干旱区生态环境知识资源中心(Arid): Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR

Arid

DOI	10.1016/j.jaridenv.2021.104599
	Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR_SMNRT2) Near Real Time product
	Gomez, Diego; Salvador, Pablo; Sanz, Julia; Rodrigo, Juan Fernando; Gil, Jorge; Casanova, Jose Luis
通讯作者	Gomez, D (corresponding author), Univ Valladolid, Remote Sensing Lab LATUV, Paseo Belen 11, Valladolid 47011, Spain.
来源期刊	JOURNAL OF ARID ENVIRONMENTS
ISSN	0140-1963
EISSN	1095-922X
出版年	2021
卷号	194
英文摘要	Despite satellite imagery is being used to identify suitable areas for desert locust, there is a lack of automatized and operational procedures in Near Real Time (NRT). The aim of this study was to assess the capacity of Soil Moisture Near Real Time Neural Network Level 2 product (MIR_SMNRT2) from the Soil Moisture and Ocean Salinity satellite (SMOS) to predict nymphs of desert locust. We used soil moisture time series (between 2016 and 2019) to build 6 machine learning models (logistic regression model glm, eXtreme Gradient Boosting xgbTree, Weighted k-Nearest Neighbors kknn, Feed-Forward Neural Networks and Multinomial Log-Linear Models nnet, support vector machine radial svmRadial, and random forest rf) over the entire recession area. Model results proved that spatial and/or temporal constraints in data sampling conditioned the predictive capacity of the selected machine learning algorithms. Furthermore, we used a forward selection procedure to evaluate the impact that time series data exert on modelling. Our results suggest that soil moisture data retrieved between 95 and 12 days (before the sighting) provided sufficient information to achieve acceptable predictive performances. This methodology can improve current preventive and control operations, it is site-specific, and could be used to other pests.
英文关键词	Forecast tool Pests Remote sensing Soil moisture
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000691565800006
WOS关键词	SOIL-MOISTURE ; EARTH OBSERVATION ; HABITAT
WOS类目	Ecology ; Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363752
作者单位	[Gomez, Diego; Salvador, Pablo; Sanz, Julia; Rodrigo, Juan Fernando; Gil, Jorge; Casanova, Jose Luis] Univ Valladolid, Remote Sensing Lab LATUV, Paseo Belen 11, Valladolid 47011, Spain
推荐引用方式 GB/T 7714	Gomez, Diego,Salvador, Pablo,Sanz, Julia,et al. Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR_SMNRT2) Near Real Time product[J],2021,194.
APA	Gomez, Diego,Salvador, Pablo,Sanz, Julia,Rodrigo, Juan Fernando,Gil, Jorge,&Casanova, Jose Luis.(2021).Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR_SMNRT2) Near Real Time product.JOURNAL OF ARID ENVIRONMENTS,194.
MLA	Gomez, Diego,et al."Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR_SMNRT2) Near Real Time product".JOURNAL OF ARID ENVIRONMENTS 194(2021).