%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2019/12.13.15.48
%2 sid.inpe.br/mtc-m21c/2019/12.13.15.48.58
%@doi 10.1007/s40808-019-00619-6
%@issn 2363-6203
%T Sugarcane drought detection through spectral indices derived modeling by remote‑sensing techniques
%D 2019
%9 journal article
%A Picoli, Michelle Cristina Araújo,
%A Machado, Pedro Gerber,
%A Duft, Daniel Garbellini,
%A Scarpare, Fábio Vale,
%A Corrêa, Simone Toni Ruiz,
%A Hernandes, Thayse Aparecida Dourado,
%A Rocha, Jansle Vieira,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidade de São Paulo (USP)
%@affiliation Universidade de São Paulo (USP)
%@affiliation Washington State University
%@affiliation Universidade de São Paulo (USP)
%@affiliation Laboratório Nacional de Ciência e Tecnologia do Bioetanal (CTBE)
%@affiliation Universidade Estadual de Campinas (UNICAMP)
%@electronicmailaddress michelle.picoli@inpe.br
%B Modeling Earth Systems and Environment
%V 5
%P 1679-1688
%K Drought stress · Climatological soil–water balance (CSWB) · Monitoring · Satellite images.
%X 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 diference vegetation index (NDVI), vegetation condition index (VCI), normalized diference water index (NDWI), global vegetation moisture index (GVMI), and normalized diference 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 defcit and water surplus, simulated by the climatological soilwater balance (CSWB) model. A signifcant correlation between spectral indices and water balance results, specifcally for NDVI and VCI indices (~30%), was observed. The drought detection system identifcation 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.
%@language en
%3 picoli_sugarcane.pdf