Abstract
The main objective of this study is to analyse the near-surface soil moisture fields over the Indian region from two gridded soil moisture datasets and to compare the soil moisture from the above-mentioned datasets with the soil moisture data obtained from the advanced scatterometer (ASCAT). The two soil moisture datasets considered in this study are (i) global land evaporation Amsterdam model (GLEAM) and (ii) Indian monsoon data assimilation and analysis (IMDAA). The IMDAA soil moisture is obtained from modelled output that assimilates soil moisture, while the soil moisture estimates from the GLEAM dataset are derived from both satellite and modelled observations. The results of this study indicate that the differences of ASCAT soil moisture with GLEAM soil moisture are consistently lower than the differences of ASCAT soil moisture with IMDAA soil moisture over all the four seasons in the period 2008–2012. Also, quantitative measures such as improvement parameter (IP), forecast parameter (FP), spatial and temporal correlation are obtained using the two datasets and the ASCAT data to further quantify the relative closeness of the datasets with ASCAT data. The results of these quantitative measures clearly indicate that over the Indian region, the GLEAM near-surface soil moisture data are closer to the ASCAT soil moisture data when compared to the IMDAA near-surface soil moisture data over all seasons for the period 2008–2012. Also, GLEAM soil moisture dataset has lower root mean square error value as compared to IMDAA soil moisture dataset over all seasons and for the period 2008–2012. Also, the results of the IP and FP indicate that the largest percentage of grid cells over which GLEAM data are closer to ASCAT are in the post-monsoon season (October and November). Based on the spatial correlation of near-surface soil moisture between IMDAA, GLEAM and ASCAT, the largest spatial correlation values are observed during the south-west Indian monsoon. The results of temporal correlation reveal that the ASCAT and GLEAM datasets have higher correlation coefficient (CC) values as compared to the CC values corresponding to the ASCAT and IMDAA datasets over most regions of India and over most of the seasons considered.
Research highlights
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The differences of advanced scatterometer (ASCAT) soil moisture with global land evaporation Amsterdam model (GLEAM) soil moisture are consistently lower than the differences of ASCAT soil moisture with Indian monsoon data assimilation and analysis (IMDAA) soil moisture for all the four seasons for 2012.
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The results indicate that the GLEAM dataset is closer to ASCAT data using quantitative measures such as improvement parameter, forecast parameter and spatial correlation.
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The results of temporal correlation reveal that the ASCAT and GLEAM datasets have higher correlation coefficient (CC) values as compared to the CC values corresponding to the ASCAT and IMDAA datasets over most regions of India and over most of the seasons considered.
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Acknowledgements
The first author thanks the Director, Indian Institute of Space Science and Technology for providing the facilities and support and acknowledges AICTE for providing financial fellowship during the post-graduation study. The first author is thankful to her senior, Ms Pavani, for introducing her to the NCL language and for supporting her all along. The first author thanks Shri Vibin Jose for clarifying doubts regarding the ASCAT data. Both the authors thank the director, National Center for Medium-Range Weather Forecasting (NCMRWF) for IMDAA data and the Global Land Evaporation Model (GLEAM) team for providing the GLEAM dataset. The authors thank National Oceanic and Atmospheric Administration for providing ASCAT data.
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Gayathri Vangala: Collected and processed the data, performed visualization, data analysis and drafted the original manuscript; Dr Anantharaman Chandrasekar: Conceptualized the study, provided critical revisions for the manuscript and supervised the work. The authors hereby declare that the study contained in this paper is an original and bonafide work. This study is not funded from any research project.
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Communicated by Kavirajan Rajendran
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Vangala, G., Chandrasekar, A. Analysis of soil moisture estimates from global and regional datasets over the Indian region. J Earth Syst Sci 131, 63 (2022). https://doi.org/10.1007/s12040-021-01800-1
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DOI: https://doi.org/10.1007/s12040-021-01800-1