Optimal solar/viewing geometry for an accurate estimation of leaf area index and leaf angle distribution from bidirectional canopy reflectance data
- 1 September 1985
- journal article
- research article
- Published by Taylor & Francis in International Journal of Remote Sensing
- Vol. 6 (9), 1493-1520
- https://doi.org/10.1080/01431168508948297
Abstract
An analysis of the accuracy in the estimation of agronomic parameters such as leaf area index (LAI) and leaf angle distribution (LAD) from the bidirectional canopy reflectance (BDCR) data is presented. This analysis shows that for a given level of errors in the data, there are certain preferred illumination and viewing directions for which the estimations are most accurate. There are other directions, e.g. nadir viewing, for which estimates can be significantly erroneous. This analysis should be useful in selecting the optimum illumination and viewing directions for off-nadir viewing remote-sensing systems such as the SPOT satellite and the Multispectral Linear Array (MLA) Shuttle.Keywords
This publication has 11 references indexed in Scilit:
- Simple Beta Distribution Representation of Leaf Orientation in Vegetation Canopies1Agronomy Journal, 1984
- Inversion of vegetation canopy reflectance models for estimating agronomic variables. V. Estimation of leaf area index and average leaf angle using measured canopy reflectancesRemote Sensing of Environment, 1984
- Inversion of vegetation canopy reflectance models for estimating agronomic variables. IV. Total inversion of the SAIL modelRemote Sensing of Environment, 1984
- Inversion of vegetation canopy reflectance models for estimating agronomic variables. III. Estimation using only canopy reflectance data as illustrated by the suits modelRemote Sensing of Environment, 1984
- Inversion of vegetation canopy reflectance models for estimating agronomic variables. II. Use of angle transforms and error analysis as illustrated by suits' modelRemote Sensing of Environment, 1984
- Automatic corn-soybean classification using Landsat MSS data. II. Early season crop proportion estimationRemote Sensing of Environment, 1984
- Automatic corn-soybean classification using landsat MSS data. I. Near-harvest crop proportion estimationRemote Sensing of Environment, 1984
- Inversion of vegetation canopy reflectance models for estimating agronomic variables. I. Problem definition and initial results using the suits modelRemote Sensing of Environment, 1983
- Estimating Development Stages of Corn from Spectral Data — An Initial Model1Agronomy Journal, 1981
- The radiation regime and architecture of plant standsPublished by Springer Nature ,1981