VPM模型与转产系数结合的吉林省玉米估产  

Estimating maize yield in Jilin Province of China using VPM modelcombined with conversion coefficient

作　　者：王永昊 王鸣雷 闫慧敏[1,2] 杨建宇 史文娇 WANG Yonghao;WANG Minglei;YAN Huimin;YANG Jianyu;SHI Wenjiao(Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Natural Resources Research,CAS,Beijing 100101,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing,100049,China;College of Land Science and Technology,China Agricultural University,Beijing 100193,China)

机构地区：[1]中国科学院地理科学与资源研究所陆地表层格局与模拟院重点实验室,北京100101 [2]中国科学院大学资源与环境学院,北京100049 [3]中国农业大学土地科学与技术学院,北京100193

出　　处：《农业工程学报》2024年第20期195-201,F0003,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划项目(2022YFB3903504);中国科学院战略性先导科技专项(XDA0440405);国家自然科学基金项目(72221002)。

摘　　要：准确估测农作物产量对于保障粮食安全、指导农业生产和调整生产策略等具有重要作用。为解决大范围估产参数确定难、成本高的问题,该研究基于2016–2021年Sentinel-2遥感数据和气象数据等,提出一种综合植被光合作用模型(vegetation photosynthesis model,VPM)与转产系数的产量估测方法,对吉林省30个玉米主产县进行估产研究。结果表明:1)该研究提出的模型估产精度较传统VPM模型表现出更高的准确性和可靠性(决定系数提升0.18;相对均方根误差降低3.24%);2)研究区玉米单产范围为7~13 t/hm^(2),高值区主要集中在中部地区,并且呈现由中部向西北和东南地区递减的趋势;3)模型敏感性分析表明,更精细的转产系数、更高分辨率的玉米空间分布数据和遥感数据能够有效提高模型估产精度。该研究提出的模型可为低成本、大规模、快速精确的估产工作提供解决方案,对实施农业估产具有重要的现实意义和推广价值。The objective of this study is to estimate the crop yields in the key corn-producing counties of Jilin Province,China.An accurate,efficient,scalable,and cost-effective model was developed using Sentinel-2 remote sensing data.High spatial and temporal resolution was offered along with the comprehensive meteorological data.A robust framework was built to estimate the maize yield.The limitations of traditional estimation were examined using ground surveys or lower-resolution satellite imagery.These were time-consuming,resource-intensive,and prone to errors,due to sampling biases or limited coverage.Sentinel-2 data was incorporated to provide a continuous and consistent view of crop growth patterns over a large area.The vegetation productivity model(VPM)was integrated to calibrate the yield conversion coefficient.VPM approach was used to estimate the crop biomass,according to the vegetation indices from remote sensing data.The biomass was converted directly into the yield.A yield conversion coefficient was also required to consider the agronomic conditions and crop varieties in the study area.The accuracy and relevance of the model were then enhanced to fine-tune the coefficient with the local yield data.The dynamic variables were integrated into the dynamic observation index in the VPM model.The relatively stable parameters were integrated into the conversion coefficient.The accuracy of yield estimation of the improved model(R²=0.53,RMSE=0.81,MRE=9.40%,NRMSE=11.73%)was superior to the traditional models(R²=0.35,RMSE=1.03 t/hm^(2),MRE=13.19%,NRMSE=14.97%).The obtained model was then applied to estimate the corn yields in the target counties of Jilin Province,where the yield range of maize per unit area was found to be 7-13 t/hm^(2).There was a distinct spatial pattern,where the higher yields were concentrated in the central regions and then gradually decreased towards the peripheries.This pattern was aligned with the geographical features,including soil fertility,irrigation availability,and climatic conditions.The

关 键 词：模型 农作物 产量 哨兵2号 吉林省 最大光能利用率 

分 类 号：S127[农业科学—农业基础科学]