Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study
AbstractUpcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques. View Full-Text
- Supplementary File 1:
Supplementary (PDF, 241 KB)
Share & Cite This Article
Berger, K.; Atzberger, C.; Danner, M.; D’Urso, G.; Mauser, W.; Vuolo, F.; Hank, T. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sens. 2018, 10, 85.
Berger K, Atzberger C, Danner M, D’Urso G, Mauser W, Vuolo F, Hank T. Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study. Remote Sensing. 2018; 10(1):85.Chicago/Turabian Style
Berger, Katja; Atzberger, Clement; Danner, Martin; D’Urso, Guido; Mauser, Wolfram; Vuolo, Francesco; Hank, Tobias. 2018. "Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study." Remote Sens. 10, no. 1: 85.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.