如何在 PythonGoogle Colab 中按地区汇总月平均降雨量

我们需要使用to_json()和其他一些东西从shapefile中获取'features'，才能将其作为ee.FeatureCollection:

file_name = '/content/drive/My Drive/Colab Notebooks/DATA_FOLDERS/SHP/gadm41_PRY_2.shx'
districts = gpd.read_file(file_name)

fc = []
for i in range(districts.shape[0]):
    g = districts.iloc[i:i + 1, :] 
    json_dict = eval(g.to_json()) 
    geo_json_dict = json_dict['features'][0] 
    fc.append(ee.Feature(geo_json_dict))

districts = ee.FeatureCollection(fc)

我们还需要在ee.ImageCollection或chirps上使用mosaic():

chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY').filterDate(startDate, endDate).select("precipitation").mosaic()

对于reduceRegions()，我们需要使用ee.Image()和getInfo():

def reduce_image(img):
    img_reduced = ee.Image(img).reduceRegions(
        reducer=ee.Reducer.mean(),
        collection=districts,
        scale=5500,
    ).getInfo()
    return img_reduced

总而言之，我们有:

file_name = '/content/drive/My Drive/Colab Notebooks/DATA_FOLDERS/SHP/gadm41_PRY_2.shx'
districts = gpd.read_file(file_name)

fc = []
for i in range(districts.shape[0]):
    g = districts.iloc[i:i + 1, :] 
    json_dict = eval(g.to_json()) 
    geo_json_dict = json_dict['features'][0] 
    fc.append(ee.Feature(geo_json_dict))

districts = ee.FeatureCollection(fc)

#startDate = ee.Date('2004-01-01')
startDate = ee.Date('2020-01-01')
endDate = ee.Date('2021-12-31')
chirps = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY').filterDate(startDate, endDate).select("precipitation").mosaic()

def reduce_image(img):
    img_reduced = ee.Image(img).reduceRegions(
        reducer=ee.Reducer.mean(),
        collection=districts,
        scale=5500,
    ).getInfo()
    return img_reduced

rainfall_reduced = reduce_image(chirps)

print(rainfall_reduced)项输出(I only included a subset since it's a million lines项):

...
      [-56.286949156999924, -24.767101287999935],
      [-56.28482055699993, -24.76206016599997],
      [-56.28269958499993, -24.757202148999852],
      [-56.28142547599998, -24.754322050999917],
      [-56.28020477399997, -24.751232146999882],
      [-56.28010559099994, -24.751117705999945]]]},
   'id': '217',
   'properties': {'CC_2': 'NA',
    'COUNTRY': 'Paraguay',
    'ENGTYPE_2': 'District',
    'GID_0': 'PRY',
    'GID_1': 'PRY.18_1',
    'GID_2': 'PRY.18.15_1',
    'HASC_2': 'PY.SP.YN',
    'NAME_1': 'San Pedro',
    'NAME_2': 'Yataity del Norte',
    'NL_NAME_1': 'NA',
    'NL_NAME_2': 'NA',
    'TYPE_2': 'Distrito',
    'VARNAME_2': 'NA',
    'mean': 0}}]}

Note:我不得不限制日期范围(即startDate)，因为它是...大量数据后，我收到以下错误/警告消息:

IOPub data rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.