CARVE: Daily Thaw State of Boreal and Arctic Alaska from AMSR-E and SSM/I, 2003-2014
简介
该数据集提供阿拉斯加和北极北方陆地表面状态的每日 10 公里分辨率地图，包括冻结、融化和解冻状态。这些数据来自 2003 年至 2014 年先进微波扫描辐射计（AMSR-E）和特殊传感器微波成像仪（SSM/I）的被动微波辐射计观测数据。数据产品与北极水库碳脆弱性实验（CARVE）期间进行的科学数据收集工作重叠。
数据产品是使用时间序列奇异性分类器生成的，该分类器可检测时间序列数据中与土壤或积雪冻融期间发生的地表水文剧烈变化相关的不连续变化或边缘。分类器使用连续小波变换和多尺度分析，对来自频率通道组合的光谱梯度亮度温度进行分析，以识别和区分融雪时间、无雪季节长度和地表再冻结。融雪是通过 K a 波段观测亮度温度的昼夜变化确定的。冻融是通过 K 和 K a 波段观测值之间的梯度以及 C 和 X 波段观测值（如有）来确定的。
该数据集包含 15 个年度每日陆地表面状态的 NetCDF 文件 (.nc) 和 15 个相应地图图像 (.png) 的配套文件 (*.zip)。
摘要
Table 1. Variables in the AMSR-E data files
Variable name Units Description
time days Days since 1970-01-01
freeze_melt_thaw 0: snow/frozen; 1: snowmelt; 2: snow-free/thawed Combined diurnal difference snowmelt and freeze/thaw state from spectral slope 36.5V-18.7V GHz
freeze_thaw_hf 0: frozen, 1: thawed Freeze/thaw state from spectral slope 36.5V-18.7V GHz (X- and C-band algorithm)
freeze_thaw_lf 0: frozen, 1: thawed Freeze/thaw state from spectral slope 10.7V-6.9V GHz (Ka- and K-band algorithm)
lat degrees north latitude coordinate
lon degrees east longitude coordinate
snow_melt 0: dry snow/thawed land, 1: snowmelt Surface state, diurnal difference classifier
Table 2. Variables in the SSM/I data files
Variable name Units Description
time days Days since 1970-01-01
freeze_melt_thaw 0: snow/frozen; 1: snowmelt; 2: snow-free/thawed Combined diurnal difference snowmelt and freeze/thaw state from spectral slope 37.0V-19.35V GHz
freeze_thaw 0: frozen, 1: thawed Freeze/thaw state from spectral slope 37.0V-19.35V GHz (X- and C-band algorithm)
lat degrees north latitude coordinate
lon degrees east longitude coordinate
snow_melt 0: dry snow/thawed land, 1: snowmelt Surface state, diurnal difference classifier
代码
!pip install leafmap
!pip install pandas
!pip install folium
!pip install matplotlib
!pip install mapclassify
import pandas as pd
import leafmap
url = "https://github.com/opengeos/NASA-Earth-Data"
df = pd.read_csv(url, sep="\t")
df
leafmap.nasa_data_login()
results, gdf = leafmap.nasa_data_search(
short_name="CARVE_Land_Thaw_State_1383",
cloud_hosted=True,
bounding_box=(-168.11, 58.84, -131.75, 71.56),
temporal=("2013-08-12", "2015-11-13"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
leafmap.nasa_data_download(results[:5], out_dir="data")
