土壤墒情遥感反演与旱情诊断

doi:10.11820/dlkxjz.2006.02.014

地理科学进展 ›› 2006, Vol. 25 ›› Issue (2): 123-130.doi: 10.11820/dlkxjz.2006.02.014

土壤墒情遥感反演与旱情诊断

李玉环^1,2, 王静¹, 曹银贵¹

恋乜辈夤婊?国土资源部土地利用重点实验室, 北京100035|2 山东农业大学资源与环境学院, 泰安 271018

收稿日期:2006-01-01 修回日期:2006-02-01 出版日期:2006-03-25 发布日期:2006-03-25
作者简介:李玉环(1965-),山东诸城人,博士,副教授,主要从事"3S"技术在土地资源与环境应用方面的教学与研究工作.
基金资助:
"国土资源部百名优秀青年科技人才计划"项目资助.

Retrieved Deduction of Soil Moisture Spatial Distribution and Drought Discrimination Based on Remote Sensing

LI Yuhuan1^1,2, 2¹, WANG Jing1¹

1. Key Laboratory of Land Use, Ministry of Land and Resources, China Land Surveying &|Planning Institute, Beijing,100035|
2. Shandong Agricultural University College of Resources and Environment, Taian 271018

Received:2006-01-01 Revised:2006-02-01 Online:2006-03-25 Published:2006-03-25

摘要/Abstract

摘要：

土壤墒情与植被生长状况和地表温度之间存在密切联系?贑OST模型算法和单窗算法，开展了TM/ETM+多光谱数据的地表反射率、地表温度(LST)和土壤调整植被指数反演(MSAVI)，分析了地表温度和植被指数的线性关系，提出了土壤墒情几何特征指数和旱情诊断函数，结合土壤含水量实测数据，建立了横山县土壤墒情遥感反演模型。实证结果表明，基于TM/ETM+数据反演的长度指数可进行旱情诊断;对土壤含水量的反演模型进行T检验，差异不显著，而基于地面温度的土壤墒情反演模型优于土壤调整植被指数反演模型。

关键词: 地表温度, 旱情诊断函数, 土壤调整植被指数, 土壤墒情

Abstract:

Soil moisture monitoring acts as an important role in reasonable water resource utilization and scientific management and decision-making of drought-fight. Soil moisture has relation with vegetation growth index(NDVI) and land surface temperature(LST). The NDVI was derived from the red and the NIR bands and the LST from the one or two thermal bands. The paper adopted LST retrieved from TM/ETM+ by mono-window algorithm and the modified soil adjustment vegetation index(MSAVI) based on ground soil spectrum line parameters and the earth reflection by COST model from the same satellite data. By analyzing the linear relation between LST and MSAVI, soil moisture indicators were put forward in terms of three geometrical expressions based on the two extreme points of the LST-MSAVI scatterplots, and drought discrimination function(DF) which was be used to discriminate the drought years or areas from the wet ones based on the DFij received either positive or negative values or the regress between the DFij values of the respective geometrical index and soil moisture component by Laboratory. a clear trend was exhibited between the drought-year cluster (negative values) and the wet-year cluster (positive values). The crossing point between the regression line and the DF= 0 line could be used to quantify the threshold between wet and drought regions in terms of soil moisture component. The retrieved model was built to illuminate soil moisture spatial distribution depending on linear regression analysis between soil moisture and LST or MSAVI. Results of the discrimination function for each of the drought indicators are presented. The “length” indicator is able to successfully separate the two drought years(1990, 2001) from the wet years(1991, 2002). The drought-land was discriminated by the DFj values as a function of the soil moisture of each region with DFj=0 and soil moisture=10~11%. Therefore the result showed that more information was mined by combining LST and MSAVI; length indicator could present valuable information for drought based on TM/ETM+; It wasn’t remarkable for the retrieved soil moisture to be tested by T-test; and the retrieved model from LST was better and more valuable than that from MSAVI.

Key words: drought discrimination function, LST, MSAVI, soil moisture

中图分类号:

P934

李玉环,王静,曹银贵. 土壤墒情遥感反演与旱情诊断[J]. 地理科学进展, 2006, 25(2): 123-130.

LI Yuhuan1,2, WANG Jing1, CAO Yingui1. Retrieved Deduction of Soil Moisture Spatial Distribution and Drought Discrimination Based on Remote Sensing[J]. PROGRESS IN GEOGRAPHY, 2006, 25(2): 123-130.

参考文献

[1] 邵晓梅等．土壤水分监测与模拟研究进展．地理科学进展， 2004， 23(3)：58～66.

[2] 张彩霞，杨勤科，李锐．基于DEM的地形湿度指数及其应用研究进展．地理科学进展， 2005， 14 (6)：118～125.

[3] Michael Tsehaye Wubet.Estimaton of Absolute Surface Temperature by Satellite Remote Sensing. International Institute for Geo-Information Science and Earth Observation. Enschede, The Netherlands， 2003，21~26.

[4] Peng Gong，Ruiliang Pu． Temperature from Remote Sensing Thermal Images． CAMFER Department of ESPM University of California, Berkeley，Berkeley， 2003，1~12.

[5] 齐述华，王长耀，牛铮，刘正军．利用NDVI时间序列数据分析植被长势对气候因子的响应．地理科学进展，2004， 23(3)：93~101.

[6] 田庆久，闵祥军．植被指数研究进展．地球科学进展， 1998， (4)：327～333.

[7] 李玉环，王静等．基于TM/ETM+遥感数据的地面相对反射率反演．山东农业大学学报(自然版)， 2005， 36(4)：545～551.

[8] Z Qin, A Karnieli， P Berliner． Remote sensing analysis of the land surface temperature anomaly in the sand-dune region across the Israel-Egypt borde． International Journal of Remote Sensing， 2002， 23(19)： 3991～ 4018.

[9] Z Qin．A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border．International journal of remote sensing， 2001，22(18)： 3719～3746.

[10] Huete A． A soil-adjusted vegetation index (SAVI)． Remote Sensing Environment， 1988，(25)：295～309.

[11] Qi J， Chehbouni A， Huete， A R， Kerr， Y H. and Sorooshian． A modified soil adjusted vegetation index． Remote Sensing Environment， 1994，(48)：119～126.

[12] Clevers， J G P W． The derivation of a simplified reflectance model for the estimation of leaf area index．Remote Sensing Environment，1988， (25)： 53～69.

[13] Lambin， E F Ehrlich． Land-cover changes in Sub-Saharan Africa． Journal Appl. Meteorology，1993， (30)：548～561.

[14] Nemani R R， Peirce L， Running S W, Goward　S． Developing satellite derived estimates of surface moisture status．Journal Appl. Meteorology，1997， (32)：562～579.

[15] Yuhuan LI， Jing Wang． Separability of Laboratory and Outdoor Measured Hyperspetral Soillines of Soil From Loess Plateau of China． Remote Sensing for Agricultrue,Ecosystems，and Hydrology Ⅵ，Proceedings of SPIE， 2004， (5568)：205～213.

土壤墒情遥感反演与旱情诊断

Retrieved Deduction of Soil Moisture Spatial Distribution and Drought Discrimination Based on Remote Sensing

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

Metrics

本文评价

推荐阅读 0

[1]	史潇, 王国杰, 孙明, 李玉涛, 王博妮, 沈婕. 高分辨红外辐射探测器地表温度数据在江苏地区1980—2009年间适用性评估[J]. 地理科学进展, 2020, 39(8): 1283-1295.
[2]	朱青,廖凯华,赖晓明,刘亚,吕立刚. 流域多尺度土壤水分监测与模拟研究进展[J]. 地理科学进展, 2019, 38(8): 1150-1158.
[3]	赵彩杉, 曾刚, 张丽娟, 张学珍. 基于Google Earth和MODIS陆地数据的农林地转换对地表温度的影响——以长江中下游及毗邻地区为例[J]. 地理科学进展, 2019, 38(5): 698-708.
[4]	高静, 龚健, 李靖业. “源-汇”景观格局的热岛效应研究——以武汉市为例[J]. 地理科学进展, 2019, 38(11): 1770-1782.
[5]	江颖慧, 焦利民, 张博恩. 城市地表温度与NDVI空间相关性的尺度效应[J]. 地理科学进展, 2018, 37(10): 1362-1370.
[6]	李斌, 王慧敏, 秦明周, 张鹏岩. NDVI、NDMI与地表温度关系的对比研究[J]. 地理科学进展, 2017, 36(5): 585-596.
[7]	李元征, 尹科, 周宏轩, 王晓琳, 胡聃. 基于遥感监测的城市热岛研究进展[J]. 地理科学进展, 2016, 35(9): 1062-1074.
[8]	周婷婷, 陈文惠. 基于MODIS数据和气象观测数据的气温空间插值方法比较[J]. 地理科学进展, 2011, 30(9): 1143-1151.
[9]	柯灵红, 王正兴, 宋春桥, 卢振权. 青藏高原东北部MODIS LST时间序列重建及与台站地温比较[J]. 地理科学进展, 2011, 30(7): 819-826.
[10]	谢苗苗, 王仰麟, 付梅臣. 城市地表温度热岛影响因素研究进展[J]. 地理科学进展, 2011, 30(1): 35-41.
[11]	张霄羽,毕于运,李召良. 遥感估算热惯量研究的回顾与展望[J]. 地理科学进展, 2008, 27(3): 166-172.
[12]	贾媛媛,李召良. 被动微波遥感数据反演地表温度研究进展[J]. 地理科学进展, 2006, 25(3): 96-105.