scholarly journals Using Multi-Angular Hyperspectral Data to Estimate the Vertical Distribution of Leaf Chlorophyll Content in Wheat

2021 ◽  
Vol 13 (8) ◽  
pp. 1501
Author(s):  
Bin Wu ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Peilei Luo ◽  
Yu Ren ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vertical Distribution ◽  
Chlorophyll Content ◽  
Spectral Response ◽  
Middle Layer ◽  
Bottom Layer ◽  
Estimation Accuracy ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
The Vertical Distribution

Heterogeneity exists in the vertical distribution of the biochemical components of crops. A leaf chlorophyll deficiency occurs in the bottom- and middle-layers of crops due to nitrogen stress and leaf senescence. Some studies used multi-angular remote sensing data for estimating the vertical distribution of the leaf chlorophyll content (LCC). However, these studies performed LCC inversion of different vertical layers using a fixed view zenith angle (VZA), but rarely considered the contribution of the components of the non-target layers to the spectral response. The main goal of this work was to determine the LCC of different vertical layers of the canopy of winter wheat (Triticum aestivum L.), using multi-angular remote sensing and spectral vegetation indices. Different combinations of VZAs were used for obtaining the LCC of different layers. The results revealed that the responses of the transformed chlorophyll in reflectance absorption index (TCARI) and modified chlorophyll absorption in reflectance index (MCARI)/optimized soil-adjusted vegetation index (OSAVI) to the upper-layer LCC were strongest at VZA 10°. For the middle-layer LCC, the response was strongest at 30°, but the response was significantly lower than that of the upper-layer. For the bottom-layer LCC, the responses were weak due to the obscuring effect of the upper- and middle-layer; thus, the LCC inversion of the bottom-layer data was not optimal for a single VZA. The optimal VZA or VZA combinations for LCC estimation were VZA 10° for the upper-layer LCC (TCARI with coefficient of determination (R2) = 0.69, root mean square error (RMSE) = 4.80 ug/cm2, MCARI/OSAVI with R2 = 0.73, RMSE = 4.17 ug/cm2), VZA 10° and 30° for the middle-layer LCC (TCARI with R2 = 0.17, RMSE = 4.81 ug/cm2, MCARI/OSAVI with R2 = 0.17, RMSE = 4.76 ug/cm2), and VZA 10°, 30°, and 50° for the bottom-layer LCC (TCARI with R2 = 0.40, RMSE = 6.29 ug/cm2, MCARI/OSAVI with R2 = 0.40, RMSE = 6.36 ug/cm2). The proposed observation strategy provided a significantly higher estimation accuracy of the target layer LCC than the single VZA approach, and demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the wheat canopy chlorophyll content vertical distribution.

scholarly journals Retrieving Crop Leaf Chlorophyll Content Using an Improved Look-Up-Table Approach by Combining Multiple Canopy Structures and Soil Backgrounds

2020 ◽  
Vol 12 (13) ◽  
pp. 2139 ◽  
Author(s):  
Xiaojin Qian ◽  
Liangyun Liu
Keyword(s):  
Chlorophyll Content ◽  
Spectral Response ◽  
Canopy Structure ◽  
Field Measurements ◽  
Wide Field ◽  
Large Variability ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Look Up Table ◽  
The Mean

Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale.

Remote sensing of leaf chlorophyll content at multiple scales using red, green, and blue bands

2010 ◽  
Author(s):  
E. Raymond Hunt, Jr. ◽  
Daniel S. Long ◽  
Jan U. H. Eitel ◽  
C. S. T. Daughtry
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Multiple Scales ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

scholarly journals A visible band index for remote sensing leaf chlorophyll content at the canopy scale

2013 ◽  
Vol 21 ◽  
pp. 103-112 ◽  
Author(s):  
E. Raymond Hunt ◽  
Paul C. Doraiswamy ◽  
James E. McMurtrey ◽  
Craig S.T. Daughtry ◽  
Eileen M. Perry ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Leaf Chlorophyll Content ◽  
Visible Band ◽  
Leaf Chlorophyll ◽  
Band Index

Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index

2011 ◽  
Vol 103 (4) ◽  
pp. 1090-1099 ◽  
Author(s):  
E. Raymond Hunt ◽  
C. S. T. Daughtry ◽  
Jan U. H. Eitel ◽  
Dan S. Long
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Leaf Chlorophyll Content ◽  
Visible Band ◽  
Leaf Chlorophyll ◽  
Band Index
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