利用基于遥感的过程模型模拟作物产量差：以华北平原冬小麦研究为例

doi:10.1016/j.jia.2023.02.003

摘要/Abstract

摘要：

理解作物产量差（YG）的空间分布对提高作物产量至关重要。目前的研究通常集中在站点尺度上，当扩展到域尺度上可能会导致相当大的不确定性。为了解决这一问题，本研究采用基于改进北方生态系统生产力模拟器（BEPS）的遥感驱动过程冬小麦作物产量模型（PRYM-Wheat），模拟了2015-2019年华北平原冬小麦的产量差。通过统计产量数据进行产量验证，表明PRYM-Wheat模型在模拟冬小麦实际产量（Ya）方面具有良好的性能。研究区Ya的分布差异较大，由东南向西北呈下降趋势。遥感估算结果表明，研究区域的平均YG为6400.6 kg ha^-1。江苏省YG产量最大，为7307.4 kg ha^-1。安徽YG最小，为5842.1 kg ha^-1。通过分析YG对环境因素的响应，发现YG与降水之间没有明显的相关性，而YG与累积温度之间存在较弱的负相关关系；此外，YG与海拔升高呈正相关。总的来说，研究作物产量差（YG）可以为今后提高作物产量提供方向。

Abstract:

Understanding the spatial distribution of the crop yield gap (YG) is essential for improving crop yields. Recent studies have typically focused on the site scale, which may lead to considerable uncertainties when scaled to the regional scale. To mitigate this issue, this study used a process-based and remote sensing driven crop yield model for winter wheat (PRYM-Wheat), which was derived from the boreal ecosystem productivity simulator (BEPS), to simulate the YG of winter wheat in the North China Plain from 2015 to 2019. Yield validation based on statistical yield data revealed good performance of the PRYM-Wheat Model in simulating winter wheat actual yield (Ya). The distribution of Ya across the North China Plain showed great heterogeneity, decreasing from southeast to northwest. The remote sensing-estimated results show that the average YG of the study area was 6 400.6 kg ha^–1. The YG of Jiangsu Province was the largest, at 7 307.4 kg ha^–1, while the YG of Anhui Province was the smallest, at 5 842.1 kg ha^–1. An analysis of the responses of YG to environmental factors showed no obvious correlation between YG and precipitation, but there was a weak negative correlation between YG and accumulated temperature. In addition, the YG was positively correlated with elevation. In general, studying the specific features of the YG can provide directions for increasing crop yields in the future

Key words: remote sensing , PRYM-Wheat model , yield gap , environmental factors , North China Plain

YANG Xu, ZHANG Jia-hua, YANG Shan-shan, WANG Jing-wen, BAI Yun, ZHANG Sha. 利用基于遥感的过程模型模拟作物产量差：以华北平原冬小麦研究为例 [J]. Journal of Integrative Agriculture, 2023, 22(10): 2993-3005.

YANG Xu, ZHANG Jia-hua, YANG Shan-shan, WANG Jing-wen, BAI Yun, ZHANG Sha. Modelling the crop yield gap with a remote sensing-based process model: A case study of winter wheat in the North China Plain[J]. Journal of Integrative Agriculture, 2023, 22(10): 2993-3005.

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