Abstract
Monitoring crop areas and yields is crucial for food security and agriculture management across the world. In this paper, we mapped the biomass and yield of winter wheat using the new Project for On-Board Autonomy-Vegetation (PROBA-V) products in the North China Plain (NCP). First, the daily 100-m land surface reflectance was generated by fusing the PROBA-V 100-m and 300-m S1 products. Our results show that the blended data exhibited high correlations with the referenced data (0.71 ≤ R2 ≤ 0.94 for the red band, 0.50 ≤ R2 ≤ 0.95 for the near-infrared band, and 0.88 ≤ R2 ≤ 0.97 for the shortwave infrared band). The time-series Normalized Difference Vegetation Index (NDVI) derived from the synthetic reflectance was then clustered for winter wheat identification. The overall classification accuracy was between 78% and 87%, with a kappa coefficient above 0.57, which was 10%–20% higher than the classification accuracy using the 300-m data. Finally, a light use efficiency model was employed to estimate the biomass and yield. The estimation results were closely related to the field-measured biomass and yield, with high R2 and low root mean square errors (RMSE) (0.864 ≤ R2 ≤ 0.871 and 168 ≤ RMSE ≤ 191 g/m2 for biomass; and 0.631 ≤ R2 ≤ 0.663 and 41.8 ≤ RMSE ≤ 62.8 g/m2 for yield). This paper shows the strong potential of using PROBA-V 100-m data to enhance the spatial resolution of PROBA-V 300-m data and because the proposed framework in this study was based only on the relatively high spatio-temporal resolution PROBA-V data and achieved favorable results, it provides a novel approach for crop areas and yields estimation utilizing the relatively new data set.
Highlights
Food is necessary for human survival and development, and access to sufficient food is a basic right every person should enjoy
The accuracy assessment of the synthetic images was undertaken using a set of images independent from those used as ESTARFM inputs, and the five statistical criteria shown in Table 4 were chosen as the evaluation indicators
The proposed framework would be less effective in the regions regions with successive clouds because if the base high-spatial-resolution or coarse-spatial-resolution regions with successive clouds because if the base high-spatial-resolution or coarse-spatial-resolution with successive clouds because if the base high-spatial-resolution or coarse-spatial-resolution images images are heavily influenced by unfavorable weather conditions, the predicted high-spatialimages areinfluenced heavily influenced by unfavorable weatherthe conditions, predicted high-spatialare heavily by unfavorable weather conditions, predicted the high-spatial-resolution data resolution data might not be so accurate and the crop classification and yield estimation resolution data might not be so accurate and the crop classification and yield estimation might not be so accurate and the crop classification and yield estimation results will be results will be affected
Summary
Food is necessary for human survival and development, and access to sufficient food is a basic right every person should enjoy. Because of constraints imposed by multiple factors, such as population growth and poverty, food security is still one of the most important challenges in many parts of the world [1,2]. The latest FAO report indicates that the trend in global hunger reduction continues, approximately one in nine people across the world remains suffered from nutrient deficiency in 2012–2014, in developing countries [3]. Wheat is the world’s third largest food crop in terms of production, and it is traded frequently between different countries to satisfy the growing demands of food-deficit countries [4]. The accurate and timely estimation of wheat areas and yields is important so that policy-making departments and international communities can make reasonable decisions.
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