128 lines
2.3 KiB
Python
128 lines
2.3 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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get_ipython().run_cell_magic('time', '', '%matplotlib inline\nfrom new_import import *\n')
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# In[2]:
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get_ipython().run_cell_magic('time', '', '# Dask gateway\ncluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1,4))\ndc = datacube.Datacube()\n\n# Configure s3 access\nconfigure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n\nclient\n')
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# In[3]:
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## cấu hình thời gian lấy ảnh và tọa độ
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date_range = ('2022-09-01', '2023-10-01')
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longtitude_range = (105.86575, 105.94120)
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latitude_range = (9.65070, 9.69850)
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# In[4]:
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## truy vấn ảnh vệ tinh sen2
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data = load_data(dc, date_range, longtitude_range, latitude_range)
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notebook_utils.heading(notebook_utils.xarray_object_size(data))
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display(data)
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# In[5]:
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get_ipython().run_cell_magic('time', '', '# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\nresult = mask_clean(data)\nprogress(result)\n')
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# In[6]:
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# Tiến hành tính toán NDVI
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ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')
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ndvi = ds1["NDVI"]
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display(ndvi)
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# In[17]:
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get_ipython().run_cell_magic('time', '', "## tính ndvi theo tháng\naverage_ndvi = ndvi.resample(time='1M').mean().persist()\nprogress(average_ndvi)\n")
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# In[18]:
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# compute average_ndvi
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average_ndvi = average_ndvi.compute()
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# In[9]:
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# cấu hình vh vv file
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name_vh = "ThuanHoa/ThuanHoa_VH.tif"
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name_vv = "ThuanHoa/ThuanHoa_VV.tif"
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# load dữ liệu sen1
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bbox = [105.5, 9.2, 106.4, 10.0]
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time_range = '2022-09-01/2023-10-01'
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dsvh, dsvv = load_sen1(bbox, time_range)
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# In[10]:
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.linear_model import LinearRegression
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from sklearn.ensemble import RandomForestRegressor
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# In[11]:
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mask = ~np.isnan(average_ndvi)
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X_train = np.stack([dsvh.values[mask], dsvv.values[mask]], axis=1)
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y_train = average_ndvi.values[mask]
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# In[12]:
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model = LinearRegression()
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model.fit(X_train, y_train)
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# In[13]:
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X_pred = np.stack([dsvh.values[~mask], dsvv.values[~mask]], axis=1)
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average_ndvi.values[~mask] = model.predict(X_pred)
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# In[14]:
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average_ndvi_filled = xr.DataArray(average_ndvi, dims=average_ndvi.dims)
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# In[16]:
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plt.imshow(average_ndvi_filled.isel(time=1))
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# In[19]:
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plt.imshow(average_ndvi.isel(time=1))
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# In[ ]:
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