Comparing the Performance of Multispectral and Hyperspectral Images for Estimating Vegetation Properties

Multispectral and hyperspectral data have been used to investigate various land cover characteristics. Hyperspectral data have more potential to retrieve information of ground features than multispectral data; however, their limited availability leads to fewer studies in the literature. This research aims to acquire both multispectral and hyperspectral images and compare their performance for estimating vegetation properties (i. e., chlorophyll content). A hyperspectral image (with 325 bands in the visible near infrared (NIR) range) was obtained using a compact hyperspectral sensor mounted on a manned helicopter. A modified camerabased three-band image (with blue, green, and NIR) and a RedEdge sensor-based five-band image (with blue, green, red, red edge, and NIR) were simulated using the hyperspectral image. These three images were compared for the estimation of vegetation chlorophyll content. Partial least square (PLS) regression and random forest regression (RFR) were both applied to estimate chlorophyll using image-derived variables, including vegetation indices and imagery textures. Results showthat the RedEdge image achieved good accuracy (R-2 similar to 0.80, RMSE similar to 14 mu g/cm(2)), close to the accuracy of using the hyperspectral image (R-2 similar to 0.81, RMSE similar to 13 mu g/cm(2)). The extra bands in the hyperspectral image did not substantially improve chlorophyll estimation. The three-band multispectral image yielded the lowest accuracy (e. g., R-2 similar to 0.42, RMSE similar to 24 mu g/cm(2)). The RFR performed consistently better than the PLS, owing to its use of randomly-selected training data and predictor variables to build regression trees. These results are expected to provide insights into future studies on the selection of remote sensing images for different monitoring needs.