干旱区生态环境知识资源中心(Arid): Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan

Arid

DOI	10.3390/rs11172040
	Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan
	Rahman, Khalil Ur 1; Shang, Songhao 1; Shahid, Muhammad 1,2; Wen, Yeqiang 1
通讯作者	Shang, Songhao
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:17
英文摘要	Accurate estimation of precipitation from satellite precipitation products (PPs) over the complex topography and diverse climate of Pakistan with limited rain gauges (RGs) is an arduous task. In the current study, we assessed the performance of two PPs estimated from soil moisture (SM) using the SM2RAIN algorithm, SM2RAIN-CCI and SM2RAIN-ASCAT, on the daily scale across Pakistan during the periods 2000-2015 and 2007-2015, respectively. Several statistical metrics, i.e., Bias, unbiased root mean square error (ubRMSE), Theil's U, and the modified Kling-Gupta efficiency (KGE) score, and four categorical metrics, i.e., probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Bias score, were used to evaluate these two PPs against 102 RGs observations across four distinct climate regions, i.e., glacial, humid, arid and hyper-arid regions. Total mean square error (MSE) is decomposed into systematic (MSEs) and random (MSEr) error components. Moreover, the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TRMM TMPA 3B42v7) was used to assess the performance of SM2RAIN-based products at 0.25 degrees scale during 2007-2015. Results shows that SM2RAIN-based product highly underestimated precipitation in north-east and hydraulically developed areas of the humid region. Maximum underestimation for SM2RAIN-CCI and SM2RIAN-ASCAT were 58.04% and 42.36%, respectively. Precipitation was also underestimated in mountainous areas of glacial and humid regions with maximum underestimations of 43.16% and 34.60% for SM2RAIN-CCI. Precipitation was overestimated along the coast of Arabian Sea in the hyper-arid region with maximum overestimations for SM2RAIN-CCI (SM2RAIN-ASCAT) of 59.59% (52.35%). Higher ubRMSE was observed in the vicinity of hydraulically developed areas. Theil's U depicted higher accuracy in the arid region with values of 0.23 (SM2RAIN-CCI) and 0.15 (SM2RAIN-ASCAT). Systematic error components have larger contribution than random error components. Overall, SM2RAIN-ASCAT dominates SM2RAIN-CCI across all climate regions, with average percentage improvements in bias (27.01% in humid, 5.94% in arid, and 6.05% in hyper-arid), ubRMSE (19.61% in humid, 20.16% in arid, and 25.56% in hyper-arid), Theil's U (9.80% in humid, 28.80% in arid, and 26.83% in hyper-arid), MSEs (24.55% in humid, 13.83% in arid, and 8.22% in hyper-arid), MSEr (19.41% in humid, 29.20% in arid, and 24.14% in hyper-arid) and KGE score (5.26% in humid, 28.12% in arid, and 24.72% in hyper-arid). Higher uncertainties were depicted in heavy and intense precipitation seasons, i.e., monsoon and pre-monsoon. Average values of statistical metrics during monsoon season for SM2RAIN-CCI (SM2RAIN-ASCAT) were 20.90% (17.82%), 10.52 mm/day (8.61 mm/day), 0.47 (0.43), and 0.47 (0.55) for bias, ubRMSE, Theil's U, and KGE score, respectively. TMPA outperformed SM2RAIN-based products across all climate regions. SM2RAIN-based datasets are recommended for agricultural water management, irrigation scheduling, flood simulation and early flood warning system (EFWS), drought monitoring, groundwater modeling, and rainwater harvesting, and vegetation and crop monitoring in plain areas of the arid region.
英文关键词	precipitation estimation soil moisture SM2RAIN-CCI SM2RAIN-ASCAT multi-satellite precipitation analysis (TMPA) error decomposition complex topography diverse climate
类型	Article
语种	英语
国家	Peoples R China ; Pakistan
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000486874300084
WOS关键词	SATELLITE SOIL-MOISTURE ; RAINFALL ESTIMATION ; GLOBAL RAINFALL ; VALIDATION ; MICROWAVE ; VERIFICATION ; DROUGHT ; SENSORS ; MODELS ; TMPA
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
来源机构	清华大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218413
作者单位	1.Tsinghua Univ, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China; 2.Natl Univ Sci & Technol Islamabad, SCEE, NICE, Islamabad 44000, Pakistan
推荐引用方式 GB/T 7714	Rahman, Khalil Ur,Shang, Songhao,Shahid, Muhammad,et al. Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan[J]. 清华大学,2019,11(17).
APA	Rahman, Khalil Ur,Shang, Songhao,Shahid, Muhammad,&Wen, Yeqiang.(2019).Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan.REMOTE SENSING,11(17).
MLA	Rahman, Khalil Ur,et al."Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan".REMOTE SENSING 11.17(2019).