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Identification of Sugarcane with NDVI Time Series Based on HJ-1 CCD and MODIS Fusion
It is currently difficult to acquire the clear-sky data with high spatial resolutions in spring and summer in the southern region of China, making it impossible to carry out timely and fine monitoring of sugarcane planting information. Thus, Fusui, a sugarcane producing county in Guangxi, was select...
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Published in: | Journal of the Indian Society of Remote Sensing 2020-02, Vol.48 (2), p.249-262 |
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description | It is currently difficult to acquire the clear-sky data with high spatial resolutions in spring and summer in the southern region of China, making it impossible to carry out timely and fine monitoring of sugarcane planting information. Thus, Fusui, a sugarcane producing county in Guangxi, was selected in this paper to analyze the NDVI characteristics and change rules during the whole growth period of sugarcane based on MODIS and HJ-1 CCD remote sensing data, which were fused into 30 m resolution NDVI time series data with high accuracy by using the spatial and temporal fusion model of multi-source remote sensing data ESTRAFM. In addition, the NDVI change rate and sample automatic training threshold were used to construct the sugarcane planting information identification model. The results showed that the fused images showed a high similarity with the observed images, indicating good fusion quality. Moreover, the correlation coefficients in the sugarcane planting area reached 0.953, and AD, AAD and SD were 0.033, 0.019 and 0.007, respectively. The NDVI change rate model was used to identify the sugarcane planting information in different time phases of 113 d, 129 d, 145 d, 193 d and 209 d in spring and summer, and the overall accuracy was 92.17%, 92.58%, 91.78%, 90.52% and 91.17%, respectively. The established model also achieved good results in 2017 with the overall accuracies are 88.44%, 87.79%, 89.79%, 88.34% for 113 d, 145 d, 193 d and 209 d. |
doi_str_mv | 10.1007/s12524-019-01042-1 |
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Thus, Fusui, a sugarcane producing county in Guangxi, was selected in this paper to analyze the NDVI characteristics and change rules during the whole growth period of sugarcane based on MODIS and HJ-1 CCD remote sensing data, which were fused into 30 m resolution NDVI time series data with high accuracy by using the spatial and temporal fusion model of multi-source remote sensing data ESTRAFM. In addition, the NDVI change rate and sample automatic training threshold were used to construct the sugarcane planting information identification model. The results showed that the fused images showed a high similarity with the observed images, indicating good fusion quality. Moreover, the correlation coefficients in the sugarcane planting area reached 0.953, and AD, AAD and SD were 0.033, 0.019 and 0.007, respectively. The NDVI change rate model was used to identify the sugarcane planting information in different time phases of 113 d, 129 d, 145 d, 193 d and 209 d in spring and summer, and the overall accuracy was 92.17%, 92.58%, 91.78%, 90.52% and 91.17%, respectively. The established model also achieved good results in 2017 with the overall accuracies are 88.44%, 87.79%, 89.79%, 88.34% for 113 d, 145 d, 193 d and 209 d.</description><identifier>ISSN: 0255-660X</identifier><identifier>EISSN: 0974-3006</identifier><identifier>DOI: 10.1007/s12524-019-01042-1</identifier><language>eng</language><publisher>New Delhi: Springer India</publisher><subject>Construction planning ; Correlation coefficients ; Earth and Environmental Science ; Earth Sciences ; Image quality ; MODIS ; Planting ; Remote sensing ; Remote Sensing/Photogrammetry ; Research Article ; Spatial data ; Sugarcane ; Summer ; Time series</subject><ispartof>Journal of the Indian Society of Remote Sensing, 2020-02, Vol.48 (2), p.249-262</ispartof><rights>The Author(s) 2019</rights><rights>This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c363t-7627f4e2dc626c92d8fa6a7c8b3e8a7047a240fc051667e11f499cb04dbe79a93</citedby><cites>FETCH-LOGICAL-c363t-7627f4e2dc626c92d8fa6a7c8b3e8a7047a240fc051667e11f499cb04dbe79a93</cites><orcidid>0000-0003-4206-4830</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Chen, Yanli</creatorcontrib><creatorcontrib>Feng, Liping</creatorcontrib><creatorcontrib>Mo, Jianfei</creatorcontrib><creatorcontrib>Mo, Weihua</creatorcontrib><creatorcontrib>Ding, Meihua</creatorcontrib><creatorcontrib>Liu, Zhiping</creatorcontrib><title>Identification of Sugarcane with NDVI Time Series Based on HJ-1 CCD and MODIS Fusion</title><title>Journal of the Indian Society of Remote Sensing</title><addtitle>J Indian Soc Remote Sens</addtitle><description>It is currently difficult to acquire the clear-sky data with high spatial resolutions in spring and summer in the southern region of China, making it impossible to carry out timely and fine monitoring of sugarcane planting information. Thus, Fusui, a sugarcane producing county in Guangxi, was selected in this paper to analyze the NDVI characteristics and change rules during the whole growth period of sugarcane based on MODIS and HJ-1 CCD remote sensing data, which were fused into 30 m resolution NDVI time series data with high accuracy by using the spatial and temporal fusion model of multi-source remote sensing data ESTRAFM. In addition, the NDVI change rate and sample automatic training threshold were used to construct the sugarcane planting information identification model. The results showed that the fused images showed a high similarity with the observed images, indicating good fusion quality. Moreover, the correlation coefficients in the sugarcane planting area reached 0.953, and AD, AAD and SD were 0.033, 0.019 and 0.007, respectively. The NDVI change rate model was used to identify the sugarcane planting information in different time phases of 113 d, 129 d, 145 d, 193 d and 209 d in spring and summer, and the overall accuracy was 92.17%, 92.58%, 91.78%, 90.52% and 91.17%, respectively. The established model also achieved good results in 2017 with the overall accuracies are 88.44%, 87.79%, 89.79%, 88.34% for 113 d, 145 d, 193 d and 209 d.</description><subject>Construction planning</subject><subject>Correlation coefficients</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Image quality</subject><subject>MODIS</subject><subject>Planting</subject><subject>Remote sensing</subject><subject>Remote Sensing/Photogrammetry</subject><subject>Research Article</subject><subject>Spatial data</subject><subject>Sugarcane</subject><subject>Summer</subject><subject>Time series</subject><issn>0255-660X</issn><issn>0974-3006</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kEFLwzAYhoMoOKd_wFPAc_RLmibtUTvnKtMdNsVbyNJkdrh2Ji3ivzdawZuHj3yE530_eBA6p3BJAeRVoCxlnADN4wBnhB6gEeSSkwRAHMadpSkRAl6O0UkI2_jJU8pGaFVWtulqVxvd1W2DW4eX_UZ7oxuLP-ruFT9Onku8qncWL62vbcA3OtgKR3Z2TyguignWTYUfFpNyiad9iC2n6Mjpt2DPft8xeprerooZmS_uyuJ6Tkwiko5IwaTjllVGMGFyVmVOCy1Ntk5spiVwqRkHZyClQkhLqeN5btbAq7WVuc6TMboYeve-fe9t6NS27X0TTyqWpAwyyAREig2U8W0I3jq19_VO-09FQX3bU4M9Fe2pH3uKxlAyhEKEm431f9X_pL4AzkhvKA</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Chen, Yanli</creator><creator>Feng, Liping</creator><creator>Mo, Jianfei</creator><creator>Mo, Weihua</creator><creator>Ding, Meihua</creator><creator>Liu, Zhiping</creator><general>Springer India</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0003-4206-4830</orcidid></search><sort><creationdate>20200201</creationdate><title>Identification of Sugarcane with NDVI Time Series Based on HJ-1 CCD and MODIS Fusion</title><author>Chen, Yanli ; Feng, Liping ; Mo, Jianfei ; Mo, Weihua ; Ding, Meihua ; Liu, Zhiping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-7627f4e2dc626c92d8fa6a7c8b3e8a7047a240fc051667e11f499cb04dbe79a93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Construction planning</topic><topic>Correlation coefficients</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Image quality</topic><topic>MODIS</topic><topic>Planting</topic><topic>Remote sensing</topic><topic>Remote Sensing/Photogrammetry</topic><topic>Research Article</topic><topic>Spatial data</topic><topic>Sugarcane</topic><topic>Summer</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Yanli</creatorcontrib><creatorcontrib>Feng, Liping</creatorcontrib><creatorcontrib>Mo, Jianfei</creatorcontrib><creatorcontrib>Mo, Weihua</creatorcontrib><creatorcontrib>Ding, Meihua</creatorcontrib><creatorcontrib>Liu, Zhiping</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>CrossRef</collection><jtitle>Journal of the Indian Society of Remote Sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Yanli</au><au>Feng, Liping</au><au>Mo, Jianfei</au><au>Mo, Weihua</au><au>Ding, Meihua</au><au>Liu, Zhiping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of Sugarcane with NDVI Time Series Based on HJ-1 CCD and MODIS Fusion</atitle><jtitle>Journal of the Indian Society of Remote Sensing</jtitle><stitle>J Indian Soc Remote Sens</stitle><date>2020-02-01</date><risdate>2020</risdate><volume>48</volume><issue>2</issue><spage>249</spage><epage>262</epage><pages>249-262</pages><issn>0255-660X</issn><eissn>0974-3006</eissn><abstract>It is currently difficult to acquire the clear-sky data with high spatial resolutions in spring and summer in the southern region of China, making it impossible to carry out timely and fine monitoring of sugarcane planting information. Thus, Fusui, a sugarcane producing county in Guangxi, was selected in this paper to analyze the NDVI characteristics and change rules during the whole growth period of sugarcane based on MODIS and HJ-1 CCD remote sensing data, which were fused into 30 m resolution NDVI time series data with high accuracy by using the spatial and temporal fusion model of multi-source remote sensing data ESTRAFM. In addition, the NDVI change rate and sample automatic training threshold were used to construct the sugarcane planting information identification model. The results showed that the fused images showed a high similarity with the observed images, indicating good fusion quality. Moreover, the correlation coefficients in the sugarcane planting area reached 0.953, and AD, AAD and SD were 0.033, 0.019 and 0.007, respectively. The NDVI change rate model was used to identify the sugarcane planting information in different time phases of 113 d, 129 d, 145 d, 193 d and 209 d in spring and summer, and the overall accuracy was 92.17%, 92.58%, 91.78%, 90.52% and 91.17%, respectively. The established model also achieved good results in 2017 with the overall accuracies are 88.44%, 87.79%, 89.79%, 88.34% for 113 d, 145 d, 193 d and 209 d.</abstract><cop>New Delhi</cop><pub>Springer India</pub><doi>10.1007/s12524-019-01042-1</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-4206-4830</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Construction planning Correlation coefficients Earth and Environmental Science Earth Sciences Image quality MODIS Planting Remote sensing Remote Sensing/Photogrammetry Research Article Spatial data Sugarcane Summer Time series |
title | Identification of Sugarcane with NDVI Time Series Based on HJ-1 CCD and MODIS Fusion |
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