Google Earth Engine ——GCOM-C 进行长期和持续的海洋表面温度数据集(JAXA/GCOM-C/L3/OCEAN/SST/V2)

简介: Google Earth Engine ——GCOM-C 进行长期和持续的海洋表面温度数据集(JAXA/GCOM-C/L3/OCEAN/SST/V2)

This product is the temperature of sea surface.

A newer version JAXA/GCOM-C/L3/OCEAN/SST/V1 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.


本产品为海面温度。


更新版本的 JAXA/GCOM-C/L3/OCEAN/SST/V1 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/OCEAN/SST/V2")

Resolution

2.5 arc minutes

Bands Table

Name Description Min* Max* Units
SST_AVE Temperature of sea surface. 0 65531 °C
SST_QA_flag SST QA
SST_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 2-3: Terrain type
    • 0: day (night fraction = 0%)
    • 1: mostly day (0% < night fraction < 50%)
    • 2: mostly night (50% < night fraction < 100%)
    • 3: night (night 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
SST_AVE_OFFSET String Offset
SST_AVE_SLOPE String Slope


数据使用:

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:

Murakami, H. (Oct. 2018). ATBD of GCOM-C chlorophyll-a concentration algorithm (Version 1). Retrieved from https://suzaku.eorc.jaxa.jp/GCOM_C/data/ATBD/ver1/SGLI_Chla_algorithm_v10.pdf


代码:

var dataset = ee.ImageCollection("JAXA/GCOM-C/L3/OCEAN/SST/V2")
                .filterDate('2020-01-01', '2020-02-01');
// Multiply with slope coefficient and add offset
var dataset = dataset.mean().multiply(0.0012).add(-10).log10();
var vis = {
  bands: ['SST_AVE'],
  min: -2,
  max: 2,
  palette: ['000000', '005aff', '43c8c8', 'fff700', 'ff0000'],
};
Map.setCenter(128.45, 33.33, 5);
Map.addLayer(dataset, vis, "Sea Surface Temperature");



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