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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Sugarcane drought detection through spectral indices derived modeling by remote-sensing techniques

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Author(s):
Araujo Picoli, Michelle Cristina [1] ; Machado, Pedro Gerber [2] ; Duft, Daniel Garbellini [3] ; Scarpare, Fabio Vale [4] ; Ruiz Correa, Simone Toni [3] ; Dourado Hernandes, Thayse Aparecida [5] ; Rocha, Jansle Vieira [6]
Total Authors: 7
Affiliation:
[1] Natl Inst Space Res INPE, BR-12227001 Sao Jose Dos Campos, SP - Brazil
[2] Univ Sao Paulo, Inst Energy & Environm, BR-05508900 Sao Paulo, SP - Brazil
[3] Univ Sao Paulo, Luiz de Queiroz Coll Agr ESALQ, BR-13418900 Piracicaba, SP - Brazil
[4] Washington State Univ, Dept Civil & Environm Engn, Pullman, WA 99164 - USA
[5] Brazilian Bioethanol Sci & Technol Lab CTBE, BR-13083970 Campinas, SP - Brazil
[6] Univ Campinas UNICAMP, Fac Agr Engn FEAGRI, BR-13083875 Campinas, SP - Brazil
Total Affiliations: 6
Document type: Journal article
Source: MODELING EARTH SYSTEMS AND ENVIRONMENT; v. 5, n. 4, p. 1679-1688, DEC 2019.
Web of Science Citations: 0
Abstract

Several indices based on satellite images have been explored to monitor agricultural drought. Despite the existence of some drought indices, no drought monitoring system for sugarcane exists. In this sense, drought detection could be useful tool to quantify losses and help with action plans. This study investigates the Landsat image potential for sugarcane drought detection by assessing the relationship between vegetation and agricultural drought indices (normalized difference vegetation index (NDVI), vegetation condition index (VCI), normalized difference water index (NDWI), global vegetation moisture index (GVMI), and normalized difference infrared index (NDII)). Two new indices combining near-infrared (NIR) and short-wave infrared (SWIR) bands are proposed for sugarcane drought detection. All indices were individually and collectively compared with soil water deficit and water surplus, simulated by the climatological soil-water balance (CSWB) model. A significant correlation between spectral indices and water balance results, specifically for NDVI and VCI indices (similar to 30%), was observed. The drought detection system identification was developed by cluster analysis classifying the pixels into three distinct groups (drought, intermediate drought, and non-drought) to later be used in the discriminant analysis. This methodology showed to have an accuracy rate of 65%. However, the discriminant analysis approach was better suited for sugarcane drought monitoring when compared with individual spectral indices. (AU)

FAPESP's process: 14/17090-5 - Impact of climate conditions on the sugarcane yield
Grantee:Michelle Cristina Araujo Picoli
Support Opportunities: Research Program on Global Climate Change - Regular Grants