scholarly journals High Spatial Resolution and High Temporal Frequency (30-m/15-day) Fractional Vegetation Cover Estimation over China Using Multiple Remote Sensing Datasets: Method Development and Validation

2021 ◽  
Vol 35 (1) ◽  
pp. 128-147
Author(s):  
Xihan Mu ◽  
Tian Zhao ◽  
Gaiyan Ruan ◽  
Jinling Song ◽  
Jindi Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Vegetation Cover ◽  
Method Development ◽  
High Spatial Resolution ◽  
Temporal Frequency ◽  
High Temporal Frequency ◽  
Fractional Vegetation Cover ◽  
Method Development And Validation ◽  
Development And Validation

scholarly journals Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image

2021 ◽  
Vol 13 (19) ◽  
pp. 3874
Author(s):  
Xu Ma ◽  
Lei Lu ◽  
Jianli Ding ◽  
Fei Zhang ◽  
Baozhong He
Keyword(s):  
Spatial Resolution ◽  
Precision Agriculture ◽  
Vegetation Cover ◽  
High Spatial Resolution ◽  
World View ◽  
Canopy Reflectance ◽  
Row Crops ◽  
High Spatial Resolution Image ◽  
Fractional Vegetation Cover ◽  
Reflectance Model

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.

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