Estimating maize yield in Jilin Province of China using VPM model combined with conversion coefficient
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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/hm2，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。
Abstract: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/hm2, 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/hm2. 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 high-resolution Sentinel-2 data was used to better capture these subtle variations in the yield patterns. Sensitivity analysis was conducted to further validate the robustness of the model. A systematic investigation was implemented to explore the impact of various factors, including the spatial resolution of remote sensing data, the vegetation indices, and the calibrated conversion coefficient. The precision of yield estimation was enhanced to employ the precise yield conversion coefficients, high-resolution remote sensing data, and maize planting distribution data. The rest factors were the spatial resolution of remote sensing data, the spatial resolution of crop distribution data, and the fineness of yield conversion coefficients. Furthermore, the future research direction was compared to further improve the accuracy of the model. The significant implications were achieved in agricultural modernization and food security. The timely and accurate information was obtained on crop yields, in order to optimize the planting strategies and decision-making on resource use. The findings can provide valuable insights into the influencing factors on agricultural productivity, and sustainability, particularly for food security at the regional and global levels.
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Keywords:
- models /
- crops /
- yield /
- Sentinel-2 /
- Jilin Province /
- maximum light use efficiency
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图 2 模型精度对比
注：R2代表决定系数，RMSE代表均方根误差，MRE代表平均相对误差，NRMSE代表归一化均方根误差。
Figure 2. Comparison of model accuracy
Note: R2 stands for coefficient of determination, RMSE stands for root mean square error, MRE stands for mean relative error, NRMSE stands for normalized root mean square error.
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图 4 2016—2021年研究区玉米估测单产空间分布
Figure 4. Spatial distribution of estimated maize yield from 2016 to 2021 in the study area
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图 5 2016—2021年研究区玉米估测单产与统计单产对比
Figure 5. Comparison of estimated and statistical maize yield from 2016 to 2021 in the study area
表 1 吉林省各地级市转产系数α
Table 1 Yield conversion coefficient α of each city in Jilin Province
城市
City转产系数
Yield conversion coefficient/(×10−2)城市
City转产系数
Yield conversion coefficient/(×10−2)长春 1.21 四平 1.33 通化 1.08 吉林 1.10 松原 1.19 白城 1.05 
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表 2 敏感性分析表
Table 2 Sensitivity analysis table
t·hm−2 项目Item 转产系数α 1 km CD MOD09A1 市级
City-level省级
Province-level城市
City白城 1.29 1.36 1.24 2.13 四平 0.94 1.30 1.34 1.74 长春 1.07 1.13 1.08 1.11 松原 0.93 0.96 1.03 0.99 通化 0.71 0.84 1.09 0.79 吉林 0.59 0.71 0.96 0.59 年份
Year2016 1.38 1.39 1.61 1.84 2017 0.91 0.98 0.97 1.38 2018 0.80 1.04 1.13 1.27 2019 0.67 0.86 1.01 1.22 2020 0.87 0.94 0.98 0.94 2021 0.77 0.94 0.98 1.31 注：从左到右4列数据分别是使用不同尺度（市级、省级）的转产系数、不同空间分辨率的玉米分布数据（1 km）和遥感数据（500 m）估产结果的RMSE值。 Note:Four columns from left to right were the RMSE of estimated maize yield by using different scale conversion coefficients (city level, provincial level), maize distribution data (1 km), and remote sensing data (500 m) with different spatial resolutions, respectively.
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