Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-Arid

Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020).

Authors

Institutions

Publication Details

Journal
AgriEngineering
Published
2024-10-18
DOI
https://doi.org/10.3390/agriengineering6040217
Citations
1
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.46

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-Arid

Pabrício Marcos Oliveira Lopes, Eberson Pessoa Ribeiro, Cristina Rodrigues Nascimento, Diego Rosyur Castro Manrique et al.
1 citations
AgriEngineering
Remote Sensing in Agriculture
0.46
article

Phenological Monitoring of Irrigated Sugarcane Using Google Earth Engine, Time Series, and TIMESAT in the Brazilian Semi-Arid

Pabrício Marcos Oliveira Lopes, Eberson Pessoa Ribeiro, Cristina Rodrigues Nascimento, Diego Rosyur Castro Manrique, Anderson Santos da Silva
article en
1 citations

Abstract

Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020).

AgriEngineeringVol. 6(4)
Universidade Federal de Pernambuco (BR), Instituto Federal de Educação, Ciência e Tecnologia de Pernambuco (BR), Universidade Federal Rural de Pernambuco (BR)
National Aeronautics and Space Administration, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Universidade de Pernambuco
Openalex Percentile: Top 29%
Remote Sensing in Agriculture
0.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.