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

Feng, Shouming; Huang, Xinyi; Zhao, Shanshan; Qin, Zhihao; Fan, Jiangwen; Zhao, Shukai

doi:10.3390/s22249977

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…The results in Table S1 are consistent with similar studies comparing satellite-derived products with ground-based measurements (e.g., [63][64][65][66][67]). Table 3 summarizes the S1 SSM validation results of this study and compares them with results from El Hajj et al [66] and Portal et al [67].…”

Section: Does S1 Ssm Perform Better Than Other Ssm Products?supporting

confidence: 88%

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

Rojas-Munoz,

Calvet,

Bonan

et al. 2023

Remote Sensing

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Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a sub-kilometer spatial resolution. In this work, aggregated 1 km × 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017–2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together.

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Section: Does S1 Ssm Perform Better Than Other Ssm Products?supporting

confidence: 88%

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

Rojas-Munoz,

Calvet,

Bonan

et al. 2023

Remote Sensing

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“…It was created by direct fusion of soil moisture products from scatterometer and radiometer, level 2 [46]. ESA data have been widely used, for example in [12,[47][48][49]. From the global daily data, two sites close to the Culiacán and Rosario stations were selected.…”

Section: Average Surface Soil Moisture (Asm)mentioning

confidence: 99%

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Atmosphere

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The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

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“…It was created by direct fusion of soil moisture products from scatterometer and radiometer, level 2 [53]. ESA data have been widely used, for example: [18,[54][55][56]. From the global daily data, two sites close to the Culiacán and Rosario stations were selected.…”

Section: Essential Climate Variables (Ecvs)mentioning

confidence: 99%

Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

Llanes Cárdenas,

Estrella Gastélum,

Parra Galaviz

et al. 2024

Preprint

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The goal was to model the irrigated (IBY) and rainfed (RBY) bean yields, as a function of essential climatic variables (ECVs), in the center (Culiacán) and south (Rosario) from Sinaloa. In Sinaloa, and for the period 1982–2013 (October–March), the following were calculated: a) temperatures. b) average degree days for the bean, c) cumulative potential evapotranspiration and d) cumulative effective precipitation. For ECVs, from the European Space Agency, e) daily soil moisture. f) IBY and RBY, from the Agrifood and Fisheries Information Service. Multiple linear regressions were applied, which modeled IBY–RBY (dependent variables), as a function of ECVs (independent variables). Then, to establish each Pearson correlation (PC) as significantly different from zero, a hypothesis test was applied: PC vs Pearson's critical correlation (CPC). The four models obtained were significantly predictive: IBY–Culiacán (PC = 0.590 > CPC = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|) and RBY–Rosario (PC = 0.532 > CPC = |0.349|). This study is the first in Sinaloa to predict IBY and RBY based on ECVs, contributing to the production of sustainable food.

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

Cited by 5 publications

References 44 publications

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

Modeling Yield of Irrigated and Rainfed Bean in Central and Southern Sinaloa State, Mexico, Based on Essential Climate Variables

Modeling of Yield of Irrigated and Rainfed Bean, Based on Essential Climatic Variables, in the Center and South from Sinaloa State, Mexico

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