本数据集用于拟合FY4A\\AGRI的绿光通道反射率数据，分为训练数据集和预测数据集两个部分。训练数据集用于构建绿色可见光通道拟合模型，由AQUA/MODIS的L1B数据中四个反射率通道数据和绿光反射率通道数据构成。预测数据集用于拟合绿光通道反射率数据，该预测数据集由FY4A/AGRI的L1数据集的四个反射率通道数据构成，将这四个通道数据输入到绿色可见光通道拟合模型中会得到拟合的FY4A/AGRI绿光通道反射率数据。", "ds_source": "
训练数据集是由AQUA/MODIS的L1B数据和GEO数据处理得到的，数据类型为HDF文件，分辨率1km，每个文件包含r（红通道反射率，中心波长0.646μm）、g（绿通道反射率，中心波长0.55μm）、b（蓝通道反射率，中心波长0.469μm）、lir（近红外反射率，中心波长0.87μm）、nir（短波近红外通道反射率，中心波长1.64μm）、flag（数据质量标识）、latitude（纬度）、longitude （经度）6个数据表，反射率度量单位为db；预测数据集使用FY4A/AGRI的L1级数据。为了方便预测，数据处理过程不再单独分出，因此没有中间文件。数据处理过程为先读取FY4A/AGRI L1级数据的NOMChannel02（红通道信号值，中心波长0.65μm）、NOMChannel01（蓝通道信号值，中心波长0.47μm）、NOMChannel03（近红外信号值，中心波长0.83μm）、NOMChannel05（短波近红外通道信号值，中心波长1.61μm），再读取这四个通道对应的CALChannel*（各通道定标值）并用定标值将信号值转换为反射率。信号值数据单位为DN，反射率数据单位为db，定标值为信号值到反射率的转换矩阵，无单位。预测数据集中包含500M、1000M、2000M、4000M四种分辨率的数据。\n
其中 AQUA/MODIS 1KM 分辨率L1B数据和GEO数据从NASA官方网站（https://modis.gsfc.nasa.gov/）中获取、时间范围为2019年第32天、第91天、第121天、第182天、第213天共5天数据；\n
FY4A/AGRI 1000M 分辨率L1级数据由国家卫星气象中心提供。共包含2020年1月1日全天数据。", "ds_process_way": "
训练数据集：从L1B数据集中读取5个通道反射率数据，从GEO数据集中读取经度和纬度，获取以上数据集中所有不满足阈值或为填充值的点的位置，生成一个维度与以上数据表相同值全为0的数组做为质量标识，并将所有异常点位置的数值变为1，最后将以上处理后的6个数据表存储到中间文件中；\n
验证数据集：读取4个通道信号值数据和4个通道定标值数据，以信号值中每个点的数值做为通道定标值数组的行数从定标值数据中获取每个点对应的反射率。由于AQUA/MODIS 和FY4A/AGRI 两个仪器在光谱上有一定差异，因此需要对FY4A/AGRI的反射率数据进行光谱校正，采用的是瞬时星下点交叉比对方法（《光谱响应差异对高精度交叉定标的影响——以FY-3A/MERSI与EOS/MODIS为研究实例》）进行光谱校正，即获取两个仪器时间和距离在一定阈值内的反射率点，统计这些点的相差的倍率和差值，以这些倍率和差值做为校正系数对FY4A/AGRI的反射率进行校正。校正后输入到模型中进行预测。", "ds_quality": "
数据比较准确，达到精度要求。", "ds_acq_start_time": "2019-02-01 00:00:00", "ds_acq_end_time": "2019-07-01 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 91151225181, "ds_files_count": 989, "ds_format": "HDF", "ds_space_res": "1千米", "ds_time_res": "5分钟", "ds_coordinate": "其他", "ds_projection": "", "ds_thumbnail": "024dc11d-1b9d-4611-a610-fbf49c924c7e.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "保存于VRChG_data文件夹。包含由AQUA/MODIS L1B数据处理的2019年第32天、第91天、第121天、第182天、第213天共5天数据做为训练模型的数据。另附2020年1月1日到1月·5日·的FY4A/AGRI L1数据用于测试模型（部分数据有缺失，不影响模型测试）", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.5067" ], "quality_level": 3, "publish_time": "2023-06-26 08:29:58", "last_updated": "2025-04-24 16:06:31", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.RS.DB2884.2023", "i18n": { "en": { "title": "Model training and prediction dataset for constructing the green light channel of Fengyun 4 satellite based on deep learning", "ds_format": "HDF", "ds_source": "
The training dataset is obtained by processing L1B data from AQUA/MODIS and GEO data. The data type is HDF file with a resolution of 1km. Each file contains six data tables: r (red channel reflectance, center wavelength 0.646 μ m), g (green channel reflectance, center wavelength 0.55 μ m), b (blue channel reflectance, center wavelength 0.469 μ m), lir (near-infrared reflectance, center wavelength 0.87 μ m), nir (shortwave near-infrared channel reflectance, center wavelength 1.64 μ m), flag (data quality indicator), latitude, and longitude. The reflectance measurement unit is db; The prediction dataset uses L1 level data from FY4A/AGRI. For the convenience of prediction, the data processing process is no longer separated separately, so there are no intermediate files. The data processing process is to first read the NOMHannel02 (red channel signal value, center wavelength 0.65 μ m), NOMHannel01 (blue channel signal value, center wavelength 0.47 μ m), NOMHannel03 (near-infrared signal value, center wavelength 0.83 μ m), and NOMHannel05 (short wave near-infrared channel signal value, center wavelength 1.61 μ m) of FY4A/AGRI L1 level data, and then read the corresponding CALChannel * (calibration values for each channel) of these four channels and convert the signal values into reflectance using the calibration values. The unit of signal value data is DN, the unit of reflectance data is db, and the calibration value is the conversion matrix from signal value to reflectance, without units. The predicted dataset contains data with four resolutions: 500M, 1000M, 2000M, and 4000M.\n
The AQUA/MODIS 1KM resolution L1B data and GEO data were obtained from NASA's official website（ https://modis.gsfc.nasa.gov/ ）Obtained data from the 32nd, 91st, 121st, 182nd, and 213rd days of 2019, totaling 5 days;\n
The FY4A/AGRI 1000M resolution L1 level data is provided by the National Satellite Meteorological Center. Includes data for the entire day of January 1, 2020.", "ds_quality": "
The data is relatively accurate and meets the accuracy requirements.", "ds_ref_way": "", "ds_abstract": "
This dataset is used to fit the green channel reflectance data of FY4A \\ AGRI, and is divided into two parts: the training dataset and the prediction dataset. The training dataset is used to construct a green visible light channel fitting model, consisting of four reflectance channel data and green reflectance channel data from AQUA/MODIS L1B data. The prediction dataset is used to fit the reflectance data of the green light channel. The prediction dataset consists of four reflectance channel data from the L1 dataset of FY4A/AGRI. By inputting these four channel data into the green visible light channel fitting model, the fitted FY4A/AGRI green light channel reflectance data will be obtained.
", "ds_time_res": "5分钟", "ds_acq_place": "Global", "ds_space_res": "1千米", "ds_projection": "", "ds_process_way": "Training dataset: Read 5 channel reflectance data from the L1B dataset, read longitude and latitude from the GEO dataset, obtain the positions of all points in the above dataset that do not meet the threshold or are filled in values, generate an array with the same dimension as the above data table and all values of 0 as quality indicators, and change the values of all abnormal point positions to 1. Finally, store the processed 6 data tables in an intermediate file;\n
Validation dataset: Read 4 channel signal value data and 4 channel calibration value data, and use the value of each point in the signal value as the number of rows in the channel calibration value array to obtain the reflectivity corresponding to each point from the calibration value data. Due to the spectral differences between AQUA/MODIS and FY4A/AGRI instruments, it is necessary to perform spectral correction on the reflectance data of FY4A/AGRI. The instantaneous subsatellite point cross comparison method (\"The Influence of Spectral Response Differences on High Precision Cross Calibration - Taking FY-3A/MERSI and EOS/MODIS as Research Examples\") is used for spectral correction, which involves obtaining reflectance points of two instruments within a certain threshold in time and distance, calculating the magnification and difference of these points, and using these magnification and difference as correction coefficients to correct the reflectance of FY4A/AGRI. After calibration, input it into the model for prediction.", "ds_ref_instruction": "Save in VRChG_ The data folder. The training model includes 5 days of data processed by AQUA/MODIS L1B in 2019, including the 32nd, 91st, 121st, 182nd, and 213 days. Attached are FY4A/AGRI L1 data from January 1st to January 5th, 2020 for testing the model (some missing data does not affect model testing)" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "卫星云图" ], "ds_subject_tags": [ "遥感地质学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2019 ], "ds_contributors": [ { "true_name": "鄢俊洁", "email": "yanjj@cma.gov.cn", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" }, { "true_name": "瞿建华", "email": "qujh@cma.gov.cn", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" }, { "true_name": "袁明鸽", "email": "yuanming_ge@163.com", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" }, { "true_name": "张贺", "email": "kycg100@163.com", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "鄢俊洁", "email": "yanjj@cma.gov.cn", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" }, { "true_name": "瞿建华", "email": "qujh@cma.gov.cn", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" }, { "true_name": "袁明鸽", "email": "yuanming_ge@163.com", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" }, { "true_name": "张贺", "email": "kycg100@163.com", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" }, { "true_name": "安宏达", "email": "anhongdaaaa@163.com", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" } ], "ds_managers": [ { "true_name": "安宏达", "email": "anhongdaaaa@163.com", "work_for": "中国气象局北京华云星地通科技有限公司", "country": "中国" } ], "category": "遥感及产品" }