Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Abstract

Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR 705 ), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60 • . Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR 705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

Estimating Nitrogen from Structural Crop Traits at Field Scale—A Novel Approach Versus Spectral Vegetation Indices

“…It can be summarized that the R 760 /R 730 and REIP might be suitable for estimating crop %N. It should, however, be further investigated how prone these VIs are to effects such as saturation [37], multiangular reflection [85], leaf inclination angle [43], or general atmospheric conditions. Further research should involve additional VIs, which were investigated for N content and N uptake, such as the red edge-based canopy chlorophyll content index [86] or the optimum multiple narrow band reflectance model [31].…”

“…These problems can be avoided by using bidirectional spectrometers or active sensors [30,40,41]. Further limiting factors are, however, the influence of differing soil properties, which is particularly important in the early growing period due to the low vegetation fraction [42], and plant properties, such as the leaf inclination angle [43]. Measurements with field spectrometers can also cover only small parts of the crop canopy.…”

Combining biophysical parameters, spectral indices and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices

“…These comprehensive factors are sufficient enough to blur the relationships between most SRIs examined and the measured parameters. Therefore, several studies have reported that to improve the fit of the relationship between SRIs and measured parameters, it is important to analyze these relationships using the pooled data of all treatments in order to avoid the heterogeneity occurring between these treatments, or design new SRIs to remove the adverse effects of multiple factors on the spectral properties of the canopy [ 29, 50, 54–59 ]. For instance, Prabhakara et al [ 58 ] reported that many SRIs failed to differentiate between the amount of biomass in six winter cover crops when the biomass was too high or too low.…”

Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat Imagery and Terrestrial LiDAR Data

“…This conclusion reveals that the LAI retrieval improvements can exist in a time series LAI retrieval and generalize to wider corn planted areas. Much research has indicated that the retrieved LAI using a remote sensing technique is underestimated [50][51][52][53][54], especially for MODIS LAI [10,[44][45][46][47][48]. Therefore, the inferred leaf angle distribution improves the underestimation of LAI retrieval using remote sensing images.…”