Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data

Farokhi, Maedeh; Faridani, Farid; Lasaponara, Rosa; Ansari, Hossein; Faridhosseini, Alireza

doi:10.3390/s21155211

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…It is also an important variable in the simulation analysis of hydrology during a specific watershed. Satellite-based remote sensing is still the mainstay of soil moisture estimation [6], due to the accessibility of global-scale measurements at a continuous spatiotemporal resolution [7] Airborne and spaceborne satellite data for earth observation can be retrieved to obtain soil moisture products with a high temporal resolution. Based on active and passive remote sensing, research institutions have released various soil moisture products (such as SMAP, ESA CCI, and AMSR2).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Feng

Huang

Zhao³

et al. 2022

Sensors

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Satellite-based soil moisture products are suitable for large-scale regional monitoring due to the accessibility. Five soil moisture products including SMAP, ESA CCI, and AMSR2 (ascending, descending, and average) were selected in the continental United States (US) from 2016 to 2021. To evaluate the performance of the products and assess their applicability, ISMN (International Soil Moisture Network) data were used as the in situ measurement. PBIAS (Percentage of BIAS), R (Pearson correlation coefficient), RMSE (Root Mean Square Error), ubRMSE (unbiased RMSE), MAE (Mean Absolute Error), and MBE (Mean Bias Error) were selected for evaluation. The performance of five products over six observation networks and various land cover types was compared, and the differences were analyzed at monthly, seasonal, and annual scales. The results show that SMAP had the smallest deviation with the ISMN data because PBIAS was around −0.13, and MBE was around −0.02 m3/m3. ESA CCI performed the best in almost all aspects; its R reached around 0.7, and RMSE was only around 0.07 m3/m3 at the three time scales. The performance of the AMSR2 products varied greatly across the time scales, and increasing errors and deviations showed from 2016 to 2020. The PBO_H2O and USCRN networks could reflect soil moisture characteristics in the continental US, while iRON performed poorly. The evaluation of the networks was closely related to spatial distributions. All products performed better over grasslands and shrublands with R, which was greater than 0.52, and ubRMSE was around 0.1 m3/m3, while products performed worse over forests, where PBIAS was less than −0.62, and RMSE was greater than 0.2 m3/m3, except for ESA CCI. From the boxplot, SMAP was close to the ISMN data with differences less than 0.004 m3/m3 between the median and lower quartiles.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Feng

Huang

Zhao³

et al. 2022

Sensors

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Sensor Networks, Data Processing, and Inference: The Hydrology Challenge

Zanella,

Zubelzu,

Bennis

2023

IEEE Access

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In the last years, many European countries have experienced the effects of climate change, in the form of a scarcity of drinking water resources, prolonged periods of drought, and extremely heavy rainfall, with unprecedented dramatic environmental, economic, and social costs. Therefore, understanding, modeling, and predicting the movement and distribution of water on Earth, and effectively managing water resources are problems of paramount importance. In this article, we discuss the fundamental role that sensing technologies, data processing algorithms, and inference based on machine learning techniques can have in modern hydrology and the many challenges that still need to be addressed to improve the accuracy and reduce the complexity of current hydrology models. More specifically, we overview the main solutions proposed in the literature to monitor, analyze and predict hydrological processes, and present a selection of results obtained from empirical data sets to ground the main concepts and substantiate the dissertation. Finally, we conclude our article by discussing open problems and possible avenues for future research.

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Estimating Maize Root Zone Soil Moisture by Assimilating High Spatiotemporal Resolution Optical and Radar Remote Sensing into the Wofost-Hydrus Coupled Model

Li,

Zheng

et al. 2024

Preprint

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Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data

Cited by 4 publications

References 52 publications

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Evaluation of Several Satellite-Based Soil Moisture Products in the Continental US

Sensor Networks, Data Processing, and Inference: The Hydrology Challenge

Estimating Maize Root Zone Soil Moisture by Assimilating High Spatiotemporal Resolution Optical and Radar Remote Sensing into the Wofost-Hydrus Coupled Model

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