GLASS植被覆盖度产品（Fractional Vegetation Cover，简称FVC）基于机器学习方法训练出从预处理的反射率到FVC值的关系模型，用以生产全球陆表植被覆盖度产品。GLASS-FVC产品空间范围为全球陆表, 时间分辨率为8天，全年共监测46 次。其中，基于AVHRR数据生产的植被覆盖度遥感数据集产品的时间范围为1981~2020，采用经纬度投影方式,空间分辨率为 5 km×5 km；基于MODIS数据生产的植被覆盖度遥感数据集产品时间范围为2000~2021 年，采用SIN投影方式,空间分辨率为 0.5 km×0.5 km。GLASS-FVC产品输出格式为HDF-EOS标准格式，包含一个植被覆盖度数据集。
\n本数据集收集了甘肃省范围内h25v04、h25v05、h26v05三个条带2000-2021年的数据。
", "ds_source": "马里兰大学GLASS产品http://www.glass.umd.edu/Download.html
", "ds_process_way": "GLASS FVC产品算法基于机器学习方法，使用了从全球分布式高空间分辨率卫星数据生成的训练样本。最初，用于MODIS数据的GLASS FVC乘积算法是使用通用回归神经网络(GRNN)方法，训练样本数据Thematic Mapper(TM)和Enhanced Thematic Mapper plus(ETM +)数据生成的。但是，在生成长期全球GLASS FVC产品的过程中，发现GRNNs方法的计算效率并不令人满意。因此，评估了四种机器学习方法，包括反向传播神经网络(BPNN)，GRNN，支持向量回归(SVR)和多元自适应回归样条(MARS)。
\n还开发了用于AVHRR数据的GLASS FVC算法，以与GLASS MODIS FVC产品配合使用。它基于GLASS MODIS FVC产品，可从AVHRR和MODIS数据实现FVC估计的连续性。
", "ds_quality": "使用高分辨率卫星数据和地面测量的估计值进行了广泛的验证实验。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "甘肃省", "ds_acq_lon_east": 108.7075, "ds_acq_lat_south": 32.596111111111114, "ds_acq_lon_west": 92.33777777777777, "ds_acq_lat_north": 42.79333333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 8832901115, "ds_files_count": 9028, "ds_format": "hdf", "ds_space_res": "500", "ds_time_res": "8天", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "10dec527-6c72-4293-a1bb-860834609c9c.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "526ff655-4cd4-4650-bb86-6fd3481dfb65", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2023-12-27 15:29:43", "last_updated": "2025-06-30 16:29:51", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.GSEER.DB4144.2023", "i18n": { "en": { "title": "Partial Vegetation Coverage (FVC) Dataset of Gansu Province (2000-2021)", "ds_format": "hdf", "ds_source": "
University of Maryland GLASS Products http://www.glass.umd.edu/Download.html
", "ds_quality": "Extensive validation experiments were conducted using high-resolution satellite data and estimated values from ground measurements.
", "ds_ref_way": "", "ds_abstract": "
The GLASS Fractional Vegetation Cover (FVC) product is based on machine learning methods to train a relationship model from preprocessed reflectance to FVC value, which is used to produce global land surface vegetation cover products. The GLASS-FVC product has a spatial range of the global land surface, with a time resolution of 8 days, and is monitored a total of 46 times throughout the year. Among them, the vegetation coverage remote sensing dataset products produced based on AVHRR data have a time range of 1981-2020, using latitude and longitude projection with a spatial resolution of 5 km × 5 km; the vegetation coverage remote sensing dataset products produced based on MODIS data have a time range of 2000-2021, using SIN projection with a spatial resolution of 0.5 km × 0.5 km. The GLASS-FVC product output format is HDF-EOS standard format, including a vegetation coverage dataset.
\nThis dataset collected data from three bands, H25v04, H25v05, and H26v05, in Gansu Province from 2000 to 2021.
", "ds_time_res": "8天", "ds_acq_place": "Gansu Province", "ds_space_res": "500", "ds_projection": "", "ds_process_way": "The GLASS FVC product algorithm is based on machine learning methods and uses training samples generated from globally distributed high-resolution satellite data. Initially, the GLASS FVC product algorithm used for MODIS data was generated using the Generalized Regression Neural Network (GRNN) method, training sample data from the Poetic Mapper (TM) and Enhanced Poetic Mapper plus (ETM+) datasets. However, in the process of generating long-term global GLASS FVC products, it was found that the computational efficiency of the GRNNs method was not satisfactory. Therefore, four machine learning methods were evaluated, including backpropagation neural network (BPNN), GRNN, support vector regression (SVR), and multivariate adaptive regression spline (MARS).
\nWe have also developed the GLASS FVC algorithm for AVHRR data to be used in conjunction with the GLASS MODIS FVC product. It is based on the GLASS MODIS FVC product and can achieve continuity in FVC estimation from AVHRR and MODIS data.
", "ds_ref_instruction": "" } }, "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": [ "陆表特征参量产品", "GLASS", "植被覆盖率(FVC)" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "甘肃省" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "张耀南", "email": "yaonan@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李红星", "email": "lihongxing@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "敏玉芳", "email": "myf@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "生态" }