基于多时相LANDSAT-TM数据作为农业气象模型输入的玉米产量估算

H. Bach
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引用次数: 26

摘要

为了验证先进产量形成模型遥感数据对玉米产量估算的准确性和及时性,我们为德国巴登-符腾堡州农村环境、食品和林业部开展了一个项目。该项目是在“特别产量估算”过程中进行的,这是欧盟进行的一项常规程序,目的是更准确地估算农业产量。所采用的方法是根据经大气校正的多时间/多光谱LANDSAT-TM数据进行基于现场的植物参数估计。利用农业气象植物生产模型进行产量预测。仅根据四次陆地卫星估算(5月至8月)和每日气象数据,就确定了1995年玉米田的粮食产量。模型产量与莱茵河谷上游23个试验田的特殊产量估算独立收集的结果进行了比较。基于landsat的估算(收获前六周)和特殊产量估算(收获时)之间的一致性显示出2.3%的相对误差。单块田的比较结果表明,在收获前6周,利用卫星信息估计玉米产量的平均相对精度为13%。所提出的方法可以转移到其他作物和地理区域。在未来的应用中,高光谱传感器在进一步提高遥感产量预测结果方面显示出巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Yield estimation of corn based on multitemporal LANDSAT-TM data as input for an agrometeorological model
In order to test remote sensing data with advanced yield formation models for accuracy and timeliness of yield estimation of corn, a project was conducted for the State Ministry for Rural Environment, Food, and Forestry of Baden-Wurttemberg (Germany). This project was carried out during the course of the `Special Yield Estimation', a regular procedure conducted for the European Union, to more accurately estimate agricultural yield. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on four LANDSAT-derived estimates (between May and August) and daily meteorological data, the grain yield of corn fields was determined for 1995. The modelled yields were compared with results gathered independently within the Special Yield Estimation for 23 test fields in the upper Rhine valley. The agreement between LANDSAT-based estimates (six weeks before harvest) and Special Yield Estimation (at harvest) shows a relative error of 2.3%. The comparison of the results for single fields shows that six weeks before harvest, the grain yield of corn was estimated with a mean relative accuracy of 13% using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results for yield prediction with remote sensing.
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