Google Earth Engine Developers

Hello, has anyone coded the Sebal algorithm in GEE?

Hello, for a small area of ​​about 50 square kilometers, is it possible to use Sentinel 2 data to calculate NDVI and use Landsat to calculate LST? Can these two be used together to calculate drought indices?

It is possible but later it is restricted to the Landsat resolution for LST! Technically it’s possible but theoretically you cannot have 10 meters resolution of Sentinel-2!

Thank you for your answer. Considering that my area is small, what satellite and sensor is best for calculating NDVI and LST? Is it scientifically problematic if the accuracy is not 10 meters? And what should I do to synchronize Sentinel and Landsat? I would be grateful if you could guide me.

The area of ​​the gardens is 40 square kilometers, and I want to monitor drought using three drought indicators for the past ten years, so that I can examine drought in the three months of June, July, and August, when vegetation cover is highest, in all years.

I want to calculate the soil moisture conditions map of Tamil Nadu, India for every year from 2015 monsoonal season to understand agricultural stress (drought)

I thought your case was a type of research! And it needs more precision and theoretical justification. In your case, you can use Landsat, and that example I wrote in GEE.

Thank you for your response. This is a research paper for writing an article, so I don't know how accurate it should be. Considering that this research is for writing a scientific paper, I would appreciate it if you could tell me if it is possible to use Sentinel images for NDVI and Landsat images for LST? In that case, does it need to be aligned? Or should both NDVI and LST be from Landsat? Or MODIS or another sensor? I would appreciate your valuable opinion on this matter. What type of data do you suggest for scientific work?

Kindly share if you have something

hello What classification method did you use?

The methodology is too complicated for calculating soil moisture from SAR images. The simplest solution is having sparse soil moisture stations over the ROI. Then you can use SAR backscatter around the stations along with the ground measurements of soil moisture to train a machine learning model like random forest. It is important to have at least one station inside each crop type or land cover type over time. The RF will understand spatial and temporal trends per each class of land cover. Then the RF will do the soil moisture estimation for you.

How to check water capacity changes in dam

Hello, how can I use shapefile as mashin learning data ?

hi

Hello You need enough samples from any classes And run machine learning.

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Hello everyone, I wanted to ask if anyone here has used an Android Tablet for GEE related work on the go. I want to get the Honor Pad 10 with a keyboard case for cloud works on the go: e.g., GEE, Google Colab etc. I already have laptops but I want a very lightweight device (500-600 gram) which I can carry in my bag everyday without worrying about battery backup and weight. (I will mainly use it for light works on the go)