Google Earth Engine ——MOD13Q1.006 Terra Vegetation Indices 16-Day Global 250m归一化植被指数（NDVI）和增强植被指数EVI

The MOD13Q1 V6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI also uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin cloud clouds. The MODIS NDVI and EVI products are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols, and cloud shadows.

Documentation:

• ​​User's Guide​​
• ​​Algorithm Theoretical Basis Document (ATBD)​​
• ​​General Documentation​​

MOD13Q1 V6产品在每个像素的基础上提供植被指数（VI）值。有两个主要的植被层。第一个是归一化植被指数（NDVI），它被称为现有的国家海洋和大气管理局-高级甚高分辨率辐射计（NOAA-AVHRR）得出的NDVI的连续性指数。第二个植被层是增强植被指数（EVI），它将树冠背景变化降到最低，并保持对茂密植被条件的敏感性。EVI还使用蓝色波段来消除由烟雾和亚像素薄云造成的残留大气污染。MODIS NDVI和EVI产品是由经过大气校正的双向表面反射率计算出来的，这些反射率已经屏蔽了水、云、重气溶胶和云影。

Dataset Availability

2000-02-18T00:00:00 - 2021-08-29T00:00:00

Dataset Provider

​​NASA LP DAAC at the USGS EROS Center​​

Collection Snippet

​​ee.ImageCollection("MODIS/006/MOD13Q1")​​

Resolution

250 meters

Bands Table

Name	Description	Min	Max	Units	Wavelength	Scale
NDVI	Normalized Difference Vegetation Index	-2000	10000			0.0001
EVI	Enhanced Vegetation Index	-2000	10000			0.0001
DetailedQA	VI quality indicators					0
DetailedQA Bitmask	Bits 0-1: VI quality (MODLAND QA Bits) 0: VI produced with good quality 1: VI produced, but check other QA 2: Pixel produced, but most probably cloudy 3: Pixel not produced due to other reasons than clouds Bits 2-5: VI usefulness 0: Highest quality 1: Lower quality 2: Decreasing quality 4: Decreasing quality 8: Decreasing quality 9: Decreasing quality 10: Decreasing quality 12: Lowest quality 13: Quality so low that it is not useful 14: L1B data faulty 15: Not useful for any other reason/not processed Bits 6-7: Aerosol Quantity 0: Climatology 1: Low 2: Intermediate 3: High Bit 8: Adjacent cloud detected 0: No 1: Yes Bit 9: Atmosphere BRDF correction 0: No 1: Yes Bit 10: Mixed Clouds 0: No 1: Yes Bits 11-13: Land/water mask 0: Shallow ocean 1: Land (nothing else but land) 2: Ocean coastlines and lake shorelines 3: Shallow inland water 4: Ephemeral water 5: Deep inland water 6: Moderate or continental ocean 7: Deep ocean Bit 14: Possible snow/ice 0: No 1: Yes Bit 15: Possible shadow 0: No 1: Yes
sur_refl_b01	Red surface reflectance	0	10000		645nm	0.0001
sur_refl_b02	NIR surface reflectance	0	10000		858nm	0.0001
sur_refl_b03	Blue surface reflectance	0	10000		469nm	0.0001
sur_refl_b07	MIR surface reflectance	0	10000		2130nm/2105 - 2155nm	0.0001
ViewZenith	View zenith angle	0	18000	Degrees		0.01
SolarZenith	Solar zenith angle	0	18000	Degrees		0.01
RelativeAzimuth	Relative azimuth angle	-18000	18000	Degrees		0.01
DayOfYear	Julian day of year	1	366			0
SummaryQA	Quality reliability of VI pixel					0
SummaryQA Bitmask	Bits 0-1: VI quality (MODLAND QA Bits) 0: Good data, use with confidence 1: Marginal data, useful but look at detailed QA for more information 2: Pixel covered with snow/ice 3: Pixel is 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：

​​LP DAAC - MOD13Q1https://doi.org/10.5067/MODIS/MOD13Q1.006​​

代码：

var dataset = ee.ImageCollection('MODIS/006/MOD13Q1')
                  .filter(ee.Filter.date('2018-01-01', '2018-05-01'));
var ndvi = dataset.select('NDVI');
var ndviVis = {
  min: 0.0,
  max: 8000.0,
  palette: [
    'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
    '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
    '012E01', '011D01', '011301'
  ],
};
Map.setCenter(6.746, 46.529, 2);
Map.addLayer(ndvi, ndviVis, 'NDVI');