Assessment of Aboveground Biomass in a Tropical Dry Deciduous Forest Using PRISMA Data

Forests are one of the most significant and major carbon sinks. Understanding the global carbon cycle requires assessing the quantity of carbon stored in forests. Remote sensing has been widely employed for estimating forest biomass from local to global. Several forest factors have been mapped or simulated using remote sensing data alone or in conjunction with field data, making forest mapping a broad field. The use of hyperspectral sensors has dominated the past ten years. The potential of the spaceborne hyperspectral sensor PRISMA is the main focus of this study. The goal of this study is to provide the most up-to-date information on hyperspectral remote sensing from space by focusing on estimating aboveground biomass (AGB) and the feasibility of PRISMA in the challenging phenological circumstances of a tropical dry deciduous forest. The objective of the study was to estimate AGB using PRISMA data and to check the phenological variations of AGB. Findings showed that the employed vegetation indices (c) and phenological conditions significantly impacted the predicted accuracy. Results conclude that the atmospherically resistant vegetation index outperforms the enhanced vegetation index (EVI), normalized difference vegetation index, and simple ratio index for PRISMA, with MAE = 5.42 t/ha, RMSE = 6.43 t/ha, and R 2 = 0.34. It is also predicted that adverse phenological circumstances were the cause of the poor correlation between field biomass and predicted biomass. To check the phenological variation, AGB was also assessed by Landsat 8 data using the same vegetation indices, in which EVI performed better than others with a 0.72 R 2 value. The study indicated that phenological variations have a significant impact on AGB estimation, and narrowband indices can be useful in such studies.