Using MODIS LAI Data to Monitor Spatio-Temporal Changes of Winter Wheat Phenology in Response to Climate Warming

Abstract

Understanding spatio-temporal changes in winter wheat (Triticum aestivum L) phenology and its response to temperature will be vital for adapting to climate change in the coming years. For this purpose, the heading date (HD), maturity date (MD), and length of the reproductive growth period (LRGP) were detected from the remotely sensed leaf area index (LAI) data by a threshold-based method during the harvest year 2003 to 2018 across the North China Plain. The results show that there was high spatial heterogeneity of winter wheat phenology in pixel scale across the whole area, which could not be detected in previous site-based studies. The results also verified that climate warming could explain part of the change in the HD. However, for the LRGP, the potential impact of non-climate effects should be further investigated. This study presents the spatio-temporal changes both in winter wheat phenology and corresponding mean temperature and then analyzes their relationships in pixel scale. Additionally, this study further discusses the potential impact of non-climate effects on the LRGP.

Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain

“…Furthermore, the combination of climatic (i.e. temperature, growing degree days) and satellite data has resulted in an adequate approach for estimating phenology, as previous studies have suggested [58,59], and wheat grain yield.…”

“…We argue that the results here obtained showed that focusing on phenology for grain yield estimation is now possible using Sentinel-2 imagery and not only applicable with ground and proximal sensing. Furthermore, the combination of climatic (i.e., temperature, growing degree days) and satellite data has resulted in an adequate approach for estimating phenology, as previous studies have suggested [58,59], and wheat grain yield.…”

Prototyping of LAI and FPAR Retrievals From GOES-16 Advanced Baseline Imager Data Using Global Optimizing Algorithm

“…eaf area index (LAI) [1] and the fraction of photosynthetically active radiation (FPAR) (0.4-0.7 μm) absorbed by vegetation [2], characterizing structure and functioning and energy absorption capacity of vegetation canopy, are two important parameters of many ecological, agronomic, climate and land surface process models [3][4][5].…”

“…These products have been widely used to monitor vegetation phenology, capture impacts of climate change and natural disaster etc. [5,11,12]. However, because of the long revisit time and cloud contamination, all of these products from low earth orbiting (LEO) sensors observations are often so temporally and spatially incomplete that the data usability is greatly reduced [13].…”

Validation and inter-comparison of the FY-3B/MERSI LAI product with GLOBMAP and MYD15A2H

“…Leaf area index (LAI) is defined as one half the total green leaf area per unit horizontal ground surface area (Chen and Black 1992), is one of the basic climate variables defined by the Global Climate Observing System (GCOS). LAI determines the effective cross-section of the interaction between the earth and atmosphere and is an important biophysical parameter in vegetation growth monitoring (Jin et al 2016;Fang et al 2019b;Song et al 2020), global climate change (Martin et al 2016;Tao, Chen, and Fu 2020) and land surface process models (Chaney, Metcalfe, and Wood 2016;Xie et al 2019). In order to run largescale ecosystem models, we need regional and even global LAI data (Myneni et al 2002).…”