期刊
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 36, 期 10, 页码 2634-2653出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2015.1041176
关键词
-
资金
- Beijing Natural Science Foundation [4141001]
- National Natural Science Foundation of China [41171281]
- National Science and Technology Pillar Programme during the Twelfth Five-Year Plan Period [2012BAH29B03, 2012BAH29B04]
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 mu g cm(-2) and 9.35 mu g cm(-2), respectively. Our analysis confirms the operational potential of PROSAIL model inversion for the retrieval of biophysical variables by integrating APK.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据