Assessing the spatial variation of cropping intensity using multi-temporal Sentinel-2 data by rule-based classification
The present study was conducted to analyze cropping intensity of four blocks (Mogra-Chinsurah, Polba-Dadpur, Singur and Haripal) of the Gangetic alluvial zone of India using multi-dated Sentinel-2 data in 2018–19 cropping year. It was observed that during peak growing stage all crops ascribed higher...
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description | The present study was conducted to analyze cropping intensity of four blocks (Mogra-Chinsurah, Polba-Dadpur, Singur and Haripal) of the Gangetic alluvial zone of India using multi-dated Sentinel-2 data in 2018–19 cropping year. It was observed that during peak growing stage all crops ascribed higher Normalized Difference Vegetation Index NDVI values (0.4 to 0.73) and NDVI became as low as 0.06 when the fields were vacant. Sentinel-2 data acquired in the peak crop growing period during each cropping season were carefully selected, and NDVI was computed over the whole study area. Rule-based classification was applied for cropping sequence and cropping intensity classification based on the occurrence and non-occurrence of crops using NDVI threshold (0.4). Sentinel-2 images acquired on 22/10/2018, 6/12/2018, 30/1/2019 and 30/4/2019 were used for masking of trees and non-agricultural area. October 22, January 30 and April 30 imageries demonstrated peak crop growing period during
kharif
,
rabi
and pre-
kharif
seasons whereas December 6 image represented occurrence of no or little crop in the study area. Crop acreage was the highest in Polba-Dadpur block during all the three seasons. The crop–fallow—crop sequence occupied the highest areas (43%) followed by crop–crop–crop sequence (39%). 50% and 39% of the total cultivated land was under 200% and 300% cropping intensities. Overall, accuracies of cropping system and cropping intensity classification were 88.54% and 87.85%, respectively. Sentinel-2 data can be successfully used for cropping system analysis which helps in crop planning and management. |
doi_str_mv | 10.1007/s10668-021-01885-0 |
format | Article |
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kharif
,
rabi
and pre-
kharif
seasons whereas December 6 image represented occurrence of no or little crop in the study area. Crop acreage was the highest in Polba-Dadpur block during all the three seasons. The crop–fallow—crop sequence occupied the highest areas (43%) followed by crop–crop–crop sequence (39%). 50% and 39% of the total cultivated land was under 200% and 300% cropping intensities. Overall, accuracies of cropping system and cropping intensity classification were 88.54% and 87.85%, respectively. Sentinel-2 data can be successfully used for cropping system analysis which helps in crop planning and management.</description><identifier>ISSN: 1387-585X</identifier><identifier>EISSN: 1573-2975</identifier><identifier>DOI: 10.1007/s10668-021-01885-0</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Ascription ; Classification ; Cropping sequence ; Cropping systems ; Crops ; Cultivated lands ; Data acquisition ; Earth and Environmental Science ; Ecology ; Economic Geology ; Economic Growth ; Environment ; Environmental Economics ; Environmental Management ; Image acquisition ; Masking ; Normalized difference vegetative index ; Seasons ; Spatial variations ; Sustainable Development ; Systems analysis ; Trees ; Vegetation</subject><ispartof>Environment, development and sustainability, 2022-09, Vol.24 (9), p.10829-10851</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2021</rights><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-4d3b0b51c7b71b0282f6e3d3ccbd7c62976f0226d1a2dbb3d15f07ea1dfedceb3</citedby><cites>FETCH-LOGICAL-c319t-4d3b0b51c7b71b0282f6e3d3ccbd7c62976f0226d1a2dbb3d15f07ea1dfedceb3</cites><orcidid>0000-0003-3003-7886</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10668-021-01885-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10668-021-01885-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>Ghosh, Argha</creatorcontrib><creatorcontrib>Nanda, Manoj K.</creatorcontrib><creatorcontrib>Sarkar, Debolina</creatorcontrib><title>Assessing the spatial variation of cropping intensity using multi-temporal Sentinel-2 data by rule-based classification</title><title>Environment, development and sustainability</title><addtitle>Environ Dev Sustain</addtitle><description>The present study was conducted to analyze cropping intensity of four blocks (Mogra-Chinsurah, Polba-Dadpur, Singur and Haripal) of the Gangetic alluvial zone of India using multi-dated Sentinel-2 data in 2018–19 cropping year. It was observed that during peak growing stage all crops ascribed higher Normalized Difference Vegetation Index NDVI values (0.4 to 0.73) and NDVI became as low as 0.06 when the fields were vacant. Sentinel-2 data acquired in the peak crop growing period during each cropping season were carefully selected, and NDVI was computed over the whole study area. Rule-based classification was applied for cropping sequence and cropping intensity classification based on the occurrence and non-occurrence of crops using NDVI threshold (0.4). Sentinel-2 images acquired on 22/10/2018, 6/12/2018, 30/1/2019 and 30/4/2019 were used for masking of trees and non-agricultural area. October 22, January 30 and April 30 imageries demonstrated peak crop growing period during
kharif
,
rabi
and pre-
kharif
seasons whereas December 6 image represented occurrence of no or little crop in the study area. Crop acreage was the highest in Polba-Dadpur block during all the three seasons. The crop–fallow—crop sequence occupied the highest areas (43%) followed by crop–crop–crop sequence (39%). 50% and 39% of the total cultivated land was under 200% and 300% cropping intensities. Overall, accuracies of cropping system and cropping intensity classification were 88.54% and 87.85%, respectively. Sentinel-2 data can be successfully used for cropping system analysis which helps in crop planning and management.</description><subject>Ascription</subject><subject>Classification</subject><subject>Cropping sequence</subject><subject>Cropping systems</subject><subject>Crops</subject><subject>Cultivated lands</subject><subject>Data acquisition</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Economic Geology</subject><subject>Economic Growth</subject><subject>Environment</subject><subject>Environmental Economics</subject><subject>Environmental Management</subject><subject>Image acquisition</subject><subject>Masking</subject><subject>Normalized difference vegetative index</subject><subject>Seasons</subject><subject>Spatial variations</subject><subject>Sustainable Development</subject><subject>Systems analysis</subject><subject>Trees</subject><subject>Vegetation</subject><issn>1387-585X</issn><issn>1573-2975</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kEtLAzEUhYMoWB9_wFXAdTSPTjKzLMUXFFyo4C7kNTVlmhmTjNJ_b9oR3Mld3Ls437mcA8AVwTcEY3GbCOa8RpgShEldVwgfgRmpBEO0EdVxuVktUFVX76fgLKUNxhQ3lM_A9yIll5IPa5g_HEyDyl518EtFX64-wL6FJvbDsFf4kF1IPu_geCC2Y5c9ym479LFALy5kH1yHKLQqK6h3MI6dQ1olZ6HpVPnTenPwvQAnreqSu_zd5-Dt_u51-YhWzw9Py8UKGUaajOaWaawrYoQWRGNa05Y7Zpkx2grDSzjeYkq5JYparZklVYuFU8S2zhqn2Tm4nnyH2H-OLmW56ccYyktJeVNGzAkvKjqpStSUomvlEP1WxZ0kWO4LllPBshQsDwVLXCA2QamIw9rFP-t_qB_DAoGd</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>Ghosh, Argha</creator><creator>Nanda, Manoj K.</creator><creator>Sarkar, 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the spatial variation of cropping intensity using multi-temporal Sentinel-2 data by rule-based classification</title><author>Ghosh, Argha ; Nanda, Manoj K. ; Sarkar, Debolina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-4d3b0b51c7b71b0282f6e3d3ccbd7c62976f0226d1a2dbb3d15f07ea1dfedceb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Ascription</topic><topic>Classification</topic><topic>Cropping sequence</topic><topic>Cropping systems</topic><topic>Crops</topic><topic>Cultivated lands</topic><topic>Data acquisition</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>Economic Geology</topic><topic>Economic Growth</topic><topic>Environment</topic><topic>Environmental Economics</topic><topic>Environmental Management</topic><topic>Image acquisition</topic><topic>Masking</topic><topic>Normalized difference vegetative index</topic><topic>Seasons</topic><topic>Spatial variations</topic><topic>Sustainable Development</topic><topic>Systems analysis</topic><topic>Trees</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ghosh, Argha</creatorcontrib><creatorcontrib>Nanda, Manoj K.</creatorcontrib><creatorcontrib>Sarkar, Debolina</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research 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Sustain</stitle><date>2022-09-01</date><risdate>2022</risdate><volume>24</volume><issue>9</issue><spage>10829</spage><epage>10851</epage><pages>10829-10851</pages><issn>1387-585X</issn><eissn>1573-2975</eissn><abstract>The present study was conducted to analyze cropping intensity of four blocks (Mogra-Chinsurah, Polba-Dadpur, Singur and Haripal) of the Gangetic alluvial zone of India using multi-dated Sentinel-2 data in 2018–19 cropping year. It was observed that during peak growing stage all crops ascribed higher Normalized Difference Vegetation Index NDVI values (0.4 to 0.73) and NDVI became as low as 0.06 when the fields were vacant. Sentinel-2 data acquired in the peak crop growing period during each cropping season were carefully selected, and NDVI was computed over the whole study area. Rule-based classification was applied for cropping sequence and cropping intensity classification based on the occurrence and non-occurrence of crops using NDVI threshold (0.4). Sentinel-2 images acquired on 22/10/2018, 6/12/2018, 30/1/2019 and 30/4/2019 were used for masking of trees and non-agricultural area. October 22, January 30 and April 30 imageries demonstrated peak crop growing period during
kharif
,
rabi
and pre-
kharif
seasons whereas December 6 image represented occurrence of no or little crop in the study area. Crop acreage was the highest in Polba-Dadpur block during all the three seasons. The crop–fallow—crop sequence occupied the highest areas (43%) followed by crop–crop–crop sequence (39%). 50% and 39% of the total cultivated land was under 200% and 300% cropping intensities. Overall, accuracies of cropping system and cropping intensity classification were 88.54% and 87.85%, respectively. Sentinel-2 data can be successfully used for cropping system analysis which helps in crop planning and management.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10668-021-01885-0</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0003-3003-7886</orcidid></addata></record> |
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subjects | Ascription Classification Cropping sequence Cropping systems Crops Cultivated lands Data acquisition Earth and Environmental Science Ecology Economic Geology Economic Growth Environment Environmental Economics Environmental Management Image acquisition Masking Normalized difference vegetative index Seasons Spatial variations Sustainable Development Systems analysis Trees Vegetation |
title | Assessing the spatial variation of cropping intensity using multi-temporal Sentinel-2 data by rule-based classification |
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