Modeling of oil palm phenology based on remote sensing data: opportunities and challenges

Abstract

Oil palm phenology has many advantages in managing the sustainability of oil palm plantations. The phenology of oil palms is a key issue in harvest estimation, fruit bunch pro- duction, estimating oil palm taxes, replanting, fertilization, and detecting oil palm disease. One of the recently developed methods of oil palm phenology involves the use of remote sensing technology. We evaluated and reviewed the current state of oil palm phenology based on remote sensing and conducted an optimized systematic review of recent scientific publications, specifi- cally focusing on scientific peer-reviewed papers published between 1990 and 2021, comprising over 100 existing journal papers on remote sensing for oil palm phenology. The review includes a description of the state of the art and the mapping of oil palm phenology based on sensors, biophysical tree parameters, and classification techniques and also describes the state of the art in the development of regression models of oil palm phenology based on wavelength, biophysical tree parameters, and the type of regression model. Finally, the review provided an opportunity to develop suitable techniques for the identification, classification, and the construction of regres- sion models of oil palm phenology. There is a lot of potential in combining multisensor approaches, suitable classification methods, and regression models for oil palm phenology. For future studies on oil palm phenology, we recommend integrating machine learning with oil palm biophysical parameters based on multisensor remote sensing technologies. © 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)