Pazhanivelan Sellaperumal, Ragunath Kaliaperumal, Muthumanickam Dhanaraju, Sudarmanian N S, Shanmugapriya P, Satheesh S, Manikandan Singaram, Sivamurugan A P, Raju Marimuthu, Baskaran Rangasamy, Tamilmounika R
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The parameters viz., the minimum values of -22.03 to -17.69 dB at the start of season, maximum value of -16.10 to -14.20 dB at the peak of season coinciding with heading and corresponding mean increase of 5.07 dB during growing stages were utilized for developing rule-based classification system. Rice area was estimated over the Cauvery Delta Zone of Tamil Nadu, India for the past six years during samba (August-January) season from 2017 to 2023 using Sentinel 1 A Synthetic Aperture Radar satellite data. Rice area maps were generated for the region utilizing parameterization with a classification accuracy of 88.5 to 94.5 per cent with a kappa score of 0.77 to 0.87 during the study period. The total classified rice area during samba season in the Cauvery Delta Zone was 508,581 ha, 456,601 ha, 506,844 ha, 511,714 ha, 524,723 ha and 476,586 ha for the years 2017-18 to 2022-23, respectively. The Start of Season (SoS) maps for samba season revealed that the major planting periods for rice were between the second fortnight of September to first fortnight of November in all the years except 2018 when early planting happened during the first fortnight of September due to favorable weather conditions and assured water supply. Near real-time information on rice area, start of season, and progress of planting derived using SAR satellite data will facilitate the development of decision support systems for sustaining the productivity of rice-based ecosystems.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"8202"},"PeriodicalIF":3.9000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11894211/pdf/","citationCount":"0","resultStr":"{\"title\":\"Time series analysis of Sentinel 1 A SAR data to retrieve annual rice area maps and long-term dynamics of start of season.\",\"authors\":\"Pazhanivelan Sellaperumal, Ragunath Kaliaperumal, Muthumanickam Dhanaraju, Sudarmanian N S, Shanmugapriya P, Satheesh S, Manikandan Singaram, Sivamurugan A P, Raju Marimuthu, Baskaran Rangasamy, Tamilmounika R\",\"doi\":\"10.1038/s41598-025-91655-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Rice is a vital staple crop globally, and accurate estimation of rice area was crucial for effective agricultural management and food security. Synthetic Aperture Radar (SAR) data has emerged as a valuable remote sensing tool for rice area estimation due to its ability to penetrate cloud cover and capture backscattered signals from rice fields. The backscatter signature of rice showed a minimum dB value at agronomic flooding indicating the Start of Season (SoS). The parameters viz., the minimum values of -22.03 to -17.69 dB at the start of season, maximum value of -16.10 to -14.20 dB at the peak of season coinciding with heading and corresponding mean increase of 5.07 dB during growing stages were utilized for developing rule-based classification system. Rice area was estimated over the Cauvery Delta Zone of Tamil Nadu, India for the past six years during samba (August-January) season from 2017 to 2023 using Sentinel 1 A Synthetic Aperture Radar satellite data. Rice area maps were generated for the region utilizing parameterization with a classification accuracy of 88.5 to 94.5 per cent with a kappa score of 0.77 to 0.87 during the study period. The total classified rice area during samba season in the Cauvery Delta Zone was 508,581 ha, 456,601 ha, 506,844 ha, 511,714 ha, 524,723 ha and 476,586 ha for the years 2017-18 to 2022-23, respectively. The Start of Season (SoS) maps for samba season revealed that the major planting periods for rice were between the second fortnight of September to first fortnight of November in all the years except 2018 when early planting happened during the first fortnight of September due to favorable weather conditions and assured water supply. 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Time series analysis of Sentinel 1 A SAR data to retrieve annual rice area maps and long-term dynamics of start of season.
Rice is a vital staple crop globally, and accurate estimation of rice area was crucial for effective agricultural management and food security. Synthetic Aperture Radar (SAR) data has emerged as a valuable remote sensing tool for rice area estimation due to its ability to penetrate cloud cover and capture backscattered signals from rice fields. The backscatter signature of rice showed a minimum dB value at agronomic flooding indicating the Start of Season (SoS). The parameters viz., the minimum values of -22.03 to -17.69 dB at the start of season, maximum value of -16.10 to -14.20 dB at the peak of season coinciding with heading and corresponding mean increase of 5.07 dB during growing stages were utilized for developing rule-based classification system. Rice area was estimated over the Cauvery Delta Zone of Tamil Nadu, India for the past six years during samba (August-January) season from 2017 to 2023 using Sentinel 1 A Synthetic Aperture Radar satellite data. Rice area maps were generated for the region utilizing parameterization with a classification accuracy of 88.5 to 94.5 per cent with a kappa score of 0.77 to 0.87 during the study period. The total classified rice area during samba season in the Cauvery Delta Zone was 508,581 ha, 456,601 ha, 506,844 ha, 511,714 ha, 524,723 ha and 476,586 ha for the years 2017-18 to 2022-23, respectively. The Start of Season (SoS) maps for samba season revealed that the major planting periods for rice were between the second fortnight of September to first fortnight of November in all the years except 2018 when early planting happened during the first fortnight of September due to favorable weather conditions and assured water supply. Near real-time information on rice area, start of season, and progress of planting derived using SAR satellite data will facilitate the development of decision support systems for sustaining the productivity of rice-based ecosystems.
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