干旱区生态环境知识资源中心(Arid): Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands

Arid

DOI	10.3390/rs16111838
	Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands
	Miao, Lijuan; Zhang, Yuyang; Agathokleous, Evgenios; Bao, Gang; Zhu, Ziyu; Liu, Qiang
通讯作者	Liu, Q
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2024
卷号	16 期号:11
英文摘要	Climate warming hampers grassland growth, particularly in dryland regions. To preserve robust grassland growth and ensure the resilience of grassland in these arid areas, a comprehensive understanding of the interactions between vegetation and climate is imperative. However, existing studies often analyze climate-vegetation interactions using concurrent vegetation indices and meteorological data, neglecting time-lagged influences from various determinants. To address this void, we employed the random forest machine learning method to predict the grassland NDVI (Normalized Difference Vegetation Index) in Asian drylands (including five central Asia countries, the Republic of Mongolia, and Parts of China) from 2001 to 2020, incorporating time-lag influences. We evaluated the prediction model's performance using three indexes, namely the coefficient of determination (R-2), root-mean-square error (RMSE), and Mean Absolute Error (MAE). The results underscore the superiority of the model incorporating time-lag influences, demonstrating its enhanced capability to capture the grassland NDVI in Asian drylands (R-2 >= 0.915, RMSE <= 0.033, MAE <= 0.019). Conversely, the model without time-lag influences exhibited relatively poor performance, notably inferior to the time-lag-inclusive model. The latter result aligns closely with remote sensing observations and more accurately reproduces the spatial distributions of the grassland NDVI in Asian drylands. Over the study period, the grassland NDVI in Asian drylands exhibited a weak decreasing trend, primarily concentrated in the western region. Notably, key factors influencing the grassland NDVI included the average grassland NDVI in the previous month, total precipitation in the current month, and average soil moisture in the previous month. This study not only pioneers a novel approach to predicting grassland growth but also contributes valuable insights for formulating sustainable strategies to preserve the integrity of grassland ecosystems.
英文关键词	grassland NDVI time lag random forests prediction Asian drylands
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001246877400001
WOS关键词	CLIMATE-CHANGE ; SOIL-MOISTURE ; VEGETATION ; TEMPERATURE ; PREDICTION ; PATTERNS ; PLATEAU ; NDVI
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405299
推荐引用方式 GB/T 7714	Miao, Lijuan,Zhang, Yuyang,Agathokleous, Evgenios,et al. Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands[J],2024,16(11).
APA	Miao, Lijuan,Zhang, Yuyang,Agathokleous, Evgenios,Bao, Gang,Zhu, Ziyu,&Liu, Qiang.(2024).Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands.REMOTE SENSING,16(11).
MLA	Miao, Lijuan,et al."Modeling with Hysteresis Better Captures Grassland Growth in Asian Drylands".REMOTE SENSING 16.11(2024).