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‎data_collection.py

+58-42
Original file line numberDiff line numberDiff line change
@@ -12,44 +12,60 @@ def get_data(long, lat, start_date, end_date):
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Map = geemap.Map()
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15-
geometry1 = ee.Geometry.Point([long,lat])
15+
# geometry1 = ee.Geometry.Point([long,lat])
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#start_date = '2021-01-01'
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#end_date = '2021-06-30'
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# Kankaria Lake, Ahmedabad
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# geometry1 = ee.Geometry.Point([72.6026,23.0063])
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geometry = ee.Geometry.Polygon([
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[72.5986408493042,23.006549566021803],
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[72.59902708740235,23.004890477468116],
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[72.60070078582764,23.003863412427236],
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[72.60040037841797,23.007142092704626],
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[72.60215990753174,23.006668071566512],
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[72.60173075408936,23.003784407100333],
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[72.60366194458008,23.00516699364359],
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[72.60374777526856,23.00686558057643],
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[72.6026748916626,23.00805062856477],
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[72.60082953186036,23.00880115357416],
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[72.59945624084473,23.00809012998513],
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[72.5986408493042,23.006549566021803],
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[72.5986408493042,23.006549566021803],
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[72.59902708740235,23.004890477468116],
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[72.60070078582764,23.003863412427236],
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[72.60040037841797,23.007142092704626],
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[72.60215990753174,23.006668071566512],
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[72.60173075408936,23.003784407100333],
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[72.60366194458008,23.00516699364359],
44-
[72.60374777526856,23.00686558057643],
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[72.6026748916626,23.00805062856477],
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[72.60082953186036,23.00880115357416],
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[72.59945624084473,23.00809012998513],
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[72.5986408493042,23.006549566021803]
49-
])
50-
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Map.addLayer(geometry1)
52-
sentinel = ee.ImageCollection("COPERNICUS/S2_SR").filterBounds(geometry) \
23+
geometry = ee.Geometry.Point([long,lat])
24+
image = ee.ImageCollection("COPERNICUS/S2_SR") \
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.filterBounds(geometry) \
26+
.filter(ee.Filter.lte('CLOUDY_PIXEL_PERCENTAGE',20)) \
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.first()
28+
# Create an NDWI image, define visualization parameters and display.
29+
ndwi = image.normalizedDifference(['B3', 'B8'])
30+
# Mask the non-watery parts of the image, where NDWI < 0.4.
31+
ndwiMasked = ndwi.updateMask(ndwi.gte(0.4))
32+
ndwiMasked1= ndwiMasked.toInt()
33+
# vectors = ndwiMasked1.reduceToVectors({
34+
# 'scale': 30.0,
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# 'geometryType': 'polygon',
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# 'eightConnected': False,
37+
# 'maxPixels':10000000
38+
# })
39+
vectors = ndwiMasked1.reduceToVectors(scale = 30.0, geometryType = 'polygon', eightConnected = False, maxPixels = 10000000, bestEffort=True)
40+
# geometry = ee.Geometry.Polygon([
41+
# [72.5986408493042,23.006549566021803],
42+
# [72.59902708740235,23.004890477468116],
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# [72.60070078582764,23.003863412427236],
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# [72.60040037841797,23.007142092704626],
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# [72.60215990753174,23.006668071566512],
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# [72.60173075408936,23.003784407100333],
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# [72.60366194458008,23.00516699364359],
48+
# [72.60374777526856,23.00686558057643],
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# [72.6026748916626,23.00805062856477],
50+
# [72.60082953186036,23.00880115357416],
51+
# [72.59945624084473,23.00809012998513],
52+
# [72.5986408493042,23.006549566021803],
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# [72.5986408493042,23.006549566021803],
54+
# [72.59902708740235,23.004890477468116],
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# [72.60070078582764,23.003863412427236],
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# [72.60040037841797,23.007142092704626],
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# [72.60215990753174,23.006668071566512],
58+
# [72.60173075408936,23.003784407100333],
59+
# [72.60366194458008,23.00516699364359],
60+
# [72.60374777526856,23.00686558057643],
61+
# [72.6026748916626,23.00805062856477],
62+
# [72.60082953186036,23.00880115357416],
63+
# [72.59945624084473,23.00809012998513],
64+
# [72.5986408493042,23.006549566021803]
65+
# ])
66+
67+
Map.addLayer(geometry)
68+
sentinel = ee.ImageCollection("COPERNICUS/S2_SR").filterBounds(vectors) \
5369
.filterDate(start_date,end_date) \
5470
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',20)) \
5571
.median()
@@ -87,7 +103,7 @@ def get_data(long, lat, start_date, end_date):
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col = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2') \
89105
.filterDate(start_date,end_date) \
90-
.filterBounds(geometry).median()
106+
.filterBounds(vectors).median()
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92108
temp = col.select('ST_B.*').multiply(0.00341802).add(149.0).subtract(273.15).rename('temp')
93109

@@ -98,10 +114,10 @@ def get_data(long, lat, start_date, end_date):
98114
starting = start_date
99115
ending = end_date
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101-
data = ee.ImageCollection('COPERNICUS/S3/OLCI').filterDate(starting, ending).filterBounds(geometry)
117+
data = ee.ImageCollection('COPERNICUS/S3/OLCI').filterDate(starting, ending).filterBounds(vectors)
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rgb = data.select(['Oa08_radiance', 'Oa06_radiance', 'Oa04_radiance'])\
104-
.median().multiply(ee.Image([0.00876539, 0.0123538, 0.0115198])).clip(geometry)
120+
.median().multiply(ee.Image([0.00876539, 0.0123538, 0.0115198])).clip(vectors)
105121
dm_2021_Jan_August_test = rgb.select('Oa08_radiance').divide(rgb.select('Oa04_radiance')).rename('dom')
106122
dom2 = rgb.select('Oa08_radiance').divide(rgb.select('Oa04_radiance')).mask(mndwitr)
107123
Map.addLayer(dom2,{'min':0,'max':0.8,'palette':['green','red','yellow']},'Dissolved organic matter')
@@ -118,7 +134,7 @@ def get_data(long, lat, start_date, end_date):
118134
# apply reducer to list
119135
latlon = latlon.reduceRegion(
120136
reducer=ee.Reducer.toList(),
121-
geometry=geometry,
137+
geometry=vectors,
122138
scale=100,
123139
tileScale = 16)
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# get data into three different arrays
@@ -129,7 +145,7 @@ def get_data(long, lat, start_date, end_date):
129145
# apply reducer to list
130146
latlon = latlon.reduceRegion(
131147
reducer=ee.Reducer.toList(),
132-
geometry=geometry,
148+
geometry=vectors,
133149
scale=100,
134150
tileScale = 16)
135151
# get data into three different arrays
@@ -140,7 +156,7 @@ def get_data(long, lat, start_date, end_date):
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141157
latlon = latlon.reduceRegion(
142158
reducer=ee.Reducer.toList(),
143-
geometry=geometry,
159+
geometry=vectors,
144160
scale=100)
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146162
data_lst = np.array((ee.Array(latlon.get("temp")).getInfo()))
@@ -150,7 +166,7 @@ def get_data(long, lat, start_date, end_date):
150166
# apply reducer to list
151167
latlon = latlon.reduceRegion(
152168
reducer=ee.Reducer.toList(),
153-
geometry=geometry,
169+
geometry=vectors,
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scale=100)
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# get data into three different arrays
156172
data_ndti = np.array((ee.Array(latlon.get("ndti")).getInfo()))
@@ -159,7 +175,7 @@ def get_data(long, lat, start_date, end_date):
159175
# apply reducer to list
160176
latlon = latlon.reduceRegion(
161177
reducer=ee.Reducer.toList(),
162-
geometry=geometry,
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geometry=vectors,
163179
scale=100)
164180
# get data into three different arrays
165181
data_ndsi = np.array((ee.Array(latlon.get("ndsi")).getInfo()))
@@ -168,7 +184,7 @@ def get_data(long, lat, start_date, end_date):
168184
# apply reducer to list
169185
latlon = latlon.reduceRegion(
170186
reducer=ee.Reducer.toList(),
171-
geometry=geometry,
187+
geometry=vectors,
172188
scale=100)
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# get data into three different arrays
174190
data_ndci = np.array((ee.Array(latlon.get("ndci")).getInfo()))
@@ -177,7 +193,7 @@ def get_data(long, lat, start_date, end_date):
177193
# apply reducer to list
178194
latlon = latlon.reduceRegion(
179195
reducer=ee.Reducer.toList(),
180-
geometry=geometry,
196+
geometry=vectors,
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scale=100,
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tileScale = 16)
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# get data into three different arrays
@@ -187,7 +203,7 @@ def get_data(long, lat, start_date, end_date):
187203
# apply reducer to list
188204
latlon = latlon.reduceRegion(
189205
reducer=ee.Reducer.toList(),
190-
geometry=geometry,
206+
geometry=vectors,
191207
scale=100)
192208
# get data into three different arrays
193209
data_ph = np.array((ee.Array(latlon.get("ph")).getInfo()))

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