# Longer the duration, Longer it will take for Processing and downloading of Images Define starting and ending year of study (Both year Inclusive).# i am taking shapefile for plain areas of NepalĪoi = geemap.shp_to_ee(study_area_shp).geometry() filter(ee.Filter.eq('ADM0_NAME','Nepal')).geometry() # adjust indentation here, May get error # aoi = ee.FeatureCollection("FAO/GAUL/2015/level0") \ # I have taken level 0 data for country data from FAO datasets Import data for declaration of area of interest (AOI).# Import request package later used for Downloading data # Authenticate the earthengine with credentials Import the GEE API and Authenticate import ee, geemap Let's dive-in directly to the code segment with Google Earth Engine (GEE) python Api. But I have not yet tested this for large images that might not be possible due to GEE restriction and is not recommended. This article will be the perfect solution for those looking for a way to download satellite images from Google Earth Engine (GEE). I would never suggest downloading the Satellite data unless absolutely necessary. I used Google Earth Engine (GEE) to download the data into my local device and prepare the dataset in the desired format for Timesat ( A software package to analyze time-series of satellite sensor data). Instead, I had no options to download MODIS EVI data (250 m resolution, 8 Day temporal resolution-terra and Aqua sensors combined). I was advised to use Timesat ( Welcome to the TIMESAT pages! (lu.se)) for this task. I passed through a similar situation for analyzing MODIS EVI datasets to determine the planting date for Rice. But there might be a time when we can't follow this suggestion. I don't like to be suggesting the data download and do image analysis on your local device. Satellite image processing might be very painful, depending upon the volume of data.
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