2016-2017 年 ABoVE 区域土壤呼吸图

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此星光明 发表于 2025/07/24 23:44:10 2025/07/24
【摘要】 ​Soil Respiration Maps for the ABoVE Domain, 2016-2017简介该数据集以 300 米空间分辨率，提供了 2016 年 8 月 18 日至 2018 年 9 月 12 日期间阿拉斯加和加拿大西北部受冻土影响的苔原和北方针叶林生态系统土壤呼吸作用产生的二氧化碳（CO2）排放的网格化估算值。估算值包括：月平均二氧化碳通量（gCO2 C m-2 d-...

​Soil Respiration Maps for the ABoVE Domain, 2016-2017

简介

该数据集以 300 米空间分辨率，提供了 2016 年 8 月 18 日至 2018 年 9 月 12 日期间阿拉斯加和加拿大西北部受冻土影响的苔原和北方针叶林生态系统土壤呼吸作用产生的二氧化碳（CO2）排放的网格化估算值。估算值包括：月平均二氧化碳通量（gCO2 C m-2 d-1）、各季节（秋季、冬季、春季、夏季）的日平均二氧化碳通量及误差估算值、年度二氧化碳吸收补偿量（即植被总初级生产力 GPP）、植被总初级生产力（GPP；gCO2 C m-2 yr-1）的年度预算，以及每个 300 米网格单元内开放（非植被）水体的比例。估算值中也包括地下呼吸源（即根系和微生物）。网格化土壤二氧化碳估算值是利用季节性随机森林模型、遥感信息以及来自土壤呼吸站和涡流协方差塔的新汇编的原位土壤二氧化碳通量数据获得的。通量塔数据与每个土壤呼吸站强制扩散 (FD) 室记录的每日间隙填充通量观测数据一起提供。数据涵盖 NASA ABoVE 域。

摘要

File Name Units Description
soil_resp_flux_YYYY-XX.tif g m-2 d-1 Twelve files. Daily average emissions from soil respiration (gCO2 C m-2 d-1) over monthly time steps where YYYY is 2016 or 2017 and XX for the 2016 files is 09–12, and for the 2017 files is 01–08. Missing values are represented by 0.
soil_resp_flux_AAA_YYYY.tif g m-2 d-1 Four files. Average estimates (gCO2 C m-2 d-1) for sampling periods, where AAA is the season and YYYY is the year. Missing values are represented by 0.
soil_resp_flux_est_error_AAA_YYYY.tif g m-2 d-1 Four files. Average flux estimate error (gCO2 C m-2 d-1) for sampling periods, where AAA is the season and YYYY is the year. Missing values are represented by -3.
soil_resp_budget_annual_2016-2017.tif g m-2 Annual soil respiration budgets (gCO2 C m-2) for September 2016–August 2017. Missing values are represented by 0.
soil_resp_budget_annual_percent_offset_GPP_2016-2017.tif percent Annual percentage offset of growing season GPP by soil respiration emissions for September 2016–August 2017. An offset of 100% indicates that soil respiration equaled or exceeded annual GPP. Missing values are represented by 0.
GPP_budget_annual_2016-2017.tif g m-2 Annual GPP budgets (gCO2 C m-2) averaged for the 2016–2017 growing seasons. A composite of SMAP, GOSIF, and MODIS GPP products. Missing values are represented by 0.
ecosystem_resp_budget_annual_percent_offset_GPP_2016-2017.tif percent Annual percentage offset of growing season GPP by ecosystem (soil + aboveground) respiration forSeptember 2016–August 2017. An offset of 100% indicates that ecosystem respiration equaled or exceeded annual GPP. Missing values are represented by 0.
fractional_water.tif 1 Provides 300 m grid fractional water (FW) information for the study domain, derived from Wang et al. (2019). FW values of 1 indicate that the 300 m pixel contains 100% surface open water coverage. Missing values are represented by 0.
soil_resp_station_descriptions.csv Provides descriptive data of site respiration stations. See Table 2 for variable descriptions. Missing values are represented by -9999.
site_monthly_soil_respiration_flux.csv Table of average monthly soil respiration fluxes for the ABoVE domain in gCO2 C m-2 d-1, used to develop the Random Forest models. These include flux data from Soil Respiration Stations and eddy covariance sites for 2016–2017 (Watts et al., 2021). The Soil Respiration Station record also includes more recent data for 2018. See Table 3 for variable descriptions. Missing values are represented by -9999.
soil_resp_station_daily_soil_respiration_flux_gap_filled_2016-2018.csv Table of daily gap filled flux observations for each Alaska Soil Respiration station forced diffusion (FD) chamber record (Watts et al., 2021). The record includes all available data from 2016–2018. See Table 4 for variable descriptions. Missing values are represented by -9999.



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_Soil_Respiration_Maps_1935",
    cloud_hosted=True,
    bounding_box=(-169.51, 55.81, -98.74, 76.69),
    temporal=("2016-08-18", "2018-09-12"),
    count=-1,  # use -1 to return all datasets
    return_gdf=True,
)
 
 
gdf.explore()
 
#leafmap.nasa_data_download(results[:5], out_dir="data")
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Python 大数据 数据挖掘 机器学习 遥感
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