Google Earth Engine ——Terra MODIS植被覆盖度(VCF)产品是全球地表植被估计的亚像素级250m分辨率产品

简介: Google Earth Engine ——Terra MODIS植被覆盖度(VCF)产品是全球地表植被估计的亚像素级250m分辨率产品

The Terra MODIS Vegetation Continuous Fields (VCF) product is a sub-pixel-level representation of surface vegetation cover estimates globally. Designed to continuously represent Earth's terrestrial surface as a proportion of basic vegetation traits, it provides a gradation of three surface cover components: percent tree cover, percent non-tree cover, and percent bare. VCF products provide a continuous, quantitative portrayal of land surface cover with improved spatial detail, and hence, are widely used in environmental modeling and monitoring applications.

Generated yearly, the VCF product is produced using monthly composites of Terra MODIS 250 and 500 meters Land Surface Reflectance data, including all seven bands, and Land Surface Temperature.

Documentation:


Terra MODIS植被连续场(VCF)产品是全球地表植被估计的亚像素级表示。它被设计成连续代表地球陆地表面基本植被特征的比例,它提供了三种表面覆盖成分的梯度:树木覆盖百分比、非树木覆盖百分比和裸露百分比。VCF产品提供了一个连续的、定量的土地表面覆盖的描述,具有更好的空间细节,因此,被广泛用于环境建模和监测应用。

VCF产品每年生成一次,使用Terra MODIS 250米和500米地表反射率数据(包括所有七个波段)和地表温度的月度合成物。

Dataset Availability

2000-03-05T00:00:00 - 2020-03-05T00:00:00

Dataset Provider

NASA LP DAAC at the USGS EROS Center

Collection Snippet

ee.ImageCollection("MODIS/006/MOD44B")

Resolution

250 meters

Bands Table

Name Description Min Max Units Scale
Percent_Tree_Cover Percent of a pixel which is covered by tree canopy 0 100 % 0
Percent_NonTree_Vegetation Percent of a pixel which is covered by non-tree vegetation 0 100 % 0
Percent_NonVegetated Percent of a pixel which is not vegetated 0 100 % 0
Quality Describes those inputs that had poor quality (cloudy, high aerosol, cloud shadow, or view zenith >45°). Each bit in the field represents 1 out of 8 input surface reflectance files to the model. 0
Quality Bitmask
  • Bit 0: State of input layers DOY 065-097
    • 0: Clear
    • 1: Bad
  • Bit 1: State of input layers DOY 113-145
    • 0: Clear
    • 1: Bad
  • Bit 2: State of input layers DOY 161-193
    • 0: Clear
    • 1: Bad
  • Bit 3: State of input layers DOY 209-241
    • 0: Clear
    • 1: Bad
  • Bit 4: State of input layers DOY 257-289
    • 0: Clear
    • 1: Bad
  • Bit 5: State of input layers DOY 305-337
    • 0: Clear
    • 1: Bad
  • Bit 6: State of input layers DOY 353-017
    • 0: Clear
    • 1: Bad
  • Bit 7: State of input layers DOY 033-045
    • 0: Clear
    • 1: Bad
Percent_Tree_Cover_SD Standard deviation (SD) of the 30 models that were used to generate the pixel value in the percent tree cover data layer 0 32767 % 0.01
Percent_NonVegetated_SD Standard deviation (SD) of the 30 models that were used to generate the pixel value in the percent non-vegetated data layer 0 32767 % 0.01
Cloud Clarifies the 'Quality' layer to give the user an indication that the 'bad' data refers to cloudy input data. Each bit in the field represents 1 out of 8 input surface reflectance files to the model. 0
Cloud Bitmask
  • Bit 0: State of input layers DOY 065-097
    • 0: Clear
    • 1: Cloudy
  • Bit 1: State of input layers DOY 113-145
    • 0: Clear
    • 1: Cloudy
  • Bit 2: State of input layers DOY 161-193
    • 0: Clear
    • 1: Cloudy
  • Bit 3: State of input layers DOY 209-241
    • 0: Clear
    • 1: Cloudy
  • Bit 4: State of input layers DOY 257-289
    • 0: Clear
    • 1: Cloudy
  • Bit 5: State of input layers DOY 305-337
    • 0: Clear
    • 1: Cloudy
  • Bit 6: State of input layers DOY 353-017
    • 0: Clear
    • 1: Cloudy
  • Bit 7: State of input layers DOY 033-045
    • 0: Clear
    • 1: Cloudy


使用说明:

MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.

数据引用:

Please visit LP DAAC 'Citing Our Data' page for information on citing LP DAAC datasets.

代码:

var dataset = ee.ImageCollection('MODIS/006/MOD44B');
var visualization = {
  bands: ['Percent_Tree_Cover'],
  min: 0.0,
  max: 100.0,
  palette: ['bbe029', '0a9501', '074b03']
};
Map.centerObject(dataset);
Map.addLayer(dataset, visualization, 'Percent Tree Cover');


相关文章
|
2月前
|
数据可视化 定位技术 Sentinel
如何用Google Earth Engine快速、大量下载遥感影像数据?
【2月更文挑战第9天】本文介绍在谷歌地球引擎(Google Earth Engine,GEE)中,批量下载指定时间范围、空间范围的遥感影像数据(包括Landsat、Sentinel等)的方法~
486 0
如何用Google Earth Engine快速、大量下载遥感影像数据?
|
2月前
|
编解码 人工智能 算法
Google Earth Engine——促进森林温室气体报告的全球时间序列数据集
Google Earth Engine——促进森林温室气体报告的全球时间序列数据集
24 0
|
2月前
|
编解码 人工智能 数据库
Google Earth Engine(GEE)——全球道路盘查项目全球道路数据库
Google Earth Engine(GEE)——全球道路盘查项目全球道路数据库
43 0
|
2月前
|
机器学习/深度学习 算法 数据可视化
基于Google Earth Engine云平台构建的多源遥感数据森林地上生物量AGB估算模型含生物量模型应用APP
基于Google Earth Engine云平台构建的多源遥感数据森林地上生物量AGB估算模型含生物量模型应用APP
103 0
|
2月前
|
数据处理
Google Earth Engine(GEE)——sentinel-1数据处理过程中出现错误Dictionary does not contain key: bucketMeans
Google Earth Engine(GEE)——sentinel-1数据处理过程中出现错误Dictionary does not contain key: bucketMeans
29 0
|
2月前
|
数据采集 编解码 人工智能
Google Earth Engine(GEE)——全球每日近地表空气温度(2003-2020年)
Google Earth Engine(GEE)——全球每日近地表空气温度(2003-2020年)
65 0
|
2月前
|
人工智能
Google Earth Engine(GEE)——1984-2019年美国所有土地上的大火烧伤严重程度和范围数据集
Google Earth Engine(GEE)——1984-2019年美国所有土地上的大火烧伤严重程度和范围数据集
15 0
|
2月前
Google Earth Engine(GEE)——当你无法进行两个图像相减的时候发生错误lst2020.subtract is not a function
Google Earth Engine(GEE)——当你无法进行两个图像相减的时候发生错误lst2020.subtract is not a function
23 0
|
2月前
|
编解码 人工智能 计算机视觉
Google Earth Engine(GEE)——Umbra卫星翁布拉合成孔径雷达公开数据
Google Earth Engine(GEE)——Umbra卫星翁布拉合成孔径雷达公开数据
20 0
|
2月前
|
人工智能
Google Earth Engine(GEE)——全球1公里的云量MODIS图像数据集
Google Earth Engine(GEE)——全球1公里的云量MODIS图像数据集
36 0
Google Earth Engine(GEE)——全球1公里的云量MODIS图像数据集

热门文章

最新文章