Monalisa Dwi Lestari, Adam Irwansyah Fauzi, Muhammad Ulin Nuha, Agung Mahadi Putra Perdana, Redho Surya Perdana, Muhammad Eko Ario Rahadianto, Rian Nurtyawan, Raden Putra, Argo Galih Suhadha, Ketut Wikantika
A plant has information on the stages of phenological development, which is very important for monitoring crop production and predicting yields in agriculture. The challenge for Indonesia's agriculture sector as a tropical country that uses high-resolution optical remote sensing is the presence of unavailable images due to interference from fog, clouds, and rain. This study uses the Leaf Area Index (LAI) as a vegetation index or biophysical parameter to map the phenology of corn plants using Decomposition and Analysis of Time Series Software (DATimeS). The data used is the image of Sentinel-2 with the acquisition in February 2020-December 2021. The results show a map of Start of Season, End of Season, Length of Season, Day of the Maximum Value, Maximum Value, Amplitude and Total Area/Seasonal Integral. The results show that corn fields Start of Season occurred in July 2020 (28%), End of Season occurred in September (31%), Length of Season occurred for three months (32%), Day of the Maximum Value occurred in phases V6-V10 (22-40 days) (41%), the maximum value has an average value of 1,459; the average value of the amplitude is 1.450, and the average total area/seasonal integral is 99 Day/LAI. This study displays information for each plant phenological indicator with varying results, and this can be caused by factors such as differences in the quality of corn seeds, depth of soil at germination, pests or weeds, or irrigation. The results of this study are expected to help achieve optimal agricultural yields through the determination of good management such as timely irrigation, fertilization and plant protection. © 2023 American Institute of Physics Inc.. All rights reserved.
Department of Geomatics Engineering, Faculty of Regional and Infrastructure Technology, Institut Teknologi Sumatera, Lampung, 35365, Indonesia; Research and Innovation Center for Geospatial Information Science, Institut Teknologi Sumatera, Lampung, 35365, Indonesia; Department of Geodesy Engineering, National Institute of Technology, 23 PH.H.Mustofa Street, Bandung, 40124, Indonesia; Research and Innovation Center for Disaster Mitigation and Early Detection of Forest Fires, Institut Teknologi Sumatera, Lampung Selatan, 35365, Indonesia; Departement of Environmental Engineering, Institut Teknologi Sumatera, Lampung, 35365, Indonesia; National Research and Innovation Agency (BRIN), Jakarta, Indonesia; Research Center for Geospatial, National Research and Innovation Agency (BRIN), Cibinong, Indonesia; Center for Remote Sensing, Institut Teknologi Bandung, Bandung, 40132, Indonesia; Remote Sensing and Geographic Information Science Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia