Remote sensing inversion of vegetation parameters with IPROSAIL-Net

• Published in: IEEE transactions on geoscience and remote sensing Vol. 62; p. 1
• Main Authors: Han, Yunli, Dong, Yingying, Zhu, Yining, Huang, Wenjiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Biological system modeling; Carbon cycle; Cereals; Chlorophyll; Chlorophyll a; Convolutional neural network; Deep learning; Dry matter; Dry matter content; EnMAP; Hyperspectral remote sensing; Inversion; IPROSAIL-Net; Leaf area; Leaf area index; Leaves; Lookup tables; Mathematical models; Modules; Neural networks; Parameter estimation; Parameters; PROSAIL model; Rapeseed; Reflectance; Reflectivity; Remote sensing; Simulation; Table lookup; Training; Vegetation; Vegetation mapping; Vegetation parameters
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3344188

Vegetation parameters are important for the global carbon cycle. Therefore, the quantitative acquisition of vegetation parameters is crucial. The inverse process of the PROSAIL model has provided a classic method for vegetation parameter estimation. The current PROSAIL inverse process simulation, based on look-up tables and other methods, has challenges such as low accuracy and poor spatial universality. To address these issues, this study proposed a PROSAIL inverse process simulation method based on deep learning. The PROSAIL model was decomposed according to the physical process of the model. Then, the corresponding network module was designed based on the inverse process of each module and combined into the IPROSAIL-Net. This network uses the reflectance of the vegetation canopies as the input and presents the leaf structure parameter, chlorophyll a+b content Cab, equivalent water thickness, dry matter content Cm, and leaf area index (LAI) as the output. This article conducted experiments using two different sets of data. PROSAIL simulation data were used to invert the five values. The inversion accuracy was above 0.99 when the number of training samples reached more than 30,000. EnMAP data were used to invert the Cab and LAI values. When the number of training samples reached more than 120, the cereals accuracy was above 0.97 and the maize and rapeseed accuracies were above 0.99. The IPROSAIL-Net design was further split into six networks for separate trainings to verify its rationality. Therefore, the IPROSAIL-Net neural network is reasonable and feasible for remote sensing inversion of vegetation parameters.