干旱区生态环境知识资源中心(Arid): PHYSICAL MODELS FOR SOIL SALINITY MAPPING OVER ARID LANDSCAPE USING LANDSAT-OLI AND FIELD DATA: VALIDATION AND COMPARISON

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	PHYSICAL MODELS FOR SOIL SALINITY MAPPING OVER ARID LANDSCAPE USING LANDSAT-OLI AND FIELD DATA: VALIDATION AND COMPARISON
	Al-Ali, Z.; Bannari, A.; Hameid, N.; El-Battay, A.
通讯作者	Bannari, A (corresponding author), Arabian Gulf Univ, Coll Grad Studies, Dept Nat Resources & Environm, POB 26671, Manama, Bahrain. ; Bannari, A (corresponding author), Arabian Gulf Univ, Coll Grad Studies, Dept Geoinformat, POB 26671, Manama, Bahrain.
会议名称	IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
会议日期	JUL 28-AUG 02, 2019
会议地点	Yokohama, JAPAN
英文摘要	The aim of the present study is focusing on a validation and comparison among eight different physical models for soil salinity mapping in arid landscape. The considered models were developed for different geographic regions around the world, i.e. Latino-America (Mexico), Middle-East (Iraq), north and east Africa (Morocco and Ethiopia) and Asia (China). These models integrated different spectral bands and unlike mathematical functions in their conceptualization (stepwise, linear, second order, logarithmic, and exponential). Three main steps were considered. The Landsat-OLI image data was radiometrically standardized and the models were implemented to derive soil salinity maps. The field survey was organized during 4 days, two days before the OLI data acquisition, and a total of 100 soil samples were collected representing different salinity levels, and each sampling location was geographically localized using accurate GPS. The laboratory analysis was accomplished to derive electrical conductivity (EC-Lab) for validation purposes. Statistical analysis (p < 0.05) was applied between predicted salinity maps (EC-Predicted) and the measured ground truth (EC-Lab). The results obtained showed that predictive models based on VNIR bands and vegetation indices are inadequate for soil salinity prediction due to a serious signals confusion between the salt-crust and the soil optical properties in these spectral bands. The statistical tests revealed insignificant fits (R-2 <= 0.41) with a very high prediction errors (RMSE >= 0.65). While, the model based on second order polynomial function and integrating the SWIR bands provides results of best fitness in comparison to the ground truth, yielding an R-2 of 0.97 and low overall RMSE of 13%.
英文关键词	Soil Salinity Physical models Validation Electrical conductivity Remote sensing Landsat-OLI
来源出版物	2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)
ISSN	2153-6996
出版年	2019
页码	7081-7084
ISBN	978-1-5386-9154-0
出版者	IEEE
类型	Proceedings Paper
语种	英语
收录类别	CPCI-S
WOS记录号	WOS:000519270606157
WOS关键词	VEGETATION
WOS类目	Geosciences, Multidisciplinary ; Remote Sensing
WOS研究方向	Geology ; Remote Sensing
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/370117
作者单位	[Al-Ali, Z.; Bannari, A.] Arabian Gulf Univ, Coll Grad Studies, Dept Nat Resources & Environm, POB 26671, Manama, Bahrain; [Bannari, A.; Hameid, N.] Arabian Gulf Univ, Coll Grad Studies, Dept Geoinformat, POB 26671, Manama, Bahrain; [El-Battay, A.] ICBA, Dubai, U Arab Emirates
推荐引用方式 GB/T 7714	Al-Ali, Z.,Bannari, A.,Hameid, N.,et al. PHYSICAL MODELS FOR SOIL SALINITY MAPPING OVER ARID LANDSCAPE USING LANDSAT-OLI AND FIELD DATA: VALIDATION AND COMPARISON[C]:IEEE,2019:7081-7084.