Dear Laith,

The question you raised is actually a bit ambiguous. We have many types of land cover, just based on one index, it is unlikely possible to characterize the full spectrum of land cover. However, we do have some preferred indices, or rather, thematic indicators, extracted from the remote sensing images, for our research, for example, NDVI, EVI, SAVI, SARVI, GDVI for dryland, LAI, WDRVI, Tasseled Cap Greenness, etc., for vegetation cover, NDII, NDWI and Tasseled Cap Wetness for moisture, Tasseled Cap Brightness, the 1st Principal Component and albedo for evaluating the bareness or brightness of soil. Thus, it is up to you to decide to choose in terms of your research target. Usually instead of using a single index, we can select several ones for achieving the objective. We may also integrate some biophysical indicators such as elevation, slope, land surface temperature, and phenological features to complete the research.

Wish this can help you sort out your ideas and procedure for your research.

All the best

R language provides more resource for classification. You can use several classification method by selecting set of samples.

Dear Laith,

The question you raised is actually a bit ambiguous. We have many types of land cover, just based on one index, it is unlikely possible to characterize the full spectrum of land cover. However, we do have some preferred indices, or rather, thematic indicators, extracted from the remote sensing images, for our research, for example, NDVI, EVI, SAVI, SARVI, GDVI for dryland, LAI, WDRVI, Tasseled Cap Greenness, etc., for vegetation cover, NDII, NDWI and Tasseled Cap Wetness for moisture, Tasseled Cap Brightness, the 1st Principal Component and albedo for evaluating the bareness or brightness of soil. Thus, it is up to you to decide to choose in terms of your research target. Usually instead of using a single index, we can select several ones for achieving the objective. We may also integrate some biophysical indicators such as elevation, slope, land surface temperature, and phenological features to complete the research.

Wish this can help you sort out your ideas and procedure for your research.

All the best

Dear Laith,

I agree with Wu, however NDVI is the most used index for extracting vegetation cover.

Dear Laith,

i think NDVI is one of the most important index to detect and extract vegetation

NDVI index can improve the extraction of vegetation

Dear Dr. Laith,

I agree with Prof. Wu. Additionally, it appeared that the MSAVI2 is more accurate in delineating the vegetative cover in most of the Iraqi lands. You could have a look at our published papers those related to the drought in Iraq.

Best regards.

You may want to look at:

Preprint A New Model of “Color” Vision for Remote Sensing