## 5 Discussion
### Advantages and Shortcomings  
The most significant advantage of the proposed method is that it relies only on the identification of cropped and uncropped arable land. It is much easier to identify uncropped arable land than specific crops, especially when only incomplete growing season remote sensing data is available [3,44]. The accuracy for separating cropped and uncropped arable land was excellent (higher than 97%) in our research, because the reflectance features and seasonal dynamic characteristics of cropped and uncropped arable land are distinctly different [32,45]. Previous research primarily explored different methods, both supervised and unsupervised, for crop classification. However, most of these methods were only feasible for identifying crops in small regions or areas with relatively low farmland fragmentation [46]. To achieve high accuracy for discriminating between different crops, high resolution images with a pixel size several times smaller than the land parcel size, as well as multi-temporal images are required [47]. These are unaffordable over large regions; consequently, it is impracticable to use crop type distribution maps to produce adjusted NDVI data for crop condition monitoring. The second advantage of our approach is that it retrieved UALR-adjusted NDVI for pure cropped arable land by using a two-step approach. For the first step, MODIS pixels were considered as a mixture of only two endmembers--cropped and uncropped arable land. According to the linear mixing model (LMM), the band reflectance of a MODIS pixel is assumed to be a linear combination of uncropped and cropped arable land, weighted by the abundance of each endmember in the pixel. The reflectance of the cropped arable land endmember was retrieved by using a linear unmixing technique applied to the reflectance of the red and NIR bands. In the second step, NDVI values for the cropped arable land (UALR-adjusted NDVI) endmember were calculated using the endmember reflectance. In  
Figure 11: _Cont._previous research, the NDVI value was simply assumed to be a linear combination of endmembers (cropped and uncropped arable land) NDVI [32], which introduces more uncertainty and larger errors than when an unmixing technique is applied to the band reflectance images. The proposed method for crop condition assessment is transferable and directly applicable to other regions at different spatial scales as long as multi-temporal high resolution images and a timely updated land-cover map are available. Given these data, the method can be incorporated into crop monitoring systems. However, there are still some potential difficulties, which are not considered in this paper. It is a great challenge to obtain timely updated land-cover map at a high spatial resolution. If a timely and accurate updated land-cover map is not available, an out-of-date land-cover map, for instance a land-cover map produced five years ago, can also contribute to the crop assessment, but will introduce more error and uncertainty to the results, because of the land-cover change from year to year. Another concern is the impacts of weather condition on the quality of remote sensing images. This is the common disadvantage of optical remote sensing techniques [48]. Fortunately, there were only a few cloudlets in some of the HJ-1 CCD images we acquired in our research. In the regions with persistent cloud cover, cloud should be first identified and excluded in order to minimize the influence on crop assessment. Overall, the proposed method is simple, operational and can be incorporated into existing crop monitoring systems. Further enhancements are certainly possible. This research assumes that the reflectance of the red and NIR bands for uncropped arable land is relatively constant throughout the study area and that the distribution of uncropped arable land does not change much during the winter crops growing season. In the future, the band reflectance of uncropped arable land will be given values based on different soil and climatic characteristics.