Hi @gmilcinski , @juangdm.ager and others who could be interested. Just a short note to tell you that I have finished the work for which I used the NDWI values to identify flooded areas. The challenge was to first find an appropiate threshold to differenciate areas mixed with vegetation (mainly rice fields) and see where and when some areas were flooded intentionally (due to agricultural practices). In the following image you can see that using 0 as threshold, some areas that were obviously flooded were not identifed (image 2). So, after some tests, I found that using a threshold of -0.07 did work very well (see image 3):
. See this animation where one can clearly see where and when parcels were flooded. It was also possible to identify, along with local precipitation data and Digital Elevation Models, if the floodings were due to agricultural practices or intense rainfall. This work helps for example, to identify potential areas for biodiversity protection, flood prone areas, and also monitor reservoir water levels. In general, it helps to improve water management in different aspects:
Additionally, in this work I carried out another analysis to identify irrigated parcels during dry months, in an area of the basin where groundwater is the only irrigation source. I did this using another index, in this case Normalized Difference Moisture Index (NDMI). Thanks @juangdm.ager for helping me developing the index in this work. It can really help to identify efficiency of irrigation and crop water stress, as well as improve water allocation and control water use and illegal water abstraction. See in this image how through using and processing this index it is possible to identify parcels that are considered to be having irrigation at certain point in time. The image is from a date after about 100 days with almost no rain, so areas in blue are clearly irrigated parcels:
If someone is interested in learning more about this work, let me know and I could share the extended report. (It is in Spanish, but with Ex. summary in English).
Thanks a lot for you help, looking forward to do more work like this and help others.