Abstract
Abstract. Sugarcane is a perennial crop that contributes to nearly 80% of the global sugar-based products. Therefore, sugarcane growers and food companies are seeking ways to address the concerns related to sugarcane crop yield and health. In this study, a spatial and spectral analysis on the peak growth stage of the sugarcane fields in Bundaberg, Queensland, Australia is performed using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) derived from high-resolution WorldView-2 (WV2) images and multispectral Unmanned Aerial Vehicle (UAV) images. Two topics are chosen for this study: 1) the difference and correlation between NDVI and NDRE that are commonly used to estimate Leaf Area Index, a common crop parameter for the assessment of crop yield and health stages; 2) the impact of spatial resolution on the systematic difference in the abovementioned two Vegetation Indices (VIs). The statistical correlation analysis between the WV2 and UAV images produced correlation coefficients of 0.68 and 0.71 for NDVI and NDRE, respectively. In addition, an overall comparison of the WV2 and UAV-derived VIs indicated that the UAV images produced a better accuracy than the WV2 images because UAV can effectively distinguish various status of vegetation owing to its high spatial resolution. The results illustrated a strong positive correlation between NDVI and NDRE, each derived from the WV2 and UAV images, and the correlation coefficients were 0.81 and 0.90, respectively, i.e. the correlation between NDVI and NDRE is higher in the UAV images than the WV2 images.
Highlights
With the increase of the world population, the amount of food and farming sources needs to be upgraded too
We performed a comparative analysis on Normalized Difference Vegetation Index (NDVI) and Normalized Difference RedEdge (NDRE), both obtained from the Unmanned Aerial Vehicles (UAVs) and WV2 data
To understand the relationship of the spatial patterns between the NDVI and NDRE values derived from the two sensors, the UAV data have been resampled to the same spatial resolution as the WV2 data (Figure 4)
Summary
With the increase of the world population, the amount of food and farming sources needs to be upgraded too. Decisions on the farming operations and the maximization of the efficiency of outputs in PA need high spatial and temporal resolution data of crop status (Huang et al, 2013). Such data could be gained from Remote Sensing (RS) platforms because. To the best of our knowledge, no previous study has mapped LAI in a sugarcane field with both NDRE and NDVI in order to compare these VIs. In addition, the drawbacks and advantages for characterizing the sugarcane LAI from UAV images and from very high spatial resolution satellite images remain a knowledge gap in the existing literature. The two major specific objectives are: to analyse NDVI and NDRE for UAV and WV2 for sugarcane in the study area and compare the overall performance of UAV and WV2
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