Multi-Year Mapping of Maize and Sunflower in Hetao Irrigation District of China with High Spatial and Temporal Resolution Vegetation Index Series  
[PERSON] and [PERSON]  
1 State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering,  
Tsinghua University, Beijing 100084, China; [EMAIL_ADDRESS]  
1  
Footnote 1: email: {  
resolution and large coverage area [8; 9; 10]. Due to their capability in characterizing crop conditions, various remote sensing-based methods had been widely used as effective tools for crop identification and cultivation area estimation [11; 12]. In order to achieve good classification results, two important issues need to be considered: the selection of an appropriate classifier and the sources of remote sensing images [13]. The most commonly used classification method by remote sensing is supervised classification with the basic unit of a pixel, which divides each pixel into a specified type of crop according to the training data based on the sampling points [12; 14; 15]. Three supervised classification algorithms based on machine learning are frequently used, including the multilayer perceptron neural networks [13; 16], the support vector machine [17; 18], and the random forest [19; 20]. These classifiers are non-parametric algorithms, which are black-box models without a fixed classification formula. Therefore, one classifier identified in a study area cannot be applied directly to other similar areas, which would affect the generalization of the classifier. A key issue for a non-parametric classifier is the determination of input characteristic variables. The large number of input variables may result in over-fitting and the Hughes phenomenon [21; 22]. Another disadvantage that cannot be ignored for a non-parametric classifier is the huge computation caused by too much data, which not only requires a high computer property but also needs a long computation time [2; 23]. Accordingly, in order to improve the effectiveness and robustness of crop identification methods, the best indicators of crop growth characteristics need to be identified. Recently, the crop phenological metric is widely used as an approach to improve the crop classifiers [24; 25; 26]. Vegetation phenology reflects the growth characteristics of each crop. Therefore, different crops could be identified according to the character of crop phenology. Previous studies have shown that vegetation indexes retrieved from remotely sensed data, such as the Enhanced Vegetation Index (EVI) [27; 28] and Normalized Difference Vegetation Index (NDVI) [26; 29], can accurately describe the crop phenology. The NDVI is widely used due to its simple calculation [30] and readily available NDVI products [16; 31]. Furthermore, during the monitoring of the vegetation dynamics in arid and semi-arid areas, the NDVI is more sensitive to soil conditions and atmospheric effects compared with other vegetation indexes. [PERSON] et al. [32] used three Moderate Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indexes including NDVI, EVI and the Soil-Adjusted Vegetation Index (SAVI), to monitor dryland vegetation dynamics. The results showed the spatial deviation of phenological metrics generated from NDVI was the largest. [PERSON] et al. [33] have compared the yield estimation effects of the predictors of NDVI and EVI, and he concluded that NDVI was the most effective predictor for three crops in the study area. This shows that NDVI is more accurate in describing the vegetation growth process. Therefore, the NDVI is used as the primary index for correlating with the phenological metrics. The source of remote sensing data is another key factor for determining the accuracy of crop identification. In the last few decades, Advanced Very High-Resolution Radiometer (NOAA-AVHRR) [31; 34], SPOT VEGETATION product [5; 35], MODIS [36; 37], and Landsat TM/ETM+ [38; 39] have become the main sources of remote sensing data.