheng et al., 2023). In these contexts, the temporal alignment of SM peaks with intense precipitation events can serve as robust evidence for the reliable performance of the SM data set under evaluation.…”

Section: Resultsmentioning

confidence: 99%

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

Zhang,

Kim,

Kim

et al. 2023

Water Resources Research

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Temporal gaps in satellite‐based soil moisture (SM) products are a persistent issue. This study presents an entirely observation‐based method to derive volumetric SM content for filling gaps in Soil Moisture Active Passive (SMAP) retrievals. Using a water balance equation, 12‐hr topsoil water amount variations are determined based on observed precipitation from the Global Precipitation Measurement Mission (inflow) and a hydrologic loss function (outflow) built on SMAP dry‐downs. A temporally seamless SM product, composed of SMAP dry‐downs and precipitation‐driven moisture approximations, was generated as a secondary outcome in determining optimal water balance parameters. This data set maintains the original SMAP SM dynamics with a median Pearson correlation (R) of 0.69 and an unbiased root‐mean‐square error (ubRMSE) of 0.05 m3/m3. Using these parameters and available SMAP observations, a 12‐hourly SM product was produced over the conterminous United States. Validated against in situ measurements, this 12‐hourly SM product exhibits good performance with a median R of 0.63 and captures most SM peaks induced by heavy rainfall. A time series examination revealed the produced 12‐hourly SM product closely corresponds to in situ SM variations and outperforms two other SMAP‐based 12‐hourly SM products gap‐filled using temporal linear interpolation and a three‐dimensional smoothing approach, especially during sparse SMAP data periods. The proposed scheme's validity is further verified by the comparable performance of the exclusive filled‐on SM estimates. Utilizing the 12‐hourly SM data set and its paired hydrologic losses could enhance the quantification connections among the hydrologic components and benefit the understanding of land‐surface hydrology.

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Section: Resultsmentioning

confidence: 99%

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

Zhang,

Kim,

Kim

et al. 2023

Water Resources Research

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“…SSM influences the partitioning of the incoming energy in the latent and sensible heat fluxes and controls the partitioning of precipitation into runoff, evapotranspiration, and infiltration. 39 We used the Copernicus "1km SSM Version 1" collection downloaded at https://land.copernicus.eu/global/products/ssm. The retrieval algorithm proposed by Bauer-Marschallinger 40 is an improved version of the TU-Wien-Change-Detection model.…”

Section: Ssmmentioning

confidence: 99%

Comparison of the IASI water deficit index and other vegetation indices: the case study of the intense 2022 drought over the Po Valley

Della Rocca,

De Feis,

Masiello

et al. 2023

Remote Sensing of Clouds and the Atmosphere XXVIII

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Exploiting the Infrared Atmospheric Sounder Interferometer (IASI) profiling capability for surface parameters, atmospheric temperature, and water vapour we have designed a new Water Deficit Index (wdi) to monitor drought and heatwaves. Because of climate change at a global level, drought is becoming a strong emergency also in countries which never experienced it, such as the Mediterranean mid-latitude area and, in particular, Italy. The last two years strongly affected the northern part of Italy, i.e. the Po Valley, causing high vegetation and soil water stress. Satellite data can provide a large spatial coverage (locally and globally) as well as a continuous data supply and are an important help to ground monitoring stations, especially in remote regions with dense vegetation. In this paper, we used the wdi to investigate the 2022 intense drought over the Po Valley region. We integrated the study considering both the Surface Soil Moisture (SSM) from Copernicus Sentinel-1 C-SAR and the Normalized Difference Moisture Index (NDMI) from Sentinel-2 images. We also considered the Fractional Vegetation Cover (FVC), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and the Leaf Area Index (LAI) data from the Drought & Vegetation Data Cube (D&V Data Cube) from the European Organization for the Exploitation of Meteorological Satellites -Satellite Application Facilities (EUMETSAT SAFs). Overall, we found that the wdi compares well to other indices related to vegetation stress and can be used as a tool for risk assessment of forest fires and agriculture productivity.

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“…Earth observation products provide valuable information for estimating water contents on a global scale (Han et al, 2023;Sungmin & Orth, 2021). Nonetheless, these estimates are obtained at coarse resolutions and accompanied by significant uncertainties, particularly in non-instrumented areas, which is a common occurrence in forest ecosystems (Melo et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Estimating soil water content in a thorny forest ecosystem by time‐lapse electrical resistivity tomography (ERT) and HYDRUS 2D/3D simulations

Faúndez Urbina,

Alanís,

Ramírez

et al. 2023

Hydrological Processes

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Determination of soil volumetric water content in forest ecosystems is particularly challenging due to deep rooting systems and unknown soil vertical and spatial heterogeneity. This research aims to test two undisturbed methods, electrical resistivity tomography (ERT) and HYDRUS 2D/3D, for 2D determination in a thorny forest ecosystem. The experiment consisted of infiltrating 10 L of water lasting 60 min. During infiltration, ERT measured apparent resistivity by time‐lapse measurements, and was measured with an FDR probe (EnviroSCAN) at 33, 63, 83, 97, and 163 cm depth close to the infiltration site. At the end of infiltration, a soil pit was dug, and 100 measurements of were performed with a TDR in a 10 × 10 cm regular grid. Archie law transformed soil resistivity (ERT) into using manual calibration, verified by an independent dataset. The 2D profile obtained by ERT was qualitatively compared with the HYDRUS 2D/3D one. HYDRUS 2D/3D was parametrized with calibrated parameters obtained with HYDRUS 1D using 106 days of obtained with EnviroSCAN. The results of HYDRUS 1D calibration and verification were satisfactory, with RMSE and Nash‐Sutcliffe coefficients ranging from 0.021 to 0.034 cm3 cm−3 and 0.11 to 0.77, respectively. The forward HYDRUS 2D/3D simulation disagrees with EnviroSCAN data for 33 cm depth. However, it follows the trend with near to zero variation of water content at 63 cm depth. Water content determination by ERT was satisfactory with RMSE for calibration and verification of 0.017 and 0.021 cm3 cm−3. HYDRUS 2D/3D and ERT comparisons were not equal, with a shallower wetting front by ERT and a deeper one for HYDRUS. Still, both wetting fronts agree with the wetting depth estimated by EnviroSCAN. We conclude that both methods are an alternative for determination in heterogeneous and deep soils of forest ecosystems.

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Global long term daily 1 km surface soil moisture dataset with physics informed machine learning

Cited by 25 publications

References 41 publications

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

Temporal Gap‐Filling of 12‐Hourly SMAP Soil Moisture Over the CONUS Using Water Balance Budgeting

Comparison of the IASI water deficit index and other vegetation indices: the case study of the intense 2022 drought over the Po Valley

Estimating soil water content in a thorny forest ecosystem by time‐lapse electrical resistivity tomography (ERT) and HYDRUS 2D/3D simulations

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