mht2953658596
commented
2 weeks ago def calculate_spm_by_gee(m: str, shp_path: str, output_dir: str):
roi = ee.FeatureCollection(shapefile.Reader(shp_path).__geo_interface__)
def rmCloud(image):
cloudShadowBitMask = (1 << 4)
cloudBitMask = (1 << 3)
qa = image.select("QA_PIXEL")
mask = (qa.bitwiseAnd(cloudShadowBitMask).eq(0)
.And(qa.bitwiseAnd(cloudBitMask).eq(0)))
return image.updateMask(mask)
def apply_scale_factors57(image):
optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2)
thermal_bands = image.select('ST_B6').multiply(0.00341802).add(149.0)
return image.addBands(optical_bands, None, True).addBands(thermal_bands, None, True)
def apply_scale_factors89(image):
optical_bands = image.select('SR_B.').multiply(0.0000275).add(-0.2)
thermal_bands = image.select('ST_B.*').multiply(0.00341802).add(149.0)
return image.addBands(optical_bands, None, True).addBands(thermal_bands, None, True)
def addNDWI57(image):
ndwi = image.normalizedDifference(['SR_B2', 'SR_B4']).rename('addNDWI57')
return ndwi
def addNDWI89(image):
ndwi = image.normalizedDifference(['SR_B3', 'SR_B5']).rename('addNDWI89')
return ndwi
if int(m) < 2012:
l5 = (ee.ImageCollection("LANDSAT/LT05/C02/T1_L2")
.filterDate(f"{m}-01-01", f"{m}-12-31")
.filterBounds(roi)
.filter(ee.Filter.lte('CLOUD_COVER', 30))
.map(rmCloud)
.map(apply_scale_factors57)
.mean()
.select(['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4']))
ndwi = addNDWI57(l5)
water = ndwi.gt(-0.005).updateMask(ndwi.lt(10))
water = water.updateMask(water.gt(0.5))
l5elicloud = ee.ImageCollection("LANDSAT/LT05/C02/T1_L2").map(rmCloud).map(apply_scale_factors57)
mean_ecloud = l5elicloud.mean().clip(roi)
SPM = mean_ecloud.expression(
'0.0430 + 45 * red', {
'red': mean_ecloud.select('SR_B3'),
}).float()
mean_ecloud = mean_ecloud.addBands(SPM.rename("SPM"))
SPM = ee.Image(water).multiply(SPM).toFloat()
elif int(m) == 2012:
l7 = (ee.ImageCollection("LANDSAT/LE07/C02/T1_L2")
.filterDate(f"{m}-01-01", f"{m}-12-31")
.filterBounds(roi)
.filter(ee.Filter.lte('CLOUD_COVER', 30))
.map(rmCloud)
.map(apply_scale_factors57)
.mean()
.select(['SR_B1', 'SR_B2', 'SR_B3', 'SR_B4']))
ndwi = addNDWI57(l7)
water = ndwi.gt(-0.005).updateMask(ndwi.lt(10))
water = water.updateMask(water.gt(0.5))
l7elicloud = ee.ImageCollection("LANDSAT/LE07/C02/T1_L2").map(rmCloud).map(apply_scale_factors57)
mean_ecloud = l7elicloud.mean().clip(roi)
SPM = mean_ecloud.expression(
'0.0430 + 45 * red', {
'red': mean_ecloud.select('SR_B3'),
}).float()
mean_ecloud = mean_ecloud.addBands(SPM.rename("SPM"))
SPM = ee.Image(water).multiply(SPM).toFloat()
elif int(m) > 2012:
l8 = (ee.ImageCollection("LANDSAT/LC08/C02/T1_L2")
.filterDate(f"{m}-01-01", f"{m}-12-31")
.filterBounds(roi)
.filter(ee.Filter.lte('CLOUD_COVER', 30))
.map(rmCloud)
.map(apply_scale_factors89)
.mean()
.select(['SR_B2', 'SR_B3', 'SR_B4', 'SR_B5']))
ndwi = addNDWI89(l8)
water = ndwi.gt(-0.005).updateMask(ndwi.lt(10))
water = water.updateMask(water.gt(0.5))
l8elicloud = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").map(rmCloud).map(apply_scale_factors89)
mean_ecloud = l8elicloud.mean().clip(roi)
SPM = mean_ecloud.expression(
'0.0430 + 45 * red', {
'red': mean_ecloud.select('SR_B4'),
}).float()
mean_ecloud = mean_ecloud.addBands(SPM.rename("SPM"))
SPM = ee.Image(water).multiply(SPM).toFloat()
imagename = "SD_" + m + "_SPM" + ".tif"
filename = os.path.join(output_dir, imagename)
geemap.download_ee_image(SPM, filename, scale=30, crs='EPSG:4326', max_tile_size=4, region=roi.geometry())
dugalh
My colleagues and I have been using the geedim library to download data, and we've encountered some issues that didn't exist before. It seems like the library has recently faced some restrictions:
Slow Download Speed: The data download process has become extremely slow, which wasn't an issue previously. Frequent Retry Errors: We are now seeing messages like "Tile downloaded failed, retry 3 of 5" very frequently. This is a new problem that didn't occur before. It appears that the library might be facing some recent limitations. Has anything changed recently that could be causing these issues? Are there any solutions or workarounds available?
Thank you for your assistance!