Assessment of the AMSR-E soil moisture product over India

Chaurasia, Sasmita; Tung, Thanh; Thapliyal, P. K.; Joshi, P. C.

doi:10.1080/01431161.2010.531782

Cited by 12 publications

(2 citation statements)

References 25 publications

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“…Therefore, there is a big difference between their spatial representiveness. This issue has been widely discussed by researchers [34][35][36][37][38][39][40][41][42][43][44][45][46]. It is suggested that ground-based validation should be regarded as a close approximation but not an absolute value.…”

Section: Validationmentioning

confidence: 99%

A Downscaling Method for Improving the Spatial Resolution of AMSR-E Derived Soil Moisture Product Based on MSG-SEVIRI Data

Zhao

2013

Remote Sensing

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Soil moisture is a vital parameter in various land surface processes, and microwave remote sensing is widely used to estimate regional soil moisture. However, the application of the retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, many methods were proposed to downscale microwave soil moisture data. The traditional method is the microwave-optical/IR synergistic approach, in which land surface temperature (LST), vegetation index and surface albedo are key parameters. However, due to the uncertainty in absolute LST estimation, this approach is partly dependent on the accuracy of LST estimation. To eliminate the impacts of LST estimation, an improved downscaling method is proposed in this study to downscale Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Land Parameter Retrieval Model (LPRM) soil moisture product with visible and thermal data of Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI). Two temperature temporal variation parameters related to soil moisture, including mid-morning rising rate and daily maximum temperature time, are introduced in the proposed method to replace LST. The proposed method and the traditional method are both applied to the Iberian Peninsula area for July and August 2007. Comparison of the two results shows that the coefficient of determination (R-squared) has an average improvement of 0.08 and the root mean square error has a systematic decrease. The downscaled soil moisture by the proposed method was validated by REMEDHUS soil moisture network in the study area, and site specific validation gets poor correlation between the two datasets because of the low spatial representativeness of site measurement OPEN ACCESSRemote Sens. 2013, 5 6791 for one MSG-SEVIRI pixel. Although the comparisons at 15 km and network scale show an improvement over the site specific comparison, it is found that the downscaling method systematically degrades the accuracy in soil moisture data, with a R-squared of 0-0.4 and 0.218 for the downscaled data set against 0.7-0.8 and 0.571 for AMSR-E data at 15 km scale and the network scale respectively.

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Section: Validationmentioning

confidence: 99%

A Downscaling Method for Improving the Spatial Resolution of AMSR-E Derived Soil Moisture Product Based on MSG-SEVIRI Data

Zhao

2013

Remote Sensing

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“…Several studies have demonstrated soil moisture estimation from space using passive microwave (e.g., [2][3][4][5]) and active microwave (scatterometer) (e.g., [4,6,7]) at a coarse (25-150 km) spatial resolution. On the other hand, active microwave SAR data have been successfully used to retrieve the soil moisture at a finer (less than 100 m) spatial resolution but with a coarse (approximately 3 weeks) temporal resolution [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

et al. 2016

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Simple Summary: -A novel algorithm delivering high resolution soil moisture maps is developed by merging active (SAR) and passive microwave.-MAPSM is based on the concept of Water Change Capacity. -A case study using MAPSM is presented by using the RADARSAT-2 and SMOS retrieved soil moisture data products over Berambadi watershed, Karnataka, India. -The algorithm parameters show scalability from the spatial resolution of 20 m to 2000 m.Abstract: Availability of soil moisture observations at a high spatial and temporal resolution is a prerequisite for various hydrological, agricultural and meteorological applications. In the current study, a novel algorithm for merging soil moisture from active microwave (SAR) and passive microwave is presented. The MAPSM algorithm-Merge Active and Passive microwave Soil Moisture-uses a spatio-temporal approach based on the concept of the Water Change Capacity (WCC) which represents the amplitude and direction of change in the soil moisture at the fine spatial resolution. The algorithm is applied and validated during a period of 3 years spanning from 2010 to 2013 over the Berambadi watershed which is located in a semi-arid tropical region in the Karnataka state of south India. Passive microwave products are provided from ESA Level 2 soil moisture products derived from Soil Moisture and Ocean Salinity (SMOS) satellite (3 days temporal resolution and 40 km nominal spatial resolution). Active microwave are based on soil moisture retrievals from 30 images of RADARSAT-2 data (24 days temporal resolution and 20 m spatial resolution). The results show that MAPSM is able to provide a good estimate of soil moisture at a spatial resolution of 500 m with an RMSE of 0.025 m 3 /m 3 and 0.069 m 3 /m 3 when comparing it to soil moisture from RADARSAT-2 and in-situ measurements, respectively. The use of Sentinel-1 and RISAT products in MAPSM algorithm is envisioned over other areas where high number of revisits is available. This will need an update of the algorithm to take into account the angle sampling and resolution of Sentinel-1 and RISAT data.

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Evaluation of WRF-simulated multilevel soil moisture, 2-m air temperature, and 2-m relative humidity against in situ observations in India

Rao

Rakesh

2018

Pure Appl. Geophys.

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Assessment of the AMSR-E soil moisture product over India

Cited by 12 publications

References 25 publications

A Downscaling Method for Improving the Spatial Resolution of AMSR-E Derived Soil Moisture Product Based on MSG-SEVIRI Data

A Downscaling Method for Improving the Spatial Resolution of AMSR-E Derived Soil Moisture Product Based on MSG-SEVIRI Data

MAPSM: A Spatio-Temporal Algorithm for Merging Soil Moisture from Active and Passive Microwave Remote Sensing

Evaluation of WRF-simulated multilevel soil moisture, 2-m air temperature, and 2-m relative humidity against in situ observations in India

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