Browsing by Author "Deepak Putrevu"

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Active-Passive Approach for NISAR High Resolution Soil Moisture Products: Retrieval and Accuracy Assessment over Indian Cropland
(Institute of Electrical and Electronics Engineers Inc., 2021) Dharmendra Kumar Pandey; Srinivasa Teja Noothi; M. Shashi; Prashant K. Srivastava; Anup Das; Om Pal; Kapil Rohilla; Ravindra Prawasi; Nijbul H. Sekh; Sushma Bisht; Deepak Putrevu; Arundhati Misra; Raj Kumar
Soil moisture is an essential variable in agricultural applications for irrigation scheduling, crop water requirements and pest management etc. However, currently available global satellite microwave radiometer derived soil moisture products are inadequate due to coarse spatial resolution for such applications. In order to improve spatial resolution, SMAP (Soil Moisture Active Passive) mission has first time demonstrated the potential of combining microwave radiometer and radar data based on Active-Passive approach to produce 3-9 km soil moisture globally. However, this approach has not been well tested quantitatively at sub km (<1km) grid resolution using spaceborne observations. In this work, we adopted and tested active-passive approach to integrate radiometer derived coarse resolution soil moisture (SMAP L-band radiometer) with fine resolution radar backscatter (Sentinel-1 C-band SAR) to downscale soil moisture at multiple grid resolution (100 m, 500m and 1000 m). The impact of scale on the robustness of the algorithm is analyzed by assessing the soil moisture retrieval accuracy. Detailed validation was attempted using Multi-Scale Field Sampling Framework (MFSF) over selected agricultural cropland study site. The results obtained are very encouraging, showing the potential of Active-Passive approach for high spatial soil moisture with correlation of 0.75, 0.76 & 0.68 and ubRMSE of 4.94%, 6.07% & 7.21% for 100m, 500m and 1km respectively. Based on the above assessment as pre-cursor study, Active-Passive approach will be the potential candidate for utilizing NISAR data to deliver high spatial resolution soil moisture operational products along with radiometer. © 2021 IEEE.
MACHINE LEARNING BASED SOIL MOISTURE RETRIEVAL ALGORITHM AND VALIDATION AT SELECTED AGRICULTURAL SITES OVER INDIA USING CYGNSS DATA
(Institute of Electrical and Electronics Engineers Inc., 2021) Shivani Tyagi; Dharmendra Kumar Pandey; Deepak Putrevu; Prashant K. Srivastava; Arundhati Misra
This paper demonstrates machine learning based approach to retrieve soil moisture (SM) and its validation over India using CYGNSS data. CYGNSS mission is mainly designed and dedicated for monitoring the tropical cyclones over ocean.However, recent developments has highlighted the potential of GNSS-Reflectometry for land applications, specially for SM with high spatio-temporal frequency over traditional satellite data sets. It can be directly utilized to retrieve SM as complementary data to fill the spatial and temporal gaps in satellite microwave radiometer derived SM, like from SMAP and SMOS mission to meet the requirements of high spatial and temporal frequency data sets for agricultural applications. In this work, we developed an Artificial Neural Network (ANN) framework to derive SM and validated at selected agricultural sites over India. SMAP derived vegetation and roughness parameters were also used as inputs for training of ANN model to add the effect of vegetation and roughness. Detailed spatial and temporal correlation analyses of CYGNSS SM were performed to test the proposed ANN model using SMAP SM and in-situ observations from hydra probe station data from 2018 to 2019. It was observed from temporal correlation analysis that CYGNSS and SMAP SM follow a good trend with high correlation using in-situ data. Spatial correlation also shows high correlation with Pearson correlation coefficient of 0:69 and RMSD of 0:057m3=m3 during pre-monsoon and 0:65 and 0:053m3=m3 in post monsoon periods, respectively. © 2021 IEEE.
Multipath phase based vegetation correction scheme for improved field-scale soil moisture retrieval over agricultural cropland using GNSS-IR technique
(Elsevier Ltd, 2024) Sushant Shekhar; Rishi Prakash; Dharmendra Kumar Pandey; Anurag Vidyarthi; Prashant K. Srivastava; Deepak Putrevu; Nilesh M. Desai
Global Navigation Satellite Systems-Interferometric Reflectometry (GNSS-IR) is an emerging remote sensing technique studied and demonstrated for soil moisture retrieval over bare soil using multipath GNSS data. However, retrieval of soil moisture in the presence of vegetation/crops is still a very challenging task even becoming a more complex problem without using any ancillary datasets to compensate the vegetation effect for soil moisture retrievals in higher crop growth stages. This paper presents a novel multipath phase-based vegetation correction scheme for improved field-scale soil moisture retrieval using L-band data from Navigation with Indian Constellations (NavIC) based on GNSS-IR technique. The proposed three steps vegetation correction scheme categorized the crop as per growth stages for sensitivity analysis of NavIC derived multipath phase as GNSS-IR observables and significantly compensated vegetation effects on multipath phase for improved soil moisture retrievals. The proposed method utilized only multipath phase data as single SNR metrics without any ancillary data sets and validated during complete growth cycle of winter wheat crop (sowing to harvesting stages). It has been shown that the soil moisture retrieved using proposed vegetation correction scheme provides a Pearson correlation coefficient of 0.87 and ubRMSD of 0.0341 m3/m3 with in situ measured soil moisture, which is better than bare scheme with Pearson correlation coefficient of 0.71 and ubRMSD of 0.0480 m3/m3. Overall, proposed scheme has found highly effective for vegetation correction and has shown a good potential candidate for VSM retrievals over crop-covered soil using other GNSS constellations (GPS, GLONASS, Galileo and BeiDou etc.). © 2024 COSPAR
Operational 500 m surface soil moisture product using EOS-04 C-band SAR over Indian agricultural croplands
(Indian Academy of Sciences, 2024) Dharmendra Kumar Pandey; Prashant Kumar Srivastava; Rucha Dave; Raj K. Setia; Ompal; Rajiv Sinha; Muddu Sekhar; Manish Parmar; Shubham Gupta; Deepak Putrevu; Raghav Mehra; V. Ramanujam; Bimal Kumar Bhattacharya; Raj Kumar
Surface soil moisture (SSM) at high spatial resolution is an essential land parameter for agricultural applications like irrigation mapping, scheduling, crop water stress assessment, etc. However, available satellite derived soil moisture products are inadequate for meeting the requirements of agricultural applications due to coarse scale soil moisture (~10–40 km). In this article, we developed an operational framework for first of its kind sub-km (~500 m) operational soil moisture product over India by utilizing ISRO’s EOS-04 C-band synthetic aperture radar (SAR) data based on active-passive approach. The potential of EOS-04 SAR for sub-km scale is demonstrated and tested over major cropland sites covering highly heterogeneous and dynamic crop conditions in different agro-climatic regions over India which shows a good agreement with in situ datasets with mean ubRMSE, ranging from 0.051 to 0.078 m3/m3. © (2024), (Indian Academy of Sciences). All rights reserved.