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基于多源遥感数据和EC-LUE模型 的冬小麦产量估算
欧阳宏达
南京大学地理与海洋科学学院
摘要:
的冬小麦产量估算:【目的】及时、准确、无损的估算冬小麦产量有助于粮食生产管理和粮食安全。为了在冬小麦收割获得产量数据,【方法】本文使用Sentinel-2 的红光波段和短波红外数据及MOD09Q1数据,使用ESTARFM融合方法,生成冬小麦生长期(3-6月)内的八天NDVI高空间分辨率时间序列数据。结合MERRA-2气象同化数据,使用EC-LUE模型进行GPP的模拟估算,并使用收割指数方法将之转化为冬小麦产量,估算结果与美国农业部门公布的县级产量数据进行比较验证。【结果】实验表明,Sentinel-2与MOD09Q1融合 NDVI具有良好的融合精度,相关系数在0.6-0.87之间。基于融合NDVI 估算的GPP相比较于MOD17A2H具有更好的空间细节和纹理。2017-2020年估算产量MAE为8.41Bu/Acre,而RMSE为9.7Bu/Acre。【结论】基准影像数量及其与预测日期的时间差会影响融合的精度,总体上能用于后续GPP模拟;EC-LUE模型较好的模拟了农作物的GPP水平和产量,该方法可解决多种农作物混合种植时带来的异质性问题,具有可移植性;基于收割指数方法将生长期内累计的GPP能转换为产量信息,能满足在作物收割之前的产量估算需求。
关键词:  ESTARFM  GPP  光能利用率模型  冬小麦  产量估算
DOI:
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基金项目:国家自然科学基金面上项目“基于高斯过程回归和综合遥感干旱指数的农业干旱预测模型研究(42271321)
Winter wheat yield estimation based on multi-source remote sensing data and EC-LUE model
Ouyang Hongda
School of Geography and Ocean Science,Nanjing University
Abstract:
[Purpose] The timely, accurate, and non-destructive estimation of winter wheat yield is important for grain production management and food security. To obtain yield data during winter wheat harvesting, [Method] this study used Sentinel-2 red and shortwave infrared data and MOD09Q1 data, and the ESTARFM fusion method to generate an eight-day high spatial resolution time series of NDVI data during the winter wheat growth period (March-June). In combination with MERRA-2 meteorological assimilation data, the EC-LUE model was used to simulate GPP and convert it to winter wheat yield using the harvest index method. The estimation results were compared and verified with county-level yield data published by the United States Department of Agriculture. [Result] The experiment results showed that the fusion of Sentinel-2 and MOD09Q1 NDVI had good fusion accuracy, with a correlation coefficient between 0.6 and 0.87. GPP estimated based on fusion NDVI had better spatial details and texture compared to MOD17A2H. The MAE and RMSE of the estimated yield from 2017 to 2020 were 8.41 Bu/Acre and 9.7 Bu/Acre, respectively. [Conclusion] The results of this study showed that the number of baseline images and the time difference between them and the predicted date would affect the accuracy of fusion. However, overall, this method could be used for subsequent GPP simulation. The EC-LUE model could simulate the GPP level and yield of crops well, and this method could solve the heterogeneity problem caused by mixed planting of various crops and was portable. The harvest index method could convert the accumulated GPP during the growth period into yield information, which could meet the demand for yield estimation before crop harvest.
Key words:  estarfm  gpp  light use efficiency model  winter wheat  yield estimation