ABoVE: Angular-corrected MODIS MAIAC Reflectance across Alaska and Canada, 2000-2017
简介
该数据集提供了 2000 年至 2017 年阿拉斯加和加拿大西部 ABoVE 区域 MODIS 多角度大气校正算法 (MAIAC) 表面反射率的角度校正。使用机器学习方法随机森林 (RF)，将原始 MAIAC 反射率数据校正为一致的视角和照明角度（0 度视角天顶角和 45 度太阳天顶角），以减少由于角度效应造成的伪影和变化。保留了原始 MAIAC 数据的亚日时间分辨率和 1 公里空间分辨率，包括 7 个陆地波段（波段 1-7）和 5 个海洋波段（波段 8-12）。由此产生的表面反射率数据适用于对表面现象的模式、过程和动态进行长期研究。数据涵盖 11 个不同的 Terra 和 Aqua 卫星 MODIS MAIAC 图块。
摘要
Bits Definition
0–2 Cloud Mask
000 = Undefined; 001 = Clear; 010 = Possibly Cloudy (detected by AOT filter);
011 = Cloudy (detected by cloud mask algorithm); 101 = Cloud Shadow;
110 = Hot spot of fire; 111 = Water Sediments
3–4 Land Water Snow/Ice Mask
00 = Land; 01 = Water; 10 = Snow; 11 = Ice
5–7 Adjacency Mask
000 = Normal condition; 001 = Adjacent to cloud;
010 = Surrounded by more than 8 cloudy pixels; 011 = Single cloudy pixel;
100 = Adjacent to snow; 101 = Snow was previously detected on this pixel
8 Aerosol Optical Thickness (AOT) Level
0 = AOT is low (<=0.6); 1 = AOT is high (> 0.6) or undefined
9 Algorithm Initialize Status
0 = Initialized; 1 = Not initialized
10 BRF Retrieved Over Snow (use AOT=0.05)
0 = no; 1 = yes
11 Altitude >3.5km (BRF is retrieved with AOT=0.01)
0 = no; 1 = yes
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="ABoVE_MODIS_MAIAC_Reflectance_1858",
cloud_hosted=True,
bounding_box=(-180.0, 44.12, 180.0, 80.81),
temporal=("2000-02-24", "2017-12-31"),
count=-1, # use -1 to return all datasets
return_gdf=True,
)
gdf.explore()
