Google Earth Engine——WHBU/NBAR_1YEAR/2YEAR/3YEAR：MODIS 8天500米BRDF-Albedo数据集，高清卫星影像作为生成地面生物量/碳地图的基础

The mosaics are created by using the MODIS 8-Day 500 meter BRDF-Albedo Quality product. Data Quality flags are used to select the best observations from the MODIS 8-day 500 meter Nadir BRDF-Adjusted Reflectance imagery product. This specific mosaic picks observations from the three highest quality categories over a 1-year period of MODIS data.

The MODIS NBAR annual mosaics have been used by government agencies and NGOs in tropical countries as input to generate aboveground biomass/carbon maps. The Woods Hole Research Center has been using the dataset for country capacity building and technical training workshops in tropical countries.

镶嵌影像是通过使用MODIS 8天500米BRDF-Albedo质量产品创建的。数据质量标志用于从MODIS 8天500米Nadir BRDF-Adjusted Reflectance图像产品中挑选最佳观测数据。这个特定的马赛克从MODIS数据的1年内的三个最高质量类别中挑选观测数据。

热带国家的政府机构和非政府组织已经使用MODIS NBAR年度马赛克，作为生成地面生物量/碳地图的输入。伍兹霍尔研究中心一直在热带国家使用该数据集进行国家能力建设和技术培训研讨会。

Dataset Availability

2000-02-18T00:00:00Z - 2017-03-14T00:00:00

Dataset Provider

Baccini, A. (Woods Hole Research Center), Sulla-Menashe, D. (Boston University)

Earth Engine Snippet

ee.ImageCollection("WHBU/NBAR_1YEAR")

Bands

引用：

Citations:

• Baccini, A., Laporte, N., Goetz, S. J., Sun, M., & Dong, H. (2008). A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environmental Research Letters, 3(4), 045011. doi:10.1088/1748-9326/3/4/045011
• A first map of tropical Africa's above-ground biomass derived from satellite imagery - IOPscience

版本：

MODIS 1-year Nadir BRDF-Adjusted Reflectance (NBAR) Mosaic

代码：

var dataset = ee.ImageCollection("WHBU/NBAR_1YEAR");

Map.addLayer(dataset.mean(), {min:0, max:7000});

版本：

MODIS 2-year Nadir BRDF-Adjusted Reflectance (NBAR) Mosaic

代码：

var dataset = ee.ImageCollection("WHBU/NBAR_2YEAR");

Map.addLayer(dataset.mean(), {min:0, max:7000});

版本：

MODIS 3-year Nadir BRDF-Adjusted Reflectance (NBAR) Mosaic

代码：

var dataset = ee.ImageCollection("WHBU/NBAR_3YEAR");

Map.addLayer(dataset.mean(), {min:0, max:7000});