Отрывок: It indicated that the index is not able to differentiate the effect of water stress. The results of the regression correlation based on the coefficient of determination are as follows:   Fig. 3. Time series analysis of spectral vegetation indices for four classes The results demonstrate that SAVI, RGVI2, NDVI, NDGI and EVI2 provide additional evidence approximately rice crop growth. The RGVI2 values are ...
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dc.contributor.authorChoudhary, K.-
dc.contributor.authorShi, W.-
dc.contributor.authorDong, Y.-
dc.date.accessioned2021-07-01 12:36:34-
dc.date.available2021-07-01 12:36:34-
dc.date.issued2021-06-
dc.identifierDspace\SGAU\20210620\89756ru
dc.identifier.citationChoudhary K, Shi W, Dong Y. Rice growth vegetation index 2 for improving estimation of rice plant phenology in costal ecosystems. Computer Optics 2021; 45(3): 438-448. DOI: 10.18287/2412-6179-CO-827.ru
dc.identifier.urihttps://dx.doi.org/10.18287/2412-6179-CO-827-
dc.identifier.urihttp://repo.ssau.ru/handle/Zhurnal-Komputernaya-optika/Rice-growth-vegetation-index-2-for-improving-estimation-of-rice-plant-phenology-in-costal-ecosystems-89756-
dc.description.abstractCrop growth is one of the most important parameters of a crop and its knowledge before harvest is essential to help farmers, scientists, governments and agribusiness. This paper provides a novel demonstration of the use of freely available Sentinel-2 data to estimate rice crop growth in a single year. Sentinel 2 data provides frequent and consistent information to facilitate coastal monitoring from field scales. The aims of this study were to modify the rice growth vegetation index to improve rice growth phenology in the coastal areas. The rice growth vegetation index 2 is the best vegetation index, compared with 11 vegetation indices, plant height and biomass. The results demonstrate that the coefficient of rice growth vegetation index 2 was 0.83, has the highest correlation with plant height. Rice growth vegetation index 2 is more appropriate for enhancing and obtaining rice phenology information. This study analyses the best spectral vegetation indices for estimating rice growth.ru
dc.description.sponsorshipThis work is supported by the Hong Kong PhD scholarship from PolyU and research grants from the Research Grants Council of (HKSAR) grant project codes B-Q49D and 1-ZVE8. Authors would also like to acknowledge the support drawn from the Agriculture department of Guangdong, China.ru
dc.language.isoenru
dc.publisherСамарский национальный исследовательский университет имени акад. С.П. Королеваru
dc.relation.ispartofseries45;3-
dc.subjectcrop growthru
dc.subjectspectral indicesru
dc.subjectphenologyru
dc.subjectrice growth vegetation index 2ru
dc.titleRice growth vegetation index 2 for improving estimation of rice plant phenology in costal ecosystemsru
dc.typeArticleru
dc.textpartIt indicated that the index is not able to differentiate the effect of water stress. The results of the regression correlation based on the coefficient of determination are as follows:   Fig. 3. Time series analysis of spectral vegetation indices for four classes The results demonstrate that SAVI, RGVI2, NDVI, NDGI and EVI2 provide additional evidence approximately rice crop growth. The RGVI2 values are ...-
dc.classindex.scsti29.31.15, 29.33.43, 20.53.23-
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