@Article{PicoliMaDuScCoHeRo:2019:SuDrDe,
               author = "Picoli, Michelle Cristina Ara{\'u}jo and Machado, Pedro Gerber 
                         and Duft, Daniel Garbellini and Scarpare, F{\'a}bio Vale and 
                         Corr{\^e}a, Simone Toni Ruiz and Hernandes, Thayse Aparecida 
                         Dourado and Rocha, Jansle Vieira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Universidade de 
                         S{\~a}o Paulo (USP)} and {Washington State University} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Laborat{\'o}rio 
                         Nacional de Ci{\^e}ncia e Tecnologia do Bioetanal (CTBE)} and 
                         {Universidade Estadual de Campinas (UNICAMP)}",
                title = "Sugarcane drought detection through spectral indices derived 
                         modeling by remote\‑sensing techniques",
              journal = "Modeling Earth Systems and Environment",
                 year = "2019",
               volume = "5",
                pages = "1679--1688",
             keywords = "Drought stress · Climatological soil–water balance (CSWB) · 
                         Monitoring · Satellite images.",
             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 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.",
                  doi = "10.1007/s40808-019-00619-6",
                  url = "http://dx.doi.org/10.1007/s40808-019-00619-6",
                 issn = "2363-6203",
             language = "en",
           targetfile = "picoli_sugarcane.pdf",
        urlaccessdate = "18 abr. 2024"
}