本研究基于2014-2018年GLASS LAI、MOD13A1、MYD13A1、Landsat7-ETM+、Landsat 8-OLI数据，通过ESTARFM-IB方式获取时间连续的8d/30m分辨率 LAI，再结合分段线性内插得到1d/30m LAI。
\n数据命名方式：yyyymmdd，yyyy表示为年份，mm表示为月份，dd表示为日。
", "ds_source": "数据来源于GLASS LAI、MOD13A1、MYD13A1、Landsat7-ETM+、Landsat 8-OLI。
", "ds_process_way": "（1）建立GLASS LAI与MOD13A1、MYD13A1 NDVI线性关系：基于2014-2018年GLASS LAI、MOD13A1、MYD13A1 NDVI的每一景影像，选取对应时间的样本点，提取样本点对应的像元值，通过这些像元值在30m尺度上建立LAI与NDVI线性关系LAI-NDVI；
\n（2）利用预处理后Landsat 7-ETM+、8-OLI的红、近红波段计算Landsat NDVI；
\n（3）将Landsat NDVI作为变量输入到LAI-NDVI线性关系中得到Landsat LAI；
\n（4）将3期GLASS LAI和2期Landsat LAI输入ESTARFM 模型中，融合得到8d/30m时空分辨率的ESTARFM-LAI；
\n（5）分段线性内插8d/30mLAI得到1d/30m的叶面积指数。
", "ds_quality": "GLASS LAI较其他遥感LAI产品具有较高精度和数据完整性，不确定性较低，认为GLASS LAI准确性较高。故将本研究得到的高时空分辨率LAI（1d/30m）数据集与GLASS LAI产品的时空特征进行对比，以验证数据集精度。高时空分辨率LAI较原始GLASS LAI展示了更多空间细节（如细小河谷），轮廓与纹理特征更加明显，地物的边界更为清晰。高时空分辨率LAI很好地反映了不同季节，LAI值在空间上随着植物生长周期而动态变化，表现为研究区LAI值冬季最低(12月、1月、2月)-春季上升(3月、4月、5月)-夏季最高(6月、7月、8月)-秋季下降(9月、10月、11月)，这种变化特征在植被覆盖较高的研究区东部最为明显。高时空分辨率LAI与原始GLASS LAI的月平均LAI值和8日平均LAI值存在显著的线性关系。
", "ds_acq_start_time": "2014-01-01 00:00:00", "ds_acq_end_time": "2018-12-27 00:00:00", "ds_acq_place": "巴音河流域", "ds_acq_lon_east": 96.71666666666667, "ds_acq_lat_south": 37.2, "ds_acq_lon_west": 98.03333333333333, "ds_acq_lat_north": 38.11666666666667, "ds_acq_alt_low": 3047.0, "ds_acq_alt_high": 5234.0, "ds_share_type": "open-access", "ds_total_size": 43173136662, "ds_files_count": 7, "ds_format": "TIF", "ds_space_res": "30", "ds_time_res": "天", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "2321ffd6-3512-467e-bb73-fa5adaddc2df.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "09314967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-09-10 09:45:54", "last_updated": "2025-04-23 09:47:27", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.2021.1972", "i18n": { "en": { "title": "Bayin River Basin High Spatiotemporal Resolution Leaf Area Index (LAI) Dataset", "ds_format": "TIF", "ds_source": "The data were sourced from the following standard remote sensing products:\n\n
GLASS LAI: Global Land Surface Satellite Leaf Area Index product.\n\n
MOD13A1: MODIS Terra Vegetation Indices 16-Day L3 Global 500m.\n\n
MYD13A1: MODIS Aqua Vegetation Indices 16-Day L3 Global 500m.\n\n
Landsat 7 ETM+: Enhanced Thematic Mapper Plus (30m multispectral).\n\n
Landsat 8 OLI: Operational Land Imager (30m multispectral).", "ds_quality": "
The GLASS (Global LAnd Surface Satellite) LAI product exhibits superior accuracy and data integrity with lower uncertainty relative to other remote sensing-derived LAI products, establishing it as a high-accuracy reference dataset. Accordingly, we conducted a spatiotemporal feature comparison between our high spatiotemporal resolution LAI dataset (1-day/30m) and GLASS LAI to validate data precision.\n
The high spatial and temporal resolution LAI shows more spatial details (e.g., small river valleys) than the original GLASS LAI, and the contour and texture features are more obvious, and the boundaries of the features are clearer. The high spatial and temporal resolution LAI reflects the different seasons well, and the LAI values change dynamically with the plant growth cycle in space, which shows that the LAI values in the study area are the lowest in winter (December, January, and February) - rising in spring (March, April, and May) - the highest in summer (June, July, and August) - and decreasing in fall (September, October, and November), and this change characteristic is most obvious in the eastern part of the study area where there is high vegetative cover. This variation was most obvious in the eastern part of the study area with higher vegetation cover. There is a significant linear relationship between the high temporal and spatial resolution LAI and the monthly and 8-day mean LAI values of the original GLASS LAI.", "ds_ref_way": "", "ds_abstract": "
This study utilized GLASS LAI, MOD13A1, MYD13A1, Landsat 7-ETM+, and Landsat 8-OLI data (2014-2018) to generate temporally continuous 8-day/30m resolution LAI through the ESTARFM-IB method, followed by piecewise linear interpolation to obtain 1-day/30m LAI.\n
Data naming: yyyymmdd, yyyy means year, mm means month, dd means day.
", "ds_time_res": "天", "ds_acq_place": "Bayin River Basin", "ds_space_res": "30", "ds_projection": "", "ds_process_way": "(1)We established linear relationships between GLASS LAI and MODIS NDVI products (MOD13A1/MYD13A1) through the following methodology: For each coincident scene of GLASS LAI, MOD13A1, and MYD13A1 NDVI data during 2014-2018, we selected temporally matched sample points, extracted corresponding pixel values, and developed LAI-NDVI regression models at 30m spatial resolution.\n
(2) The Normalized Difference Vegetation Index (NDVI) was calculated using preprocessed red and near-infrared (NIR) bands from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) data.\n (3) The Landsat-derived NDVI values were input into the pre-established LAI-NDVI linear regression model to estimate high-resolution Leaf Area Index (LAI) values.\n (4) The three-period GLASS LAI data and two-period Landsat LAI data were input into the ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to generate fused ESTARFM-LAI products with 8-day/30m spatiotemporal resolution.\n (5) The 8-day/30m LAI product was temporally downscaled to daily (1d/30m) resolution using piecewise linear interpolation, generating continuous Leaf Area Index (LAI) time series while preserving the original spatial fidelity.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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": [ "叶面积指数", "LAI" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青海省", "巴音河流域", "德令哈" ], "ds_time_tags": [ 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "傅笛", "email": "fud2020@163.com", "work_for": "青海师范大学地理科学学院", "country": "中国" }, { "true_name": "金鑫", "email": "jinx13@lzu.edu.cn", "work_for": "青海师范大学地理科学学院", "country": "中国" }, { "true_name": "金彦香", "email": "jinyx13@lzu.edu.cn", "work_for": "青海师范大学地理科学学院", "country": "中国" }, { "true_name": "毛旭锋", "email": "maoxufeng@yeah.net", "work_for": "青海师范大学地理科学学院", "country": "中国" }, { "true_name": "翟婧雅", "email": "jingyasea@163.com", "work_for": "青海师范大学地理科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "傅笛", "email": "fud2020@163.com", "work_for": "青海师范大学地理科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "傅笛", "email": "fud2020@163.com", "work_for": "青海师范大学地理科学学院", "country": "中国" }, { "true_name": "金鑫", "email": "jinx13@lzu.edu.cn", "work_for": "青海师范大学地理科学学院", "country": "中国" } ], "category": "遥感及产品" }