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REMOTE SENSING FOR LAND & RESOURCES    2011, Vol. 23 Issue (2) : 104-109     DOI: 10.6046/gtzyyg.2011.02.19
Technology Application |

Retrieving Soil Moisture of Shiyang River Basin by ATI and TVI Based on EOS/MODIS Data

WEI Wei 1, REN Hao-chen 1, ZHAO Jun 1, WANG Xu-feng 2
1.College of Geographical and Environment Science, Northwest Normal University, Lanzhou 730070, China; 2.Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou  730000,  China
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Abstract  

This paper intends to examine spatial distribution of soil moisture in Shiyang River basin in eastern Hexi corridor, an important arid ecosystem in Northwest China. The MODIS data MOD11A2, MOD13A1 and MCD43A3 were used to compute such parameters as NDVI, ATI and TVI required for the study. An applicable approach from ATI and TVI has been proposed to estimate the soil moisture in the basin, using NDVI as a threshold for each pixel. The approach was validated with the field data. The distribution pattern of the soil moisture in Shiyang River basin was further analyzed using DEM data. The results show that the compound ATI and TVI models can effectively improve retrieval accuracy and remedy the shortage of one-sided method. The results were acceptable through a comparison with the field data. The study reveals that in Shiyang River basin, the soil moisture is degressive from the upstream area to downstream area, and it is obviously lower in the sloping land of foothills, the edge of oasis and desert area. Relatively, the soil moisture has observably changed in the upstream and downstream areas and marginal areas of oasis. In Shiyang River basin, the soil moisture distribution shows significant differentiation with the change of elevation, slope and other terrain factors. In Shiyang River basin, the drought is mainly distributed in farmland and grassland, and the drought extent of farmland is in different degrees of seriousness in different landuse types. 

Keywords Quickbird      SPOT      Mine      Ecological environment     
: 

TP 79

 
Issue Date: 17 June 2011
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Cite this article:   
WEI Wei, REN Hao-Chen, ZHAO Jun, WANG Xu-Feng.
Retrieving Soil Moisture of Shiyang River Basin by ATI and TVI Based on EOS/MODIS Data[J]. REMOTE SENSING FOR LAND & RESOURCES,2011, 23(2): 104-109.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.02.19     OR     https://www.gtzyyg.com/EN/Y2011/V23/I2/104

[1]Gert A S,Edwin T E.水文与水管理中的遥感技术[M].韩敏,译.北京:水利水电出版社,2006:187-188.

[2]刘志明,张柏,晏明,等.土壤水分与干旱遥感研究的进展与趋势[J].地球科学进展,2003,18(4):576-583.

[3]Watson K,Phon H A.Thermal Inertia Mapping from Satellites Discrimination of Geologic Units in Oman [J].Jres Geol Suvr,1974,2(2):147-158.

[4]Bijleveld R A.‘Tell Us’A Combined Surface Temperature,Soil Moisture and Evaporation Mapping Approach [R].Paper Presented at 12th Int Symp on Rem Sens of Env,Mnaila,Philippines,1978:20-26.

[5]Price J C.On the Analysis of Thermal Infrared Imagery,The Limited Utility of Apparent Thermal Inertia[J].Remote Sens Environ,1985,18:59-73.

[6]Carlson T N.Regional-Scale Estimates of Surface Luoisture Availability and Thermal Inertia Using Remote Thermal Measurements[J].Remote Sens Environ,1986(1):197-247.

[7]隋洪智,田国良,李付琴.农田蒸散双层模型及其在干旱遥感监测中的应用[J].遥感学报,1997,1(3):220-224.

[8]金一愕,刘长盛,张文忠.利用气象卫星GMS和AVHRR资料推算地面水分含量的方法[J].应用气象学报,1998,9(2):197-204.

[9]魏振超,何离庆,等.基于GIS的土壤信息系统[J].重庆大学学报,2003,26(6):39-41.

[10]王晓云,郭文利,等.利用“3S”技术进行北京地区土壤水分监测应用技术研究[J].应用气象学报,2002,13(4):442-429.

[11]乔平林,张继贤,林宗坚.石羊河流域水质环境遥感监测评价研究[J].国土资源遥感,2003(4):39-45.

[12]陈权,李震,王磊.环境小卫星S波段SAR监测土壤水分变化应用分析[J].国土资源遥感,2005(2):12-15.

[13]肖乾广,陈维英,盛永伟,等.用气象卫星监测土壤水分的试验研究[J].应用气象学报,1994,5(3):312-318.

[14]宋承运,邓孺孺,王中挺.基于植被-土壤二向反射模型的土壤含水量遥感[J].国土资源遥感,2006(3):29-32.

[15]Liang S L.Narrowband to Broadband Conversions of Land Surface Albedo Ⅱ:Algorithms[J].Remote Sensing of Environment,2000(76):213-238.

[16]Henricksen B L.Reflections on Drought-Ethiopia 1983-1984[J].International Journal of Remote Sensing,1986,7(11):1447-1451.

[17]赵立军.基于MODIS数据的北京地区土壤含水量遥感信息模型研究[D].北京:中国农业大学,2004.

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