Abstract
With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.
Highlights
Fractional vegetation cover (FVC) is a canopy architecture parameter that represents the fraction of the land surface covered by green foliage in the two-dimensional plane [1].FVC is a very important parameter for describing the vegetation cover on the Earth’s surface, and is one of the important indicators of ecosystem change [2]
The FVC values of row crops estimated by U-spectral mixture analysis (SMA)-2, C-SMA-2, U-SMA-5, and C-SMA-5 (Figure 4c–f) are significantly higher than the FVC values of row crops estimated by the modified four-stream (MFS) + backward propagation neural networks (BPNNs), PROSAIL + BPNN, and empirical method (Figure 4a,b,g)
To find a reasonable algorithm for estimating the FVC of row crops in the high spatial resolution images, further serving precision agriculture, this study introduced a canopy reflectance model of row crops coupled with BPNNs to estimate the FVC of row crops
Summary
Fractional vegetation cover (FVC) is a canopy architecture parameter that represents the fraction of the land surface covered by green foliage in the two-dimensional plane [1].FVC is a very important parameter for describing the vegetation cover on the Earth’s surface, and is one of the important indicators of ecosystem change [2]. Fractional vegetation cover (FVC) is a canopy architecture parameter that represents the fraction of the land surface covered by green foliage in the two-dimensional plane [1]. Precision agriculture requires remote sensing as support [3,4]. In the estimation of FVC during the crop growing season, high spatial resolution remote sensing data is a good choice for precision agriculture [5]. The estimation of the FVC of row crops is more complicated than that of continuous crops (canopy state at the later stage of crop growth) in the high spatial resolution remote sensing data. Developing the estimated methods of the FVC of row crops are urgently needed in remote sensing
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