~ 2122 ~ Journal of Pharmacognosy and Phytochemistry 2018; 7(3): 2122-2125 E-ISSN: 2278-4136 P-ISSN: 2349-8234 JPP 2018; 7(3): 2122-2125 Received: 05-03-2018 Accepted: 10-04-2018 Chetan Kumar Bhatt Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar, Uttarakhand, India AS Nain Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar, Uttarakhand, India Manoj Kumar Bhatt Department of Soil Science, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar, Uttarakhand, India Correspondence Chetan Kumar Bhatt Department of Agrometeorology, College of Agriculture, G.B. Pant University of Agriculture and Technology, Pantnagar, U.S. Nagar, Uttarakhand, India Downscaling SMOS soil moisture data using geospatial technology Chetan Kumar Bhatt, AS Nain and Manoj Kumar Bhatt Abstract Downscaling of SMOS data using Geospatial Technology was carried out to develop a system to enhance the spatial resolution of soil moisture which otherwise is available at~ 40 Km, by incorporating various ancillary information, at a much higher spatial scale. The current spatial resolution of SMOS data has limited uses due to the coarse resolution. Two study sites one each in Udham Singh Nagar district (Rudrapur area) and Nainital District (Haldwani area) were selected for developing downscaling approach. Point wise soil moisture was interpolated in Quantum GIS software by employing Inverse Distance Weightage algorithm. LANDSAT 8 images were used to generate land use and land cover map of the study region. Soil information was retrieved from the earlier studies carried out in the department. Soil layer and land use map were merged in order to analyze the moisture regime of the area. Soil moisture at each polygon was extracted from interpolated soil moisture layer. Multivariate model was used to downscale the soil moisture values of SMOS at 1km spatial resolution by using the layers of LST and EVI as input. Results indicate that downscaled moisture exhibited quite good agreement with measured soil moisture (R 2 , 0.924 RMSE 0.0487). Keywords: landsat 8, MODIS, SMOS, soil moisture, NDVI, EVI Introduction Soil moisture plays a very important role for both on small agricultural scale and in large-scale modeling of land/atmosphere interaction. Soil moisture observations over large areas are increasingly required in a range of environmental applications including meteorology, hydrology, water resource management and climatology. Various approaches have been developed over the past two decades to infer near- surface soil moisture from remote sensing measurements of surface temperature, radar backscatter and microwave brightness temperature Prigent et al., 2005; Crow and Zhan, 2007 [1] . The relative merit of these approaches depends on the strength of the physical link between the observable in the different spectral domains and soil water content the spatial/temporal resolution which is technically achievable by the different space borne remote sensing systems. The physical link between L-band brightness temperature and soil moisture profile (up to 5 cm) has been shown to be stronger than at higher frequency, and more direct than with radar backscatter and with thermal data. The first satellite to make L-band observations specific to soil moisture retrieval will be the European Soil Moisture and Ocean Salinity (SMOS) mission was launched in 2009. The baseline SMOS payload is an L-band (1.4 GHz) two dimensional (2D) interferometric radiometer that aims at providing global maps of soil moisture with an accuracy better than 4% v/v every 3 days and with a resolution better than 50 km (Kerr et al., 2001) [3] . Materials and Methods The study area chosen is comprised of Nainital and Udham Singh Nagar districts of Uttarakhand, India. Udham Singh Nagar is located between 28 0 58′ 4′′N and 79 0 24′ 0′′ to E. Nainital is located between 29 0 22′ 48′′N to N and 79 0 27′ 0′′ to E.SMOS, LANDSAT 8, and MODIS data were used. The SMOS data downloaded from the website https://earth.esa.int/web/guest/missions/esa-operational-eo missions/smos, Landsat8 and Modis data downloaded from the site http://earthexplorer.usgs.gov/. The soil moisture data was in the form of pixels and took on the basis of area of interest. After the selection of pixel, subsetting of the places was done and values of soil moisture for Nainital and U.S Nagar for the different dates were drawn Point soil moisture data was interpolated in using inverse distance weightage method by keeping the cell size of 0.00036 and 0.00039. The interpolated soil moisture layer was assigned to World Geodetic System 84(WGS-84). Initially soil moisture has been interpolated in the percentages which was