Google Earth Engine ——GCOM-C 进行长期和持续的全球叶面积指数数据集（JAXA/GCOM-C/L3/LAND/LAI/V2）

此乘积为单位地面面积的单面绿叶面积之和。

这是一个持续的数据集，延迟为 3-4 天，目前只有 2020 年的数据可用。供应商已经发布了重新处理整个积压工作的时间表。

GCOM-C 进行长期和持续的全球观测和数据收集，以阐明辐射收支和碳循环波动背后的机制，从而对未来温度上升做出准确预测。同时，与有气候数值模型的研究机构合作，有助于减少气候数值模型得出的温升预测误差，提高各种环境变化的预测精度。安装在 GCOM-C 上的 SGLI 是安装在 ADEOS-II (MIDORI II) 上的 Global Imager (GLI) 的连续传感器，是测量从近紫外到热红外区域 (380 nm-12 um) 的辐射的成像辐射计在 19 个频道中。在日本附近的中纬度地区，地面观测宽度超过 1,000 公里，可以进行大约每两天一次的全球观测。此外，SGLI 实现了比同类全局传感器更高的分辨率，并具有偏振观测功能和多角度观测功能。

This product is the sum of the one-sided green leaf area per unit ground area.

This is an ongoing dataset with a latency of 3-4 days, with only 2020 data currently available. The provider has released a ​​schedule​​ for the reprocessing of entire backlog.

GCOM-C conducts long-term and continuous global observation and data collection to elucidate the mechanism behind fluctuations in radiation budget and carbon cycle needed to make accurate projections regarding future temperature rise. At the same time, cooperating with research institutions having a climate numerical model, it contributes to reduction of errors in temperature rise prediction derived from the climate numerical model and improvement of accuracy of prediction of various environmental changes. SGLI mounted on GCOM-C is the succession sensor of the Global Imager (GLI) mounted on ADEOS-II (MIDORI II) and is the Imaging Radiometer which measures the radiation from near-ultraviolet to thermal infrared region (380 nm-12 um) in 19 channels. Global observation of once for approximately every two days is possible at mid-latitude near Japan by observation width at ground greater than 1,000 km. In addition, SGLI realizes high resolution than the similar global sensor and has a polarized observation function and a multi-angle observation function.

Dataset Availability

2020-01-01T00:00:00 - 2021-09-11T00:00:00

Dataset Provider

​​Global Change Observation Mission (GCOM)​​

Collection Snippet

​

​ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LAI/V2")​

​

Resolution

2.5 arc minutes

Bands Table

Name	Description	Min*	Max*	Units
LAI_AVE	The sum of the one-sided green leaf area per unit ground area.	65531	none
LAI_QA_flag	LAI QA
LAI_QA_flag Bitmask	Bits 0-1: Terrain type 0: water (land fraction = 0%) 1: mostly water (0% < land fraction < 50%) 2: mostly coastal (50% < land fraction < 100%) 3: land (land fraction = 100%)

* = Values are estimated

 影像属性：

Name	Type	Description
ALGORITHM_VERSION	String	Algorithm version
GRID_INTERVAL	String	Spatial resolution
GRID_INTERVAL_UNIT	String	Unit of GRID_INTERVAL
IMAGE_END_TIME	String	Image acquisition end time
IMAGE_START_TIME	String	Image acquisition start time
PROCESSING_RESULT	String	Good, Fair, Poor, NG
PROCESSING_UT	String	Processing time
PRODUCT_FILENAME	String	Source filename
PRODUCT_VERSION	String	Product version
SATELLITE_DIRECTION	String	Satellite orbit direction
LAI_AVE_OFFSET	String	Offset
LAI_AVE_SLOPE	String	Slope

数据说明：

This dataset is free to use without any restrictions (including commercial use). Anyone wishing to publish analyzed results or value added data products should properly credit the original G-Portal data, e.g., "PR data by Japan Aerospace Exploration Agency". For value added data products, please indicate the credit of the original G-Portal data, e.g., "Original data for this value added data product was provided by Japan Aerospace Exploration Agency."

See ​​G-Portal's terms of service (Article 7)​​ for additional information.

引用：

Ono, Y. (Nov. 2011). GCOM-C1 / SGLI LAI Product Algorithm Theoretical Basis Document (Version 1). Retrieved from ​​https://suzaku.eorc.jaxa.jp/GCOM_C/data/ATBD/ver1/Ono_Y_ATBD.pdf​​

var dataset = ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LAI/V2")
                .filterDate('2020-01-01', '2020-02-01');

// Multiply with slope coefficient
var dataset = dataset.mean().multiply(0.001).log10();

var visualization = {
  bands: ['LAI_AVE'],
  min: -3.0,
  max: 1.66,
  palette: [
    "040274","040281","0502a3","0502b8","0502ce","0502e6",
    "0602ff","235cb1","307ef3","269db1","30c8e2","32d3ef",
    "3be285","3ff38f","86e26f","3ae237","b5e22e","d6e21f",
    "fff705","ffd611","ffb613","ff8b13","ff6e08","ff500d",
    "ff0000","de0101","c21301","a71001","911003",
  ]
};

Map.setCenter(128.45, 33.33, 5);

Map.addLayer(dataset, visualization, "Leaf Area Index");