Improved PROSAIL Inversion via Auto Differentiation for Estimating Leaf Area Index and Canopy Chlorophyll Content

The accurate mapping of Leaf Area Index (LAI) and Canopy Chlorophyll Content (CCC) is crucial for studying physiological and ecological processes. Leaf Area Index (LAI) is essential for understanding processes such as photosynthesis, transpiration, and energy exchange between the land surface and the atmosphere. CCC serves as an indicator of plant health and productivity, and it is used to assess nitrogen content, which is vital for crop management. We conducted an inversion study for the PROSAIL model using the auto-differentiation (AD) method to retrieve LAI and CCC. The accuracy is assessed on two multispectral/hyperspectral simulated datasets and three measured datasets, covering various types of satellite/airborne image data and in-situ measurements. The RMSE of LAI and CCC is 1.0640 and 74.5518 ug/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for multi-spectral simulated datasets and 0.5537 and 39.5267 ug/cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for hyperspectral simulated datasets under two observation directions. For Barrax agricultural crop datasets, the AD inversion method demonstrates a comparable LAI and an enhanced CCC inversion accuracy when juxtaposed with well-established Simplified Level 2 Product Prototype Processor (SL2P). The feasibility of the AD inversion method is demonstrated in multi-spectral, hyperspectral, and multi-sensor spectral application scenarios. This method has the potential to enhance the retrieval of vegetation biophysical parameters and improve the ecosystem monitoring in the future.