Abstract
Accurate estimation of fractional vegetation cover (FVC) from digital images taken by commercially available cameras is of great significance in order to monitor the vegetation growth status, especially when plants are under water stress. Two classic threshold-based methods, namely, the intersection method (T1 method) and the equal misclassification probability method (T2 method), have been widely applied to Red-Green-Blue (RGB) images. However, the high coverage and severe water stress of crops in the field make it difficult to extract FVC stably and accurately. To solve this problem, this paper proposes a fixed-threshold method based on the statistical analysis of thresholds obtained from the two classic threshold approaches. Firstly, a Gaussian mixture model (GMM), including the distributions of green vegetation and backgrounds, was fitted on four color features: excessive green index, H channel of the Hue-Saturation-Value (HSV) color space, a* channel of the CIE L*a*b* color space, and the brightness-enhanced a* channel (denoted as a*_I). Secondly, thresholds were calculated by applying the T1 and T2 methods to the GMM of each color feature. Thirdly, based on the statistical analysis of the thresholds with better performance between T1 and T2, the fixed-threshold method was proposed. Finally, the fixed-threshold method was applied to the optimal color feature a*_I to estimate FVC, and was compared with the two classic approaches. Results showed that, for some images with high reference FVC, FVC was seriously underestimated by 0.128 and 0.141 when using the T1 and T2 methods, respectively, but this problem was eliminated by the proposed fixed-threshold method. Compared with the T1 and T2 methods, for images taken in plots under severe water stress, the mean absolute error of FVC obtained by the fixed-threshold method was decreased by 0.043 and 0.193, respectively. Overall, the FVC estimation using the proposed fixed-threshold method has the advantages of robustness, accuracy, and high efficiency, with a coefficient of determination (R2) of 0.99 and root mean squared error (RMSE) of 0.02.
Highlights
IntroductionWater stress has become a great challenge to maize products globally
The sensitivity of the crop water stress index (CWSI) empirical method for maize water stress has been verified within the same research field in previous studies [46,47]
Significant correlations were found between CWSI and classical water stress indicators
Summary
Water stress has become a great challenge to maize products globally. In research on crop yield, non-destructive monitoring of crop structural traits is of great significance. As one of the most widely used structural traits, fractional vegetation cover (FVC), defined as the proportion of ground surface occupied by green vegetation [1], plays an important role in monitoring vegetation growth status and estimating crop yields (e.g., evapotranspiration and above-ground biomass) [2,3,4]. FVC is a key parameter in the AquaCrop model, which is widely used to simulate crop yield response to water under different irrigation and field management practices [5,6].
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