Google Earth Engine——GOES-16/17 MCMIPC Series ABI Level 2 云层和水分图像产品的分辨率都是2公里数据集

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发布于 2024-02-02 12:20:17

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发布于 2024-02-02 12:20:17

文章被收录于专栏：GEE数据专栏，GEE学习专栏，GEE错误集等专栏GEE数据专栏，GEE学习专栏，GEE错误集等专栏

The Cloud and Moisture Imagery products are all at 2km resolution. Bands 1-6 are reflective. The dimensionless "reflectance factor" quantity is normalized by the solar zenith angle. These bands support the characterization of clouds, vegetation, snow/ice, and aerosols. Bands 7-16 are emissive. The brightness temperature at the Top-Of-Atmosphere (TOA) is measured in Kelvin. These bands support the characterization of the surface, clouds, water vapor, ozone, volcanic ash, and dust based on emissive properties.

README

GOES卫星是由NOAA管理的地球静止气象卫星。

云层和水分图像产品的分辨率都是2公里。1-6波段是反射的。无尺寸的 "反射系数 "数量是以太阳天顶角为标准的。这些波段支持云、植被、雪/冰和气溶胶的特征。频段7-16是发射型的。大气层顶部（TOA）的亮度温度以开尔文测量。这些波段支持根据发射特性对地表、云层、水汽、臭氧、火山灰和灰尘进行定性。

Dataset Availability

2017-07-10T00:00:00 - 2021-09-30T00:00:00

Dataset Provider

NOAA

Collection Snippet

ee.ImageCollection("NOAA/GOES/16/MCMIPC")

Resolution

2000 meters

Bands Table

Name	Description	Min	Max	Units	Wavelength
CMI_C01	Visible - Blue Daytime aerosol over land, coastal water mapping.	0	1.3	Reflectance factor	0.45-0.49µm
DQF_C01	Data quality flags	0	4
CMI_C02	Visible - Red Daytime clouds, fog, insolation, winds	0	1.3	Reflectance factor	0.59-0.69µm
DQF_C02	Data quality flags	0	4
CMI_C03	Near-IR - Veggie Daytime vegetation, burn scar, aerosol over water, winds	0	1.3	Reflectance factor	0.846-0.885µm
DQF_C03	Data quality flags	0	4
CMI_C04	Near-IR - Cirrus Daytime cirrus cloud	0	1.3	Reflectance factor	1.371-1.386µm
DQF_C04	Data quality flags	0	4
CMI_C05	Near-IR - Snow/Ice Daytime cloud-top phase and particle size, snow	0	1.3	Reflectance factor	1.58-1.64µm
DQF_C05	Data quality flags	0	4
CMI_C06	Near IR - Cloud Particle Size Daytime land, cloud properties, particle size, vegetation, snow	0	1.3	Reflectance factor	2.225-2.275µm
DQF_C06	Data quality flags	0	4
CMI_C07	Infrared - Shortwave Window Brightness	197.31	411.86	K	3.80-4.00µm
DQF_C07	Data quality flags	0	4
CMI_C08	Infrared - Upper-level water vapor High-level atmospheric water vapor, winds, rainfall Brightness	138.05	311.06	K	5.77-6.6µm
DQF_C08	Data quality flags	0	4
CMI_C09	Infrared - Mid-level water vapor Mid-level atmospheric water vapor, winds, rainfall Brightness	137.7	311.08	K	6.75-7.15µm
DQF_C09	Data quality flags	0	4
CMI_C10	Infrared - Lower-level water vapor Lower-level water vapor, winds, and sulfur dioxide Brightness	126.91	331.2	K	7.24-7.44µm
DQF_C10	Data quality flags	0	4
CMI_C11	Infrared - Cloud-top phase Total water for stability, cloud phase, dust, sulfur dioxide, rainfall Brightness	127.69	341.3	K	8.3-8.7µm
DQF_C11	Data quality flags	0	4
CMI_C12	Infrared - Ozone Total ozone, turbulence, winds	117.49	311.06	K	9.42-9.8µm
DQF_C12	Data quality flags	0	4
CMI_C13	Infrared - \"Clean\" longwave window Surface and clouds Brightness	89.62	341.27	K	10.1-10.6µm
DQF_C13	Data quality flags	0	4
CMI_C14	Infrared - Longwave window Imagery, sea surface temperature, clouds, rainfall Brightness	96.19	341.28	K	10.8-11.6µm
DQF_C14	Data quality flags	0	4
CMI_C15	Infrared \"Dirty\" longwave Total water, volcanic ash, sea surface temperature Brightness	97.38	341.28	K	11.8-12.8µm
DQF_C15	Data quality flags	0	4
CMI_C16	Infrared - CO_2 longwave Air temperature, cloud heights Brightness	92.7	318.26	K	13.0-13.6µm
DQF_C16	Data quality flags	0	4

Class Table: DQF_C01

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C02

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C03

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C04

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C05

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C06

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C07

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C08

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C09

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C10

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C11

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C12

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C13

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C14

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C15

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

Class Table: DQF_C16

Value	Color Value	Description
0	#ffffff	Good pixels
1	#ff00ff	Conditionally usable pixels
2	#0000ff	Out of range pixels
3	#00ffff	No value pixels
4	#ffff00	Focal plane temperature threshold exceeded

影像属性：

Name	Type	Description
CMI_C01_offset	Double	Offset to add to scaled CMI_C01 values
CMI_C01_scale	Double	Scale to multiply with raw CMI_C01 values
CMI_C02_offset	Double	Offset to add to scaled CMI_C02 values
CMI_C02_scale	Double	Scale to multiply with raw CMI_C02 values
CMI_C03_offset	Double	Offset to add to scaled CMI_C03 values
CMI_C03_scale	Double	Scale to multiply with raw CMI_C03 values
CMI_C04_offset	Double	Offset to add to scaled CMI_C04 values
CMI_C04_scale	Double	Scale to multiply with raw CMI_C04 values
CMI_C05_offset	Double	Offset to add to scaled CMI_C05 values
CMI_C05_scale	Double	Scale to multiply with raw CMI_C05 values
CMI_C06_offset	Double	Offset to add to scaled CMI_C06 values
CMI_C06_scale	Double	Scale to multiply with raw CMI_C06 values
CMI_C07_offset	Double	Offset to add to scaled CMI_C07 values
CMI_C07_scale	Double	Scale to multiply with raw CMI_C07 values
CMI_C08_offset	Double	Offset to add to scaled CMI_C08 values
CMI_C08_scale	Double	Scale to multiply with raw CMI_C08 values
CMI_C09_offset	Double	Offset to add to scaled CMI_C09 values
CMI_C09_scale	Double	Scale to multiply with raw CMI_C09 values
CMI_C10_offset	Double	Offset to add to scaled CMI_C10 values
CMI_C10_scale	Double	Scale to multiply with raw CMI_C10 values
CMI_C11_offset	Double	Offset to add to scaled CMI_C11 values
CMI_C11_scale	Double	Scale to multiply with raw CMI_C11 values
CMI_C12_offset	Double	Offset to add to scaled CMI_C12 values
CMI_C12_scale	Double	Scale to multiply with raw CMI_C12 values
CMI_C13_offset	Double	Offset to add to scaled CMI_C13 values
CMI_C13_scale	Double	Scale to multiply with raw CMI_C13 values
CMI_C14_offset	Double	Offset to add to scaled CMI_C14 values
CMI_C14_scale	Double	Scale to multiply with raw CMI_C14 values
CMI_C15_offset	Double	Offset to add to scaled CMI_C15 values
CMI_C15_scale	Double	Scale to multiply with raw CMI_C15 values
CMI_C16_offset	Double	Offset to add to scaled CMI_C16 values
CMI_C16_scale	Double	Scale to multiply with raw CMI_C16 values

使用说明：

NOAA data, information, and products, regardless of the method of delivery, are not subject to copyright and carry no restrictions on their subsequent use by the public. Once obtained, they may be put to any lawful use.

数据引用：

Bah, Gunshor, Schmit, Generation of GOES-16 True Color Imagery without a Green Band, 2018. doi:10.1029/2018EA000379

Product User Guide (PUG) Volume 5, L2+ Products.

Schmit, T., Griffith, P., et al, (2016), A closer look at the ABI on the GOES-R series, Bull. Amer. Meteor. Soc., 98(4), 681-698. doi:10.1175/BAMS-D-15-00230.1

代码：

// Band aliases.
var BLUE = 'CMI_C01';
var RED = 'CMI_C02';
var VEGGIE = 'CMI_C03';
var GREEN = 'GREEN';
// 16 pairs of CMI and DQF followed by Bah 2018 synthetic green.
// Band numbers in the EE asset, 0-based.
var NUM_BANDS = 33;
// Skipping the interleaved DQF bands.
var BLUE_BAND_INDEX = (1 - 1) * 2;
var RED_BAND_INDEX = (2 - 1) * 2;
var VEGGIE_BAND_INDEX = (3 - 1) * 2;
var GREEN_BAND_INDEX = NUM_BANDS - 1;

// Visualization range for GOES RGB.
var GOES_MIN = 0.0;
var GOES_MAX = 0.7;  // Alternatively 1.0 or 1.3.
var GAMMA = 1.3;

var goesRgbViz = {
  bands: [RED, GREEN, BLUE],
  min: GOES_MIN,
  max: GOES_MAX,
  gamma: GAMMA
};

var applyScaleAndOffset = function(image) {
  image = ee.Image(image);
  var bands = new Array(NUM_BANDS);
  for (var i = 1; i < 17; i++) {
    var bandName = 'CMI_C' + (100 + i + '').slice(-2);
    var offset = ee.Number(image.get(bandName + '_offset'));
    var scale =  ee.Number(image.get(bandName + '_scale'));
    bands[(i-1) * 2] = image.select(bandName).multiply(scale).add(offset);

    var dqfName = 'DQF_C' + (100 + i + '').slice(-2);
    bands[(i-1) * 2 + 1] = image.select(dqfName);
  }

  // Bah, Gunshor, Schmit, Generation of GOES-16 True Color Imagery without a
  // Green Band, 2018. https://doi.org/10.1029/2018EA000379
  // Green = 0.45 * Red + 0.10 * NIR + 0.45 * Blue
  var green1 = bands[RED_BAND_INDEX].multiply(0.45);
  var green2 = bands[VEGGIE_BAND_INDEX].multiply(0.10);
  var green3 = bands[BLUE_BAND_INDEX].multiply(0.45);
  var green = green1.add(green2).add(green3);
  bands[GREEN_BAND_INDEX] = green.rename(GREEN);

  return ee.Image(ee.Image(bands).copyProperties(image, image.propertyNames()));
};

var collection = 'NOAA/GOES/16/MCMIPC/';
var imageName = '2020211203115800000';
var assetId = collection + imageName;
var image = applyScaleAndOffset(assetId);
Map.addLayer(image, goesRgbViz);

代码：

// NOAA GOES-17 full disk fire product for a single time slice.

var image = ee.Image('NOAA/GOES/17/FDCF/2019275191034100000');
var area = image.select('Area');
var temp = image.select('Temp');
var dqf = image.select('DQF');

var xmin = -205;  // On station as GOES-W
var xmax = xmin + 135;
Map.setCenter((xmin+xmax)/2, 15, 3);
var geometry = ee.Geometry.Rectangle([xmin, -65, xmax, 65], null, true);

var DQFVis = {
  min: 0,
  max: 5,
  palette: [
    'blanchedalmond',  // Good quality fire pixel
    'olive',           // Good quality fire free land
    'teal',            // Opaque cloud
                       // Bad surface type, sunglint, LZA threshold exceeded,
    'darkslateblue',   // off earth, or missing input data
    'lemonchiffon',    // Bad input data
    'burlywood'        // Algorithm failure
  ]};
Map.addLayer(dqf, DQFVis, 'DQF');

// Fires are small enough that they are difficult to see at the scale of
// an entire GOES image.  Buffer fires based on area to make them stand out.
var area = area.reduceToVectors({
  geometry: geometry,
  scale: 2000,
  geometryType: 'centroid',
  labelProperty: 'area',
  maxPixels: 1e10,
}).map(function(feature){
  return feature.buffer(ee.Number(feature.get('area')).add(1).pow(1.5));
});
Map.addLayer(area, {color: 'orange'}, 'area');

// Buffer fires based on temperature to make them stand out.
var temp = temp.reduceToVectors({
  geometry: geometry,
  scale: 2000,
  geometryType: 'centroid',
  labelProperty: 'temp',
  maxPixels: 1e10,
}).map(function(feature){
  return feature.buffer(ee.Number(feature.get('temp')).add(2).pow(1.2));
});
Map.addLayer(temp, {color: 'red'}, 'temp');

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原始发表：2024-02-01，如有侵权请联系 cloudcommunity@tencent.com 删除

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