Google Earth Engine ——GCOM-C 進行長期和持續的全球葉面積指數資料集（JAXA/GCOM-C/L3/LAND/LAI/V2）

此乘積為機關地面面積的單面綠葉面積之和。

這是一個持續的資料集，延遲為 3-4 天，目前隻有 2020 年的資料可用。供應商已經釋出了重新處理整個積壓工作的時間表。

GCOM-C 進行長期和持續的全球觀測和資料收集，以闡明輻射收支和碳循環波動背後的機制，進而對未來溫度上升做出準确預測。同時，與有氣候數值模型的研究機構合作，有助于減少氣候數值模型得出的溫升預測誤差，提高各種環境變化的預測精度。安裝在 GCOM-C 上的 SGLI 是安裝在 ADEOS-II (MIDORI II) 上的 Global Imager (GLI) 的連續傳感器，是測量從近紫外到熱紅外區域 (380 nm-12 um) 的輻射的成像輻射計在 19 個頻道中。在日本附近的中緯度地區，地面觀測寬度超過 1,000 公裡，可以進行大約每兩天一次的全球觀測。此外，SGLI 實作了比同類全局傳感器更高的分辨率，并具有偏振觀測功能和多角度觀測功能。

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

 影像屬性：

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

資料說明：

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");