The MOD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an 8-day composite product produced at 500 meter pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover.
The pixel values for the two Evapotranspiration layers (ET & PET) are the sum of all eight days within the composite period. The pixel values for the two Latent Heat layers (LE & PLE) are the average of all eight days within the composite period. Note that the last 8-day period of each year is a 5 or 6-day composite period, depending on the year.
According to the "Layers" section of the dataset docs, the class assignment in fill values 32761 through 32767 might be inaccurate. They are not included in the EE assets.
Documentation:
- User's Guide
- Algorithm Theoretical Basis Document (ATBD)
- General Documentation
MOD16A2第6版蒸发/热量通量产品是一个以500米像素分辨率制作的8天综合产品。用于MOD16数据产品收集的算法是基于Penman-Monteith方程的逻辑，其中包括每日气象再分析数据的输入，以及MODIS遥感数据产品，如植被属性动态、反照率和土地覆盖。
两个蒸散层（ET和PET）的像素值是综合期内所有8天的总和。两个潜热层（LE和PLE）的像素值是综合期内所有8天的平均值。请注意，每年的最后8天是一个5或6天的综合期，这取决于年份。
根据数据集文档的 "层 "部分，填充值32761到32767中的类别分配可能是不准确的。它们不包括在EE资产中。
Dataset Availability
2001-01-01T00:00:00 - 2021-09-06T00:00:00
Dataset Provider
NASA LP DAAC at the USGS EROS Center
Collection Snippet
ee.ImageCollection("MODIS/006/MOD16A2")
Resolution
500 meters
Bands Table
Name | Description | Min | Max | Units | Scale |
|---|---|---|---|---|---|
ET | Total evapotranspiration | -32767 | 32700 | kg/m^2/8day | 0.1 |
LE | Average latent heat flux | -32767 | 32700 | J/m^2/day | 10000 |
PET | Total potential evapotranspiration | -32767 | 32700 | kg/m^2/8day | 0.1 |
PLE | Average potential latent heat flux | -32767 | 32700 | J/m^2/day | 10000 |
ET_QC | Evapotranspiration quality control flags | 0 | |||
ET_QC Bitmask | Bit 0: MODLAND_QC bits 0: Good quality (main algorithm with or without saturation)1: Other quality (back-up algorithm or fill values)Bit 1: Sensor 0: Terra1: AquaBit 2: Dead detector 0: Detectors apparently fine for up to 50% of channels 1, 21: Dead detectors caused >50% adjacent detector retrievalBits 3-4: Cloud state 0: Significant clouds NOT present (clear)1: Significant clouds WERE present2: Mixed cloud present on pixel3: Cloud state not defined, assumed clearBits 5-7: SCF_QC 5-level confidence quality score 0: Main (RT) method used, best result possible (no saturation)1: Main (RT) method used with saturation. Good, very usable2: Main (RT) method failed due to bad geometry, empirical algorithm used3: Main (RT) method failed due to problems other than geometry, empirical algorithm used4: Pixel not produced at all, value couldn't be retrieved (possible reasons: bad L1B data, unusable MOD09GA data) | ||||
- Bit 0: MODLAND_QC bits
- 0: Good quality (main algorithm with or without saturation)
- 1: Other quality (back-up algorithm or fill values)
- Bit 1: Sensor
- 0: Terra
- 1: Aqua
- Bit 2: Dead detector
- 0: Detectors apparently fine for up to 50% of channels 1, 2
- 1: Dead detectors caused >50% adjacent detector retrieval
- Bits 3-4: Cloud state
- 0: Significant clouds NOT present (clear)
- 1: Significant clouds WERE present
- 2: Mixed cloud present on pixel
- 3: Cloud state not defined, assumed clear
- Bits 5-7: SCF_QC 5-level confidence quality score
- 0: Main (RT) method used, best result possible (no saturation)
- 1: Main (RT) method used with saturation. Good, very usable
- 2: Main (RT) method failed due to bad geometry, empirical algorithm used
- 3: Main (RT) method failed due to problems other than geometry, empirical algorithm used
- 4: Pixel not produced at all, value couldn't be retrieved (possible reasons: bad L1B data, unusable MOD09GA data)
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 - MOD16A2
代码：
代码语言：javascript复制var dataset = ee.ImageCollection('MODIS/006/MOD16A2')
.filter(ee.Filter.date('2018-01-01', '2018-05-01'));
var evapotranspiration = dataset.select('ET');
var evapotranspirationVis = {
min: 0.0,
max: 300.0,
palette: [
'ffffff', 'fcd163', '99b718', '66a000', '3e8601', '207401', '056201',
'004c00', '011301'
],
};
Map.setCenter(6.746, 46.529, 2);
Map.addLayer(evapotranspiration, evapotranspirationVis, 'Evapotranspiration');
