Winter Wheat Green-up Date Variation and its Diverse Response on the Hydrothermal Conditions over the North China Plain, Using MODIS Time-Series Data

Linghui Guo,Shaohong Wu,Linlin Zhang,Jiangbo Gao,Chengyuan Hao,Xiangming Xiao

doi:10.3390/rs11131593

Abstract

Vegetation phenology plays a critical role in the dynamic response of terrestrial ecosystems to climate change. However, the relationship between the phenology of winter wheat and hydrothermal factors is inadequate, especially in typical agricultural areas. In this study, the possible effects of preseason climate changes on the green-up date (GUD) of winter wheat over the North China Plain (NCP) was investigated, using the MODIS EVI 8-day time-series data from 2000 to 2015, as well as the concurrent monthly mean temperature (Tm), mean maximum (Tmax) and minimum temperature (Tmin) and total precipitation (TP) data. Firstly, we quantitatively identified the time lag effects of winter wheat GUD responses to different climatic factors; then, the major driving factors for winter wheat GUD were further explored by applying multiple linear regression models. The results showed that the time lag effects of winter wheat GUD response to climatic factors were site- and climatic parameters-dependent. Negative temperature effects with about a 3-month time lag dominated in most of the NCP, whereas positive temperature effects with a zero-month lag were most common in some of the southern parts. In comparison, total precipitation had a negative zero-month lag effect in the northern region, but two lagged months occurred in the south. Regarding the time lag effects, the explanation power of climatic factors improved relatively by up to 77%, and the explanation area increased by 41.20%. Additionally, change in winter wheat GUD was primarily determined by temperature rather than by TP, with a marked spatial heterogeneity of the Tmax and Tmin effect. Our results confirmed different time lag effects from different climatic factors on phenological processes in spring, and further suggested that both Tmax and Tmin should be considered to improve the performance of spring phenology models.

Highlights

The North China Plain (NCP) is one of the world’s major winter wheat production areas, accounting for approximately three quarters of the total wheat production in China, and about one eighth of the global wheat production [1,2,3]; it is of importance for both Chinese and global food security.the monsoon climate makes it sensitive to global climate change
Our results indicated that the explanation ability of the three climate factors on winter wheat green-up date (GUD) can be relatively improved by 0–77%, and that the explanation area could increase by 41.20% when the preseason climate effect was considered, which suggests that vegetation phenology response studies should be improved by taking into account of the time lag effects of climatic factors on vegetation
Our results showed that no significant trend in the green-up date averaged over NCP for the period of 2001–2015, but, interestingly, we found a strongly significant advance during the period of 2010–2015, at a rate of −2.82 days/year (P < 0.05), with a widespread advancing trend across the southwest to northeast part of the NCP (52.03% of all pixels)

Summary

Introduction

The North China Plain (NCP) is one of the world’s major winter wheat production areas, accounting for approximately three quarters of the total wheat production in China, and about one eighth of the global wheat production [1,2,3]; it is of importance for both Chinese and global food security.the monsoon climate makes it sensitive to global climate change. Zhang et al [7] noted that the mean annual temperature in some parts of the NCP had increased rapidly, by 0.57–0.44 ◦C/10a, from 1981–2011, higher than the corresponding national and global average warming [8]. Its annual precipitation exhibited larger variations over the years as a whole [9], its seasonal and regional reduction seemed to be significant [11,12]. These changing hydrothermal conditions have inevitable consequences on winter wheat growing activity [13,14,15]. Quantifying winter wheat growth dynamics and understanding the driving mechanisms behind these variations are scientific issues of great concern

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