Abstract
Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat: R2 = 0.62 and RMSE = 77.10 μg cm−2; MERIS satellite data for soybeans: R2 = 0.24 and RMSE = 136.54 μg cm−2). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVI2 and random forest regression, with R2 = 0.65 and RMSE = 37.76 μg cm−2 (field spectra data) and R2 = 0.78 and RMSE = 47.96 μg cm−2 (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data.
Highlights
Chlorophyll is the main photosynthetic leaf pigment, playing a critical role by converting solar radiation into stored chemical energy [1]
The results indicate that the random forest regression (RFR) model using a vegetation indices (VIs) combination showed the best improvement in Canopy chlorophyll content (CCC) estimation, while the perforthis study, which only used the MERIS terrestrial chlorophyll index (MTCI), confirm the findings from previous studies showing that low-leaf area index (LAI) and high-LAI conditions can cause difficulties when using VIs to estimate vegetation chlorophyll [19,20,21,24]
The results indicate that the RFR model using a VI combination showed the best improvement in CCC estimation, while the performance improvement for Medium Resolution Imaging Spectrometer (MERIS) satellite data varied for different LAI values (Figure 13b)
Summary
Chlorophyll is the main photosynthetic leaf pigment, playing a critical role by converting solar radiation into stored chemical energy [1]. Is calculated based on the leaf area index (LAI) and leaf chlorophyll content (LCC) and expressed per unit leaf area. This measure is useful for monitoring the productivity and growth status of vegetation [2,3]. Over the past few decades, extensive research has found that CCC is the primary driving force for estimating gross primary productivity (GPP) [1,4], so its accurate determination is extremely important for agricultural applications. The methods currently used to determine CCC consist of two approaches: (1) A laboratory-based approach, and (2) non-destructive remote sensing technology. The development of remote-sensing technology has enabled the estimation of CCC using satellite data with various temporal and spatial resolutions [6,7,8]
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