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/V1）

2024-02-02 11:25:01 浏览数 (1)

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

A newer version JAXA/GCOM-C/L3/LAND/LAI/V2 is also available for this dataset which uses this algorithm for processing.

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.

代码语言：javascript复制
此乘积为单位地面面积的单面绿叶面积之和。

更新版本的 JAXA/GCOM-C/L3/LAND/LAI/V2 也可用于此数据集，该数据集使用此算法进行处理。

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。

Dataset Availability

2018-01-01T00:00:00 - 2020-06-28T00:00:00

Dataset Provider

Global Change Observation Mission (GCOM)

Collection Snippet

ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LAI/V1")

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.

0

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%)

  • 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

代码：

代码语言：javascript复制
var dataset = ee.ImageCollection("JAXA/GCOM-C/L3/LAND/LAI/V1")
                .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");
data google string 模型 数据

0 人点赞