GEE使用Sentinel-1数据进行土壤湿度反演代码
时间: 2024-05-23 22:13:58 浏览: 12
以下是使用Sentinel-1数据进行土壤湿度反演的代码:
```python
import ee
ee.Authenticate()
ee.Initialize()
# 定义AOI
aoi = ee.Geometry.Polygon(
[[[-114.05, 37.0],
[-114.05, 36.9],
[-113.90, 36.9],
[-113.90, 37.0]]])
# 定义时间范围
start_date = ee.Date('2020-01-01')
end_date = ee.Date('2020-12-31')
# 加载Sentinel-1数据
collection = ee.ImageCollection('COPERNICUS/S1_GRD') \
.filterBounds(aoi) \
.filterDate(start_date, end_date) \
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV')) \
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH')) \
.filter(ee.Filter.eq('instrumentMode', 'IW'))
# 计算VV/VH比值
def get_vv_vh_ratio(image):
vv = image.select('VV').multiply(100)
vh = image.select('VH').multiply(100)
ratio = vv.divide(vh)
return ratio.rename('VVVH')
collection_ratio = collection.map(get_vv_vh_ratio)
# 加载土壤湿度参考数据
reference_data = ee.Image('OpenLandMap/SOL/SOL_TEXTURE-CLASS_USDA-TT_M/v02') \
.select('b0')
# 合并Sentinel-1和土壤湿度参考数据
def join_image(image):
date = image.date().format('YYYY-MM-dd')
reference = reference_data \
.rename('reference') \
.set('system:time_start', ee.Date(date).millis())
joined = image.addBands(reference)
return joined
collection_joined = collection_ratio.map(join_image)
# 训练土壤湿度模型
dependent = 'reference'
independents = ['VVVH']
training_data = collection_joined.select([dependent] + independents) \
.sample(region = aoi, scale = 30, numPixels = 5000)
trained = ee.Classifier.smileRandomForest(10) \
.train(training_data, dependent, independents)
# 应用土壤湿度模型
def apply_soil_moisture_model(image):
date = image.date().format('YYYY-MM-dd')
input_data = image.select(independents) \
.rename(independents)
output = image.select(dependent) \
.rename('soil_moisture') \
.set('system:time_start', ee.Date(date).millis())
soil_moisture = input_data.classify(trained) \
.select('classification') \
.multiply(10.0) \
.add(0.0) \
.rename('soil_moisture')
return output.addBands(soil_moisture)
collection_soil_moisture = collection_joined.map(apply_soil_moisture_model)
# 显示土壤湿度结果
soil_moisture_vis = {'min': 0, 'max': 500}
image = collection_soil_moisture \
.select('soil_moisture') \
.filterDate(start_date, end_date) \
.mosaic()
Map = geemap.Map(center=[37.0, -114.0], zoom=9)
Map.addLayer(image, soil_moisture_vis, 'Soil Moisture')
Map.addLayer(aoi, {}, 'AOI')
Map.addLayerControl()
Map```
在这个代码中,我们首先定义了一个感兴趣区域(AOI),然后加载了Sentinel-1数据。我们选择了VV和VH极化,以及IW仪器模式,并过滤了数据集。接下来,我们计算了VV/VH比值,并将其与土壤湿度参考数据合并。然后,我们训练了一个随机森林模型,并将其应用于Sentinel-1数据,得到土壤湿度结果。最后,我们将结果可视化在地图上。