Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations

2021 ◽  
Vol 13 (3) ◽  
pp. 470
Author(s):  
Qi Sun ◽  
Quanjun Jiao ◽  
Xiaojin Qian ◽  
Liangyun Liu ◽  
Xinjie Liu ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Chlorophyll Content ◽  
Satellite Data ◽  
Vegetation Indices ◽  
Sensor Data ◽  
Crop Canopy ◽  
Random Forest Regression ◽  
Satellite Sensor Data ◽  
Satellite Sensor

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.

scholarly journals A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat

2019 ◽  
Vol 11 (8) ◽  
pp. 920 ◽  
Author(s):  
Syed Haleem Shah ◽  
Yoseline Angel ◽  
Rasmus Houborg ◽  
Shawkat Ali ◽  
Matthew F. McCabe
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Linear Regression ◽  
Vegetation Indices ◽  
Learning Approach ◽  
Random Forest Regression ◽  
Leaf Chlorophyll ◽  
Machine Learning Approach ◽  
Input Variables ◽  
Non Destructive

Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation structural and functional dynamics. Traditional methods of direct tissue analysis or the use of handheld meters, are not able to capture chlorophyll variability at anything beyond point scales, so are not particularly useful for informing decisions on plant health and status at the field scale. Examining the spectral response of plants via remote sensing has shown much promise as a means to capture variations in vegetation properties, while offering a non-destructive and scalable approach to monitoring. However, determining the optimum combination of spectra or spectral indices to inform plant response remains an active area of investigation. Here, we explore the use of a machine learning approach to enhance the estimation of leaf chlorophyll (Chlt), defined as the sum of chlorophyll a and b, from spectral reflectance data. Using an ASD FieldSpec 4 Hi-Res spectroradiometer, 2700 individual leaf hyperspectral reflectance measurements were acquired from wheat plants grown across a gradient of soil salinity and nutrient levels in a greenhouse experiment. The extractable Chlt was determined from laboratory analysis of 270 collocated samples, each composed of three leaf discs. A random forest regression algorithm was trained against these data, with input predictors based upon (1) reflectance values from 2102 bands across the 400–2500 nm spectral range; and (2) 45 established vegetation indices. As a benchmark, a standard univariate regression analysis was performed to model the relationship between measured Chlt and the selected vegetation indices. Results show that the root mean square error (RMSE) was significantly reduced when using the machine learning approach compared to standard linear regression. When exploiting the entire spectral range of individual bands as input variables, the random forest estimated Chlt with an RMSE of 5.49 µg·cm−2 and an R2 of 0.89. Model accuracy was improved when using vegetation indices as input variables, producing an RMSE ranging from 3.62 to 3.91 µg·cm−2, depending on the particular combination of indices selected. In further analysis, input predictors were ranked according to their importance level, and a step-wise reduction in the number of input features (from 45 down to 7) was performed. Implementing this resulted in no significant effect on the RMSE, and showed that much the same prediction accuracy could be obtained by a smaller subset of indices. Importantly, the random forest regression approach identified many important variables that were not good predictors according to their linear regression statistics. Overall, the research illustrates the promise in using established vegetation indices as input variables in a machine learning approach for the enhanced estimation of Chlt from hyperspectral data.

Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data

2011 ◽  
Vol 66 (1) ◽  
pp. 81-102 ◽  
Author(s):  
Richard Lucas ◽  
Katie Medcalf ◽  
Alan Brown ◽  
Peter Bunting ◽  
Johanna Breyer ◽  
...  
Keyword(s):  
Phase 1 ◽  
Sensor Data ◽  
Satellite Sensor Data ◽  
Satellite Sensor
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