干旱区生态环境知识资源中心(Arid): Soil moisture retrieval from Sentinel-1 acquisitions in an arid environment in Tunisia: application of Artificial Neural Networks techniques

Arid

DOI	10.1080/01431161.2019.1629503
	Soil moisture retrieval from Sentinel-1 acquisitions in an arid environment in Tunisia: application of Artificial Neural Networks techniques
	Hachani, A.1; Ouessar, M.1; Paloscia, S.2; Santi, E.2; Pettinato, S.2
通讯作者	Paloscia, S.
会议名称	6th Small Unmanned Aerial Systems for Environmental Research (UAS4Enviro)
会议日期	JUN 27-29, 2018
会议地点	Univ Split, Fac Elect Engn Mech Engn & Naval Architecture, Split, CROATIA
英文摘要	In this paper, an approach for estimating the soil moisture content (SMC) in arid environment in Tunisia is presented. In countries characterized by arid and semi-arid climate, it is very important to obtain reliable estimates of soil moisture evolution for water management purposes, in order to reduce water wastes and properly schedule agricultural practices. On the other hand, the retrieval of SMC is often hampered by the small humidity range (below 10%). A retrieval algorithm aiming at estimating the soil moisture and based on artificial neural networks (ANN) has therefore been implemented, using the data collected by the Synthetic Aperture Radar (SAR) sensor of Sentinel-1. By taking advantage of the fast computation and high retrieval accuracy, ANN are able to generate reliable output maps of SMC starting from the complex SAR images and using little auxiliary information, as Digital Elevation Models, Local Incidence angle, Normalized Difference Vegetation Index (NDVI), and so on. The peculiar strategy adopted for the training, which has been obtained by combining satellite measurements with data simulated by electromagnetic model (based on the Integral Equation Model, IEM), made this algorithm robust and almost site independent. The obtained results demonstrated that ANN represent a powerful tool for estimating SMC, provided that they have been trained with consistent datasets, made up by both experimental and theoretical data. The relationship of the algorithm validation between the estimated and measured SMC showed Pearson's correlation coefficient, r = 0.77, and RMSE = 1.84%, in spite of the very low SMC values found on the area.
来源出版物	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2019
卷号	40
期号	24
页码	9159-9180
出版者	TAYLOR & FRANCIS LTD
类型	Article; Proceedings Paper
语种	英语
国家	Tunisia;Italy
收录类别	CPCI-S
WOS记录号	WOS:000475168600001
WOS关键词	MICROWAVE ; MODEL ; BACKSCATTERING ; ALGORITHM ; IMAGES ; SMAP
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/308156
作者单位	1.IRA, Lab Team Eremol & Combating Desertificat, Medenine, Tunisia; 2.Natl Res Council IFAC CNR, Earth Observat Dept, Inst Appl Phys, Florence, Italy
推荐引用方式 GB/T 7714	Hachani, A.,Ouessar, M.,Paloscia, S.,et al. Soil moisture retrieval from Sentinel-1 acquisitions in an arid environment in Tunisia: application of Artificial Neural Networks techniques[C]:TAYLOR & FRANCIS LTD,2019:9159-9180.