Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model

Abstract

Leaf area index (LAI) and leaf chlorophyll content (LCC) are major considerations in management decisions, agricultural planning, and policy-making. When a radiative transfer model (RTM) was used to retrieve these biophysical variables from remote-sensing data, the ill-posed problem was unavoidable. In this study, we focused on the use of agronomic prior knowledge (APK), constructing the relationship between LAI and LCC, to restrict and mitigate the ill-posed inversion results. For this purpose, the inversion results obtained using the SAILH+PROSPECT (PROSAIL) canopy reflectance model alone (no agronomic prior knowledge, NAPK) and those linked with APK were compared. The results showed that LAI inversion had high accuracy. The validation results of the root mean square error (RMSE) between measured and estimated LAI were 0.74 and 0.69 for NAPK and APK, respectively. Compared with NAPK, APK improved LCC estimation; the corresponding RMSE values of NAPK and APK were 13.36 µg cm^–2 and 9.35 µg cm^–2, respectively. Our analysis confirms the operational potential of PROSAIL model inversion for the retrieval of biophysical variables by integrating APK.

Acknowledgements

We are grateful to Mr Weiguo Li, Mrs Hong Chang, and Ling Kong for data collection. We thank the editors and anonymous reviewers who provided many useful comments.

Additional information

Funding

This study was supported by the Beijing Natural Science Foundation [grant number 4141001]; the National Natural Science Foundation of China [grant number 41171281]; and the Key Projects in the National Science and Technology Pillar Programme during the Twelfth Five-Year Plan Period [grant numbers 2012BAH29B03 and 2012BAH29B04].