中国农业气象 ›› 2026, Vol. 47 ›› Issue (9): 1427-1438.doi: 10.3969/j.issn.1000-6362.2026.09.006

• 农业生物气象栏目 • 上一篇    下一篇

黑龙江粮食主产区主要粮食作物长势分级监测及收获评估

薄宇,吴琼,姜丽霞,石慕真,李百超,李正泉,程春香,王宇凡,王营, 潘雪,曲芙瑶   

  1. 1. 黑龙江省生态气象中心,哈尔滨 150030;2. 黑龙江省气象科学研究所,哈尔滨 150030;3. 浙江省气候中心,杭州 310052;4. 黑龙江省气候中心,哈尔滨 150030
  • 收稿日期:2025-06-24 出版日期:2026-09-20 发布日期:2026-09-18
  • 作者简介:薄宇,E-mail:53967422@qq.com
  • 基金资助:
    黑龙江省气象局创新发展专项项目(HQZC2025044);黑龙江省气象局自筹项目(HQZC2023068)

Growth Grading Monitoring and Harvest Assessment of Major Grain Crops in the Major Grain−producing Areas of Heilongjiang Province

BO Yu, WU Qiong, JIANG Li-xia, SHI Mu-zhen, LI Bai-chao, LI Zheng-quan, CHENG Chun-xiang, WANG Yu-fan, WANG Ying, PAN Xue, QU Fu-yao   

  1. 1.Heilongjiang Ecological Meteorology Center, Harbin 150030, China; 2.Heilongjiang Province Institute of Meteorological Science, Harbin 150030; 3.Zhejiang Climate Center, Hangzhou 310052; 4.Heilongjiang Climate Center, Harbin 150030
  • Received:2025-06-24 Online:2026-09-20 Published:2026-09-18

摘要:

基于遥感影像和农业观测数据,结合2019−2023年黑龙江粮食主产区归一化植被指数(Normalized difference vegetation index,NDVI)旬值和日值,构建主要粮食作物长势分级模型(Crop growth grading model,CGG)与收获监测模型(Harvest monitoring model,HM),分析黑龙江粮食主产区主要粮食作物长势以及收获区域的时空演变特征,以期提升区域智慧气象保障粮食安全能力,助力区域农业发展。结果表明:(1)构建的CGG模型和HM模型可较好判识2019−2023年黑龙江粮食主产区主要粮食作物长势和收获,总体平均绝对百分误差(Mean absolute percentage error,MAPE)低于25.0%;(2)CGG模型模拟发现2019−2023年黑龙江粮食主产区主要粮食作物长势逐年向好明显,2023年主要粮食作物生长季黑龙江粮食主产区生长状态优良和较差等级的时段,分别占全生长季的43.3%和8.3%;(3)基于HM模型可知,2019−2023年截至10月下旬黑龙江粮食主产区绝大部分地区主要粮食作物已收获,大豆收获进度略晚于玉米和水稻。说明构建的CGG模型和HM模型可评估黑龙江粮食主产区主要粮食作物长势和收获情况,2019−2023年黑龙江粮食主产区主要粮食作物长势向好,收获及时。

关键词: 黑龙江省, 主要粮食作物, 作物长势, 收获进度

Abstract:

Real−time monitoring of the growth and harvesting progress of major grain crops (corn, rice and soybean) is crucial for national food security and precise smart agricultural services. Based on remote sensing images and agricultural observation data, this study established a self−developed crop growth grading model (CGG) and harvest monitoring model (HM) by combining the ten−day and daily normalized difference vegetation index (NDVI) datasets in the major grain−producing areas of Heilongjiang province from 2019 to 2023. These models were applied to analyze the spatiotemporal evolution characteristics of major grain crop growth and harvest areas, aiming to improve the capability of regional smart meteorological services in guaranteeing food security and boosting regional agricultural development. The results showed that: (1) the self−developed CGG and HM models could effectively identify the growth status and harvesting conditions of major grain crops in the study area from 2019 to 2023, with an overall mean absolute percentage error (MAPE) lower than 25.0%. (2) Simulations via the CGG model revealed a significant year−by−year improvement in the growth of major grain crops in the major grain−producing areas of Heilongjiang Province from 2019 to 2023. During the crop growing season of 2023, the periods with excellent and poor growth status accounted for 43.3% and 8.3% of the entire growing season, respectively. (3) Based on the HM model, by late October from 2019 to 2023, most areas of the major grain−producing areas in Heilongjiang province had completed the harvesting of major grain crops, and the harvesting progress of soybeans was slightly later than that of corn and rice. It is demonstrated that the self−developed CGG and HM models can effectively evaluate the growth and harvesting status of major grain crops in the major grain−producing areas of Heilongjiang province, and the crops showed a favorable growth trend with timely harvesting in the region from 2019 to 2023.

Key words: Heilongjiang province, Major grain crops, Crop growth, Harvest progress