Surface level soil moisture from two gridded datasets over India are evaluated in this study. The firstone is the UK Met Office (UKMO) soil moisture analysis produced by a land data assimilation systembased on Extended Kalman Filter method (EKF), which make use of satellite observation of AdvancedScatterometer (ASCAT) soil wetness index as well as the screen level meteorological observations. Seconddataset is a satellite soil moisture product, produced by National Remote Sensing Centre (NRSC) usingpassive microwave Advanced Microwave Scanning Radiometer 2 measurements. In-situ observations ofsoil moisture from India Meteorological Department (IMD) are used for the validation of the gridded soilmoisture products. The difference between these datasets over India is minimum in the non-monsoonmonths and over agricultural regions. It is seen that the NRSC data is slightly drier (0.05%) and UKMOsoil moisture analysis is relatively wet during southwest monsoon season. Standard AMSR-2 satellitesoil moisture product is used to compare the NRSC and UKMO products. The standard AMSR-2 andUKMO values are closer in monsoon season and AMSR-2 soil moisture is higher than UKMO in allseasons. NRSC and AMSR-2 showed a correlation of 0.83 (significant at 0.01 level). The probabilitydistribution of IMD soil moisture observation peaks at 0.25 m^3/m^3, NRSC at 0.15 m^3/m^3, AMSR-2 at0.25 m3/m3 and UKMO at 0.35 m^3/m^3 during June–September period. Validation results show UKMOanalysis has better correlation with in-situ observations compared to the NRSC and AMSR-2 datasets.The seasonal variation in soil moisture is better represented in UKMO analysis. Underestimation of soilmoisture during monsoon season over India in NRSC data suggests the necessity of incorporating theactual vegetation for a better soil moisture retrieval using passive microwave sensors. Both productshave good agreement over bare soil, shrubs and grassland compared to needle leaf tree, broad leaf treeand urban land cover types.
Volume 130, 2021
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