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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (4) : 156-160     DOI: 10.6046/gtzyyg.2017.04.23
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Retrieval of precipitation for grassland based on the multi-temporal Sentinel-1 SAR data
ZHANG Zhaoying1, LU Yicen2, WU Guozhou3, WANG Yongli3
1. Xilingol Meteorological Bureau, Xilinhot 026000, China;
2. Zhejiang Meteorological Bureau, Hangzhou 310002, China;
3. Ecological and Agricultural Meteorology Center of Inner Mongolia, Hohhot 010051, China
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Abstract  Water resource is indispensable to the growth of meadows in prairie, and the precise acquisition of the amount of precipitation is of great significance to the continental ecosystem and hydrological circle. This study proposed a novel technique to retrieve the precipitation on the basis of multi-temporal SAR imagery based on the fact that variations of the dielectric constant give rise to modifications of the soil moisture before and after the process of precipitation, allowing for the immediate changes in soil backscattering coefficients. Sentinel-1A SAR and actual measurements of rainfall in the meteorological stations of Erlianhot and Xilinhot were used to verify the retrieval results, which indicated the superb exponential regressive model between the difference values of backscattering coefficient before and after the process of precipitation and the real precipitation measurements. Thus, the spatial distribution of the retrieved precipitation on September 21, 2015 was obtained in the use of this method with Erlianhot as an example, meanwhile, the result shows the favorable spatial distribution consistency of the derived one in comparison with the product of MODIS atmospheric precipitable water considering the atmospheric motion and the acquisition time of imagery. Therefore, this pragmatic method is contrived to yield the actual rainfall spatial distribution in low vegetation coverage regions.
Keywords moment matching      GF-2      streaking noise      destriping model     
:  TP79  
Issue Date: 04 December 2017
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CUI Jian
SHI Penghui
BAI Weiming
LIU Xiaojing
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CUI Jian,SHI Penghui,BAI Weiming, et al. Retrieval of precipitation for grassland based on the multi-temporal Sentinel-1 SAR data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 156-160.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.04.23     OR     https://www.gtzyyg.com/EN/Y2017/V29/I4/156
[1] 尹燕亭,侯向阳,运向军.气候变化对内蒙古草原生态系统影响的研究进展[J].草业科学,2011,28(6):1132-1139.
Yin Y T,Hou X Y,Yun X J.Advances in the climate change influencing grassland ecosystems in Inner Mongolia[J].Pratacultural Science,2011,28(6):1132-1139.
[2] 廖彬武.基于雷达资料降水量的反演[J].河南科技,2016(1):151-152.
Liao B W.Retrieval of precipitation based on Radar data[J].Journal of Henan Science and Technology,2016(1):151-152.
[3] Adler R F,Negri A J.A satellite infrared technique to estimate tropical convective and stratiform rainfall[J].Journal of Applied Meteorology,1988,27(1):30-51.
[4] 何 连,秦其明,任华忠,等.利用多时相Sentinel-1 SAR数据反演农田地表土壤水分[J].农业工程学报,2016,32(3):142-148.
He L,Qin Q M,Ren H Z,et al.Soil moisture retrieval using multi-temporal Sentinel-1 SAR data in agricultural areas[J].Transaction of the Chinese Society of Agricultural Engineer,2016,32(3):142-148.
[5] 曾玲方,李 霖,万丽华.基于Sentinel-1卫星SAR数据的洪水淹没范围快速提取[J].地理信息世界,2015,22(5):100-103,107.
Zeng L F,Li L,Wan L H.SAR-based fast flood mapping using Sentinel-1 imagery[J].Geomatics World,2015,22(5):100-103,107.
[6] 李新武,郭华东,李 震,等.重复轨道SIR-C极化干涉SAR数据植被覆盖区土壤水分反演研究[J].遥感学报,2009,13(3):423-436.
Li X W,Guo H D,Li Z,et al.The inversion method study of sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data[J].Journal of Remote Sensing,2009,13(3):423-436.
[7] 胡 蝶,郭 铌,沙 莎,等.Radarsat-2/SAR和MODIS数据联合反演黄土高原地区植被覆盖下土壤水分研究[J].遥感技术与应用,2015,30(5):860-867.
Hu D,Guo N,Sha S,et al.Soil moisture retrieved using Radarsat-2/SAR and MODIS remote sensing data in vegetated areas of Loess Plateau[J].Remote Sensing Technology and Application,2015,30(5):860-867.
[8] 姜立鹏,覃志豪,谢 雯.针对MODIS近红外数据反演大气水汽含量研究[J].国土资源遥感,2006,18(3):5-9.doi:10.6046/gtzyyg.2006.03.02.
Jiang L P,Qin Z H,Xie W.Retrieving atmospheric water vapor from MODIS near infrared data[J].Remote Sensing for Land and Resources,2006,18(3):5-9.doi:10.6046/gtzyyg.2006.03.02.
[9] 程红芳,章文波,陈 锋.植被覆盖度遥感估算方法研究进展[J].国土资源遥感,2008,20(1):13-18.doi:10.6046/gtzyyg.2008.01.02.
Cheng H F,Zhang W B,Chen F.Advances in researches on application of remote sensing method to estimating vegetation coverage[J].Remote Sensing for Land and Resources,2008,20(1):13-18.doi:10.6046/gtzyyg.2008.01.02.
[10] 徐 爽,沈润平,杨晓月.利用不同植被指数估算植被覆盖度的比较研究[J].国土资源遥感,2012,24(4):95-100.doi:10.6046/gtzyyg.2012.04.16.
Xu S,Shen R P,Yang X Y.A comparative study of different vegetation indices for estimating vegetation coverage based on the dimidiate pixel model[J].Remote Sensing for Land and Resources,2012,24(4):95-100.doi:10.6046/gtzyyg.2012.04.16.
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