diff --git a/city_metrix/layers/esa_ndvi.py b/city_metrix/layers/esa_ndvi.py
deleted file mode 100644
index ad117d1..0000000
--- a/city_metrix/layers/esa_ndvi.py
+++ /dev/null
@@ -1,203 +0,0 @@
-def test_esa_ndvi():
-    count = (
-        EsaNdvi()
-        .get_data(BBOX_BRAZIL_LAURO_DE_FREITAS_1)
-        .count()
-    )
-    assert count
-
-
-
-import ee
-import xee
-import xarray as xr
-from shapely.geometry import box
-
-from .layer import Layer, get_image_collection
-
-""""
-NDVI = Sential-2 Normalized Difference Vegetation Index
-"""
-class EsaNdvi(Layer):
-    def __init__(self, start_date="2013-01-01", end_date="2023-01-01", **kwargs):
-        super().__init__(**kwargs)
-        self.start_date = start_date
-        self.end_date = end_date
-
-    # https://gis.stackexchange.com/questions/457822/retrieving-maximum-ndvi-for-a-year-and-associated-date-for-each-pixel-in-google
-    def get_data(self, bbox):
-        ndvi = self.get_ndvi(bbox)
-
-        # NDVIthreshold = 0.4  # decimal
-        # year = 2020
-        #
-        # yearStr = str(year)
-        # NDVIthresholdStr = str(NDVIthreshold)
-        # startdate = '' + yearStr + '-01-01'
-        # enddate = '' + yearStr + '-12-31'
-        #
-        # s2 = ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
-        #
-        # green = s2.filterDate(startdate, enddate).map(self.addNDVI)
-
-        # dataset = ee.Image("NASA/NASADEM_HGT/001")
-        # nasa_dem = ee.ImageCollection(ee.ImageCollection(dataset)
-        #                               .filterBounds(ee.Geometry.BBox(*bbox))
-        #                               .select('elevation')
-        #                               .mean()
-        #                               )
-        # data = get_image_collection(nasa_dem, bbox, 30, "NASA DEM").elevation
-
-        return 2 # data
-
-    # def get_ndvi(self, bbox):
-    #     # centroid = box(*bbox).centroid
-    #
-    #     NDVIthreshold = 0.4  # decimal
-    #     year = 2020
-    #
-    #     yearStr = str(year)
-    #     NDVIthresholdStr = str(NDVIthreshold)
-    #     startdate = '' + yearStr + '-01-01'
-    #     enddate = '' + yearStr + '-12-31'
-    #
-    #     dataset = ee.ImageCollection("COPERNICUS/S2_HARMONIZED")
-    #
-    #     # green = s2.filterDate(startdate, enddate).map(self.addNDVI)
-    #
-    #     # dataset = ee.ImageCollection("ECMWF/ERA5/DAILY")
-    #
-    #     def addNDVI(image):
-    #         ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
-    #         return image.addBands(ndvi)
-    #
-    #     ndvi = (dataset
-    #             .filter(ee.Filter.And(
-    #         ee.Filter.date(self.start_date, self.end_date),
-    #                 ee.Filter.bounds(ee.Geometry.BBox(*bbox))))
-    #                       )
-    #
-    #     green = dataset.map(addNDVI)
-    #     green = green.qualityMosaic('NDVI').select('NDVI').float();
-    #     greenScale = green.projection().nominalScale()
-    #     # green = green.addBands(ee.Image(year).rename('time_start'))
-    #     # Map.addLayer(green,{},"NDVI")
-    #     greenmask = green.updateMask(green.select('NDVI').gte(NDVIthreshold))
-    #
-    #     g=2
-
-
-import ee
-
-# Initialize GEE
-ee.Initialize()
-
-# Define the bounding box (lon1, lat1, lon2, lat2)
-bbox = ee.Geometry.Rectangle([lon1, lat1, lon2, lat2])
-
-# Define the date range
-start_date = 'YYYY-MM-DD'
-end_date = 'YYYY-MM-DD'
-
-# Load Sentinel-2 data
-collection = ee.ImageCollection('COPERNICUS/S2').filterBounds(bbox).filterDate(start_date, end_date)
-
-# Calculate NDVI
-def calculate_ndvi(image):
-    ndvi = image.normalizedDifference(['B8', 'B4'])
-    return image.addBands(ndvi.rename('NDVI'))
-
-ndvi_collection = collection.map(calculate_ndvi)
-
-# Export the data
-task = ee.batch.Export.image.toDrive(
-    image=ndvi_collection.select('NDVI'),
-    description='NDVI_export',
-    folder='your_folder',
-    fileNamePrefix='ndvi',
-    region=bbox,
-    scale=10  # Set the desired resolution (e.g., 10 meters)
-)
-
-task.start()
-
-
-# import ee
-#
-# # Initialize GEE
-# ee.Initialize()
-#
-# # Define the bounding box (lon1, lat1, lon2, lat2)
-# bbox = ee.Geometry.Rectangle([lon1, lat1, lon2, lat2])
-#
-# # Define the date range
-# start_date = 'YYYY-MM-DD'
-# end_date = 'YYYY-MM-DD'
-#
-# # Load Sentinel-2 data
-# collection = ee.ImageCollection('COPERNICUS/S2').filterBounds(bbox).filterDate(start_date, end_date)
-#
-# # Calculate NDVI
-# def calculate_ndvi(image):
-#     ndvi = image.normalizedDifference(['B8', 'B4'])
-#     return image.addBands(ndvi.rename('NDVI'))
-#
-# ndvi_collection = collection.map(calculate_ndvi)
-#
-# # Export the data
-# task = ee.batch.Export.image.toDrive(
-#     image=ndvi_collection.select('NDVI'),
-#     description='NDVI_export',
-#     folder='your_folder',
-#     fileNamePrefix='ndvi',
-#     region=bbox,
-#     scale=10  # Set the desired resolution (e.g., 10 meters)
-# )
-#
-# task.start()
-
-
-
-
-# import ee
-#
-# # Initialize GEE
-# ee.Initialize()
-#
-# # Define your region of interest (ROI) as a polygon
-# asset_polygon = ee.Geometry.Polygon(
-#     [[[-122.5, 37.5],
-#       [-122.5, 37.6],
-#       [-122.4, 37.6],
-#       [-122.4, 37.5]]])
-#
-# # Define the year for which you want to retrieve data
-# year = 2023
-#
-# # Load Sentinel-2 Surface Reflectance (SR) data
-# sentinel2 = ee.ImageCollection('COPERNICUS/S2_HARMONIZED') \
-#     .filterBounds(asset_polygon) \
-#     .filterDate(f'{year}-01-01', f'{year}-12-31') \
-#     .select(['B4', 'B8'])  # Red (B4) and Near-Infrared (B8) bands
-#
-# # Compute NDVI for each image
-# def calculate_ndvi(image):
-#     ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi')
-#     return image.addBands(ndvi)
-#
-# sentinel2_with_ndvi = sentinel2.map(calculate_ndvi)
-#
-# # Compute the maximum NDVI and acquisition date for each pixel
-# max_ndvi = sentinel2_with_ndvi.qualityMosaic('ndvi').select('ndvi')
-# acquisition_date = sentinel2_with_ndvi.qualityMosaic('system:time_start').select('system:time_start')
-#
-# # Export the rasters as single-band images to Google Drive
-# task_max_ndvi = ee.batch.Export.image.toDrive(
-#     image=max_ndvi,
-#     description=f'S2_max_ndvi_{year}',
-#     folder='gee',
-#     scale=10,
-#     region=asset_polygon.geometry()
-# )
-#
-# task_acquisition_date = ee.batch
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