使用Landsat8时态数据将Google Earth引擎脚本转换为Python

Question

我正在为我的工作做一项研究，我需要用python修改我的google earth引擎脚本，但我遇到了一些问题。他们能帮我吗？

这是一个google earth引擎脚本：

import ee
ee.Initialize()
 //Importing image and geometry:
var l8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
//    geometry = /* color: #d63000 */ee.Geometry.Polygon(
//        [[[-80.92489760146748, 25.433457120928352],
//          [-80.64474623427998, 25.488013471687964],
//          [-80.57882826552998, 25.710940372707608],
//          [-81.02377455459248, 25.770317250349557],
//          [-80.95236342177998, 25.552457242621447]]]);

//Filtering date, polygon, and cloudiness
    var image = l8.filterDate ('2010-09-01', '2021-12-31')
                .filterBounds (geometry)
                .filterMetadata ('CLOUD_COVER', 'less_than', 1);

    //NDVI calculation:
    var ndvi_func = function (i) {
      var ndvi = i.normalizedDifference (['B5', 'B4']).rename ('NDVI')
      return i.addBands(ndvi);
    }

    var image_ndvi = image.map(ndvi_func);

    //Calculating year wise NDVI
    var year = ee.List.sequence(2010,2021);
    var year_func = function(y){
      var range = ee.Filter.calendarRange (y, y, 'year');
      return image_ndvi.select('NDVI').filter(range).mean().set ('Year', y)
    };
    var yearwise_ndvi = ee.ImageCollection(year.map(year_func));
    print (yearwise_ndvi);
    Map.addLayer (yearwise_ndvi)

//Creating time-series chart:
var chart = ui.Chart.image.series ({
  imageCollection: image_ndvi.select('NDVI'),
  region: geometry,
  reducer: ee.Reducer.mean(),
  scale: 30})

print(chart);

图：

​

上面的脚本显示了一个地区的NDVI时间序列，我需要用python完成。下面的脚本显示了python中的错误：

l8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
geometry = ee.Geometry.Polygon([[[-80.92489760146748, 25.433457120928352],
                                 [-80.64474623427998, 25.488013471687964],
                                 [-80.57882826552998, 25.710940372707608],
                                 [-81.02377455459248, 25.770317250349557],
                                 [-80.95236342177998, 25.552457242621447]]]);

#Filtering date, polygon, and cloudiness
image = l8.filterDate ('2010-09-01', '2021-12-31')
image  = image.filterBounds (geometry)
image  = image.filterMetadata ('CLOUD_COVER', 'less_than', 1);

#NDVI calculation:
def ndvi_func(i):
    ndvi = i.normalizedDifference (['B5', 'B4']).rename ('NDVI')
    return i.addBands(ndvi)

image_ndvi = ndvi_func(image.map)

#Calculating year wise NDVI
year = ee.List.sequence(2010,2021);
def year_func(y):
    range = ee.Filter.calendarRange (y, y, 'year');
    return image_ndvi.select('NDVI').filter(range).mean().set ('Year', y)

yearwise_ndvi = ee.ImageCollection(year.map(year_func));
    print (yearwise_ndvi);
    Map.addLayer (yearwise_ndvi)

#Creating time-series chart:
chart = ui.Chart.image.series ({imageCollection: image_ndvi.select('NDVI'),
                                region: geometry,
                                reducer: ee.Reducer.mean(),
                                scale: 30})

print(chart)

问题是这些函数不会加载数据。

您是否愿意帮助我解决此问题，并将NDVI数据按日期加载到DataFrame中？

我也希望有每日NDVI而不是每月/每年，但我没有在谷歌地球引擎上得到它。

谢谢!

Answer 1

我强烈推荐使用eemont，它让这一切在python中变得轻而易举。您可以直接在他们的自述文件中找到适合您的用例的示例：

import ee, eemont

f1 = ee.Feature(ee.Geometry.Point([3.984770,48.767221]).buffer(50),{'ID':'A'})
f2 = ee.Feature(ee.Geometry.Point([4.101367,48.748076]).buffer(50),{'ID':'B'})
fc = ee.FeatureCollection([f1,f2])

S2 = (ee.ImageCollection('COPERNICUS/S2_SR')
   .filterBounds(fc)
   .filterDate('2020-01-01','2021-01-01')
   .maskClouds()
   .scale()
   .index(['EVI','NDVI']))

# By Region
ts = S2.getTimeSeriesByRegion(reducer = [ee.Reducer.mean(),ee.Reducer.median()],
                              geometry = fc,
                              bands = ['EVI','NDVI'],
                              scale = 10)

请注意，他们在NDVI计算中使用了LandSat -2，但是可以很容易地将其更改为使用sentinel。

另请注意:您的脚本不是Python。如果你真的需要我们的帮助来调试你当前的脚本，请删除所有的分号，坚持PEP8格式，并包括你的导入。