## 4 Areas that Require Further Research
### Use of passive microwaves for moisture status  
Irrigated areas, by definition, contain increased soil moisture due to artificial watering of fields. Microwave remote sensing can be used to estimate soil moisture and, by extension, irrigation, on the basis of the large contrast that exists between the dielectric constant values for dry and wet soils [88,89]. Temporal monitoring of water availability at the soil root zone during growth periodscould provide a way to pre-determine irrigated and non-irrigated fields. This information can then be refined using optical or radar sensors at increased spatial resolutions. Because microwaves penetrate clouds, microwave remote sensing also presents a good alternative to optical remote sensing for monitoring irrigation distribution, as well as the health and water needs of irrigated areas in humid locations with frequent cloud cover. One limitation of passive microwave data is their coarse spatial resolution (on the order of 25 km on a side). Nevertheless, these data can be used to pre-determine the locations of irrigated areas in a broader context using remote sensing. _Classification algorithms with many-to-one mapping capabilities_  
Because of the number of images to be processed and the need for multi-temporal data, the use of more automated methods based on supervised classification using machine learning algorithms is important. Neural networks (specifically Fuzzy ARTMAP: [90-92]), decision trees [24], and support vector machines (SVM) [93] are particularly powerful in handling information from multiple sources or dates to generate reliable classification results. One of the strengths of these machine-learning algorithms is the \"many-to-one\" mapping capability, meaning that any given output class can have many spectral manifestations, each of which is preserved internally within the trained set. For irrigation mapping, this is particularly useful, as most studies use only a limited number of classes and the non-irrigated class could encompass multiple land cover classes. The idea of many spectral manifestations can also be extended into the temporal domain, where irrigated productive agricultural fields and barren non-irrigated fields each with particular multi-date NDVI trajectories can be fed into a training set as individual training samples to classify the amount of irrigated land per study period. _Object-oriented classification_  
When using high spatial resolution data to map irrigated agricultural lands, traditional spectral based methods may result in rather poor or incorrect classification. This is because in this type of data, much information is contained in spatial relations of pixels and not in their spectral response or temporal change. Therefore, methods that exploit the relationship between a group of pixels in an attempt to generate improved classification are promising when analyzing high resolution data in fragmented environments. This form of classification is called the object-oriented approach. Unfortunately, there are no examples of application of object-oriented classification methods to map irrigated landscapes in the literature, although a few examples exist for mapping land cover types in cultivated settings. In an object-oriented classification process, image objects (e.g., agricultural fields) can be created by means of automatic segmentation algorithms, or by ancillary sources of information such as cadastral cartography. In general, a segmentation algorithm would produce a space division conditioned by sensor attributes instead of geographic characteristics. Image classification by parcels has been widely used for agricultural applications [94,95]. Object-based image analysis can be accomplished following two different approaches: prior to classification (per-parcel) and after per-pixel classification. In the first approach, descriptive features of each parcel are first calculated and then parcels are classified as a whole using any of the available classification algorithms.