Dr. Raghavendra B. Jana Profile

Dr. Raghavendra B. Jana

Post Doctoral Fellow at King Abdullah Univ of Science and Technology

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Proceedings Article | 25 October 2016 Paper

Continuous data assimilation for downscaling large-footprint soil moisture retrievals

Muhammad Altaf, Raghavendra Jana, Ibrahim Hoteit, Matthew McCabe

Proceedings Volume 9998, 99981O (2016) https://doi.org/10.1117/12.2241042

KEYWORDS: Soil science, Data modeling, Spatial resolution, Systems modeling, Satellites, Remote sensing, Hydrology, Statistical modeling, Solids, Error analysis

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Soil moisture is a key component of the hydrologic cycle, influencing processes leading to runoff generation, infiltration and groundwater recharge, evaporation and transpiration. Generally, the measurement scale for soil moisture is found to be different from the modeling scales for these processes. Reducing this mismatch between observation and model scales in necessary for improved hydrological modeling. An innovative approach to downscaling coarse resolution soil moisture data by combining continuous data assimilation and physically based modeling is presented. In this approach, we exploit the features of Continuous Data Assimilation (CDA) which was initially designed for general dissipative dynamical systems and later tested numerically on the incompressible Navier-Stokes equation, and the Benard equation. A nudging term, estimated as the misfit between interpolants of the assimilated coarse grid measurements and the fine grid model solution, is added to the model equations to constrain the model’s large scale variability by available measurements. Soil moisture fields generated at a fine resolution by a physically-based vadose zone model (HYDRUS) are subjected to data assimilation conditioned upon coarse resolution observations. This enables nudging of the model outputs towards values that honor the coarse resolution dynamics while still being generated at the fine scale. Results show that the approach is feasible to generate fine scale soil moisture fields across large extents, based on coarse scale observations. Application of this approach is likely in generating fine and intermediate resolution soil moisture fields conditioned on the radiometerbased, coarse resolution products from remote sensing satellites.

Proceedings Article | 5 May 2016 Paper

Soil moisture variability across different scales in an Indian watershed for satellite soil moisture product validation

Gurjeet Singh, Rabindra Panda, Binayak Mohanty, Raghavendra Jana

Proceedings Volume 9877, 98772B (2016) https://doi.org/10.1117/12.2222743

KEYWORDS: Soil science, Satellites, Remote sensing, Climatology, Vegetation, Statistical analysis, Microwave radiation, Sensors, Agriculture, Calibration

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Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA’s Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.

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