本数据集基于亚洲年度水稻地图，将多源预测因子整合至三个机器学习(ML)模型中，生成1995-2015年期间高空间分辨率(4 km)的季节性水稻产量数据集(AsiaRiceYield4km)。将预测因子分为4类，考虑最全面的水稻生长条件，并基于逆概率加权法确定最优ML模型。结果表明，AsiaRiceYield4km具有较好的季节性水稻产量估算精度(单粒水稻:R2=0.88, RMSE = 920 kg·ha-1;双季稻:R2=0.91, RMSE = 554 kg·ha-1;三粒稻:R2=0.93, RMSE = 588 kg·ha-1)。与SPAM模型相比，亚洲水稻产量4km的R2平均提高了0.20,RMSE平均降低了618 kg·ha-1。特别是，恒定的环境条件，包括经度、纬度、海拔和土壤性质，对水稻产量的估计贡献最大(~ 45%)。在水稻不同生育期，生殖期的预测因子对水稻产量预测的影响大于营养期和全生育期的预测因子。本数据集是一种新型的长期网格化水稻产量数据集，可以填补水稻主要产区高空间分辨率季节性产量产品的不足，促进全球农业可持续发展的相关研究。", "ds_source": "
通过收集综合水稻面积图、1400个行政单位的水稻产量(每个有水稻田的国家的最小行政——规模单位)、叶面积指数信息(来自遥感产品)和水稻产量 生长环境条件(地点、时间、土壤和气候)，估算水稻产量。", "ds_process_way": "
基于亚洲年度水稻地图，将多源预测因子整合至三个机器学习(ML)模型中，生成1995-2015年期间高空间分辨率(4 km)的季节性水稻产量数据集(AsiaRiceYield4km)。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "1995-01-01 00:00:00", "ds_acq_end_time": "2015-12-31 00:00:00", "ds_acq_place": "柬埔寨,中国,印度,印度尼西亚,日本,马来西亚,缅甸,尼泊尔,巴基斯坦,韩国,泰国,菲律宾,越南,孟加拉国", "ds_acq_lon_east": 25.0, "ds_acq_lat_south": -10.0, "ds_acq_lon_west": -170.0, "ds_acq_lat_north": 80.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 546767158, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "4000", "ds_time_res": "季节", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "9c650428-8f4b-470a-8025-f40299efb763.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-10-23 17:39:36", "last_updated": "2026-01-14 11:08:03", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB4057.2023", "i18n": { "en": { "title": "Asian Seasonal Rice Yield Dataset (1995-2015)", "ds_format": "tif", "ds_source": "
By collecting comprehensive rice area maps, rice yields from 1400 administrative units (the minimum administrative scale unit for each country with rice paddies), leaf area index information (from remote sensing products), and rice yield growth environmental conditions (location, time, soil, and climate), rice yields were estimated.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
This dataset is based on the annual rice map of Asia, integrating multiple predictive factors into three machine learning (ML) models to generate a seasonal rice yield dataset with high spatial resolution (4km) from 1995 to 2015 (AsiaRiceEild4km). Classify the predictive factors into four categories, consider the most comprehensive rice growth conditions, and determine the optimal ML model based on the inverse probability weighting method. The results showed that AsiaRiceYield 4km had good accuracy in estimating seasonal rice yield (single grain rice: R2=0.88, RMSE=920 kg · ha-1); Double cropping rice: R2=0.91, RMSE=554 kg · ha-1; Three grain rice: R2=0.93, RMSE=588 kg · ha-1). Compared with the SPAM model, the R2 of 4km rice yield in Asia increased by an average of 0.20, and the RMSE decreased by an average of 618 kg · ha-1. Especially, constant environmental conditions, including longitude, latitude, altitude, and soil properties, contribute the most (~45%) to the estimation of rice yield. At different growth stages of rice, the predictive factors of the reproductive stage have a greater impact on rice yield prediction than those of the nutritional stage and the entire growth stage. This dataset is a new type of long-term gridded rice yield dataset that can fill the gap of high spatial resolution seasonal yield products in major rice producing areas and promote relevant research on global agricultural sustainable development.
", "ds_time_res": "季节", "ds_acq_place": "Cambodia, China, India, Indonesia, Japan, Malaysia, Myanmar, Nepal, Pakistan, South Korea, Thailand, Philippines, Vietnam, Bangladesh", "ds_space_res": "4000", "ds_projection": "", "ds_process_way": "Based on the annual rice map of Asia, multiple predictive factors were integrated into three machine learning (ML) models to generate a seasonal rice yield dataset with high spatial resolution (4km) from 1995 to 2015 (AsiaRiceHead4km).", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "季节性水稻产量", "机器学习", "高分辨率", "亚洲产品" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "柬埔寨", "中国", "印度", "印度尼西亚", "日本", "马来西亚", "缅甸", "尼泊尔", "巴基斯坦", "越南", "孟加拉国", "韩国", "泰国", "菲律宾" ], "ds_time_tags": [ 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015 ], "ds_contributors": [ { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张朝", "email": "zhangzhao@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "生态" }