## 4 Areas that Require Further Research
### Use of passive microwaves for moisture status
#### Data Fusion  
Information about the agricultural areas is currently being captured by a number of satellite-based sensors of different spatial, spectral, temporal, and radiometric characteristics. It would be beneficial to utilize information from multiple sources to more effectively map irrigated areas and the concept of merging data for the same scene from different sensors is called data fusion. The motivation behind data fusion is to generate an interpretation of the scene not obtainable with data from a single sensor, or to reduce the uncertainty associated with the data from individual sensors [98]. For example, for an image segmentation or classification task, the goal of fusing data from different sensors is to reduce the classification error rate obtained by single source classification. [PERSON] and [PERSON] [98] suggest that before being able to implement and use an image fusion approach some critical questions need to be answered by the user including: What is the objective/application of the user? Which types of data are the most useful for meeting these needs? Which is the 'best' technique of fusing these data types for that particular application? What are the necessary pre-processing steps involved? Which combination of the data is the most successful? These and other questions comprise a large number of parameters to be considered. While there are not many examples of data fusion for classification problems in irrigated agricultural settings, a small review of the existing methodologies here is useful. In general, data fusion techniques can be grouped into two classes: (i) Colour related techniques, and (ii) Statistical/numerical methods [98]. The first comprises the color composition of three image channels in the RGB color space as well as more sophisticated color transformations. Statistical approaches, on the other hand, are developed on the basis of channel statistics including correlation and filters. Techniques like PCA and regression belong to this group. The numerical methods follow arithmetic operations such as image differencing and ratios but also adding of a channel to other image bands. A form of sophisticated numerical approach uses wavelets in a multiresolution environment. For example, [99] uses this wavelet approach to merge Radar data with information from optical sensors to improve the classification accuracies. The premise here is that while each of these data types are not sufficient in identifying irrigated areas alone, their joint information is powerful and improve classification accuracies [98,99]. In addition to these forms of traditional image fusion methods, there is an emerging trend in the literature of what would be called \"information merging\" from different satellite sources. The main difference here is the goal of image or information fusion is not to generate pixels with improved information content from multiple data sources but rather to use information from multiple sources to make informed decisions. Some of the best examples of this new form of data fusion are provided by [7,21,56]. For example, [PERSON] _et al._[7] used information from coarse resolution high temporal frequency observations as a guide to determining the ideal acquisition timing of high spatial resolution data for mapping purposes. While irrigated area mapping was accomplished solely based on high resolution data, the fact that an additional sensor data with different capabilities was used to steer the acquisition times is sufficient to consider this application a form of data fusion. Another great example is provided by [21] who used temporal profiles from one set of observations to interpret land cover type in another sensor data. This form of information fusion is important in irrigated area mapping since the phenology (temporal profiles) of crops are crucial to their identification and mapping--not only from other crops, but also from other vegetation.