SoilGrid 250米土壤质地数据集下载

Gee中的数字和矩阵混合运算方法print(Math.cos(40 * Math.PI/180)) // Client-sideprint(ee.Number(40 * Math.PI/180).cos()) // Server-side (For ee.Number and ee.Image)https://gis.stackexchange.com/questions/359702/calculate-sine-and-cosine-in-google-earth-engine...

gee选择一年中不同时间的数据//filter collection by range, day of year, and boundsvar collection = ee.ImageCollection('COPERNICUS/S2') .filterDate('2015-05-01', '2017-05-31') .filter(ee.Filter.dayOfYear(121, 151)) .filterBounds(GP);

Compaq Visual FortranV 6.7 正式版https://www.greenxf.com/soft/214445.htmlCompaq Visual Fortran(Fortran语言编译工具)是一款专业实用流畅度高的Fortran语言编译软件。不知道怎么进行Fortran语言编译？那就快试试绿色先锋小编推荐的Compaq Visual Fortran正式版下载使用。他可以对矩阵和复数进行运算，而且接近数学公式的自然描述，此版本是Compaq Visual Fortran破解版，内附注

下载哨兵2号数据（Export Google Earth Engine RGB Sentinel-2 Imagery to Google Drive using Python API）# This is the cloud masking function provided by GEE but adapted for use in Python.def maskS2clouds(image): qa = image.select('QA60') # Bits 10 and 11 are

gee去云处理Landsat、Sentinel和Modis影像1.Landsat 和 MODIS影像的去云函数function cloudfree_mod09a1(image){ var qa = image.select('StateQA') var cloudState = bitwiseExtract(qa, 0, 1) var cloudShadowState = bitwiseExtract(qa, 2) var cirrusState = bitwiseExtract(qa

Gee获取影像每个像元的经纬度，以及把他们处理成为Numpy矩阵变量#获取影像的经纬度def getImgData(image): image = image.addBands(ee.Image.pixelLonLat()) roi = image.geometry() data = image.reduceRegion( reducer = ee.Reducer.toList(), geometry = roi, scale = 10, ma

Calculate NDVI from Sentinel-2 with Python API# get indexesdef getNDVI(image): # Normalized difference vegetation index (NDVI) ndvi = image.normalizedDifference(['nir','red']).rename("ndvi") image = image.addBands(ndvi) return(image

gee 把数据下载到本地#方法一from google.colab import filesdf.to_csv('filename.csv') files.download('filename.csv')#方法二#!pip install -U -q PyDrivefrom pydrive.auth import GoogleAuthfrom pydrive.drive import GoogleDrivefrom google.colab import authfrom google

gee导出DataFrame为csv# Import Drive API and authenticate.from google.colab import drive# Mount your Drive to the Colab VM.drive.mount('/gdrive')# Write the DataFrame to CSV file.with open('/gdrive/My Drive/foo.csv', 'w') as f: df.to_csv(f)https:/

gee 获取时间序列import eeee.Authenticate()ee.Initialize()collection = ee.ImageCollection('NASA/FLDAS/NOAH01/C/GL/M/V001')taken = collection.filterDate('2019-01-01', '2019-12-31').sort('system:time_start', False)poi = ee.Geometry.Point([112.621391, -7.983

gee 获取固定行列的数据var L5fs1998post = ee.ImageCollection('LANDSAT/LT5_SR') //Landsat 5 Surface reflectance.filterDate('1998-1-9', '1998-2-27') //filter to date bounds .filter(ee.Filter.eq('wrs_path', 91))//filter to path and row.filter(ee.Filter.eq('wrs_row'

gee 日期#GEE中的日期类#定义方式一date = ee.Date("2019-7-1")print(date.getInfo())#格式化输出print(date.format("yyymmdd").getInfo())#定义方式二date = ee.Date.fromYMD(2019,7,1)print(date.format("yyymmdd").getInfo())#定义方式三date = ee.Date.parse(""yyyymmdd,"20190701")pri

gee system time 转换为 date// Random dummy Imagevar image = ee.Image("COPERNICUS/S2_SR/20200401T001611_20200401T001605_T01WCR");// system:time_start is saved as miliseconds (epoch):var epoch = image.get("system:time_start")print("Epoch system:time_start

Google Earth Engine（区域统计）提示：在通过GEE计算遥感的地表参量以后，我们就需要对计算出来的数据进行统计。GEE上面画图的功能并不是很齐全，得到的效果也不尽如人意。因此我们就需要将GEE对区域的统计量进行导出，导入到本地以后，再进行绘图。本期的的主要内容就是利用GEE对区域内的地表参量（如植被覆盖度、叶面积指数、蒸散量，本次以NDVI为例）进行统计（如平均值、面积等）。最后把统计的值导出到本地。var table = ee.FeatureCollection("users/b

Google earth engine重采样代码.代码.# An highlighted blockimport eeee.Initialize()#获取landsat数据image_30m = ee.Image('LANDSAT/LC08/C01/T1/LC08_044034_20170614')#获取其中一个波段band2 = image_30m.select('B2')proj = band2.projection().getInfo()crs = proj['crs'#重采样为