The utility of radiative transfer models (RTM) on remotely sensed data in retrieving biophysical and biochemical properties of terrestrial biomes: A systematic review

Over the past few decades, there has been significant recognition of the value of Radiative Transfer Models (RTMs) for ecological remote sensing applications. This has led to various studies aimed at utilizing RTM techniques to quantify and map a range of biophysical and biochemical properties at different scales. Most literature reviews have predominantly focused on 1D models, such as PROSAIL, overlooking the more robust 3D models. This paper provides a detailed systematic review on the progress, gaps, and opportunities associated with both 1D and 3D RTM models in the context of remote sensing of terrestrial biomes. The review reveals a skewed distribution of research efforts between the Global North and South, with a significant concentration of studies conducted in the United States, China, and Germany, while fewer investigations have been conducted in Africa. Furthermore, most studies have primarily utilized MODIS and Landsat sensors, focusing on plant attributes such as Leaf Area Index (LAI) and chlorophyll content. These studies have been predominantly conducted in grassland and forest landscapes. Overall, the findings indicate that PROSPECT and PROSAIL have been the most popular models over the past two decades. In the realm of 3D models, the Discrete Anisotropic Radiative Transfer (DART) and Forest Light Interaction Model (FLIGHT) models have been the most popular. These models have been primarily utilized through the look-up table (LUT) method, followed by the hybrid approach combining machine learning and RTMs. Understanding both 1D and 3D models offers an opportunity to assess the current state of research and identify future opportunities in the application of radiative transfer modeling for ecological remote sensing. By addressing the existing gaps and leveraging advancements in modeling techniques, researchers can enhance the accuracy and applicability of remote sensing on various ecosystems.