Spatial-temporal Variation Characteristics of Soil Moisture and Its Relationship with Meteorological Factors in Henan Province Based on TVDI
-
摘要: 河南是我国重要的粮食生产基地，但干旱频发，对社会经济发展产生巨大阻碍。土壤湿度作为反映干旱灾害发生的重要指标，开展土壤湿度动态变化及其与气象因子关系研究，可为科学认识干旱发生发展规律及制定调控措施提供依据。基于MODIS增强型植被指数EVI和校正后的陆地表面温度LST数据构建双抛物线型EVI-LST特征空间，根据该特征空间计算温度植被干旱指数TVDI，分析河南省2000-2019年土壤湿度的时空变化特征及其对气温降水的响应，结果表明：(1)TVDI与同期0～10 cm土壤相对湿度呈显著负相关（P<0.05），可以有效反映河南省土壤湿度情况，适用于该地区土壤湿度监测。(2)20年间河南省主要土壤湿度类型由湿润变为正常，转折点为2012年，干旱每年均有发生，三门峡、洛阳至平顶山的峡谷地区干旱发生频率最高。(3)土壤湿度分布有明显的空间分异性，呈现豫东平原、豫南山区和豫西伏牛山脉较湿润，豫中、豫北山区和豫西浅山丘陵区较干旱的分布特征。研究时段内干旱发生区域向东南扩张，土壤湿度整体呈缓慢变干趋势，湿润趋势和干旱趋势面积占比分别为29.71%和60.27%。(4)气象因子中降水增加引起土壤湿度升高，而气温增加则导致土壤湿度下降，二者中气温对土壤湿度的影响程度更大。Abstract: Henan is an important grain production base in China,but frequent drought has greatly hindered social and economic development. As an important index to reflect drought,the research on the dynamic change of soil moisture and its relationship with meteorological factors can provide a basis for scientifically understanding the occurrence and development law of drought and formulating control measures.Based on MODIS enhanced vegetation index（EVI）and corrected land surface temperature（LST）data to build a double parabola EVI-LST space,according to the characteristic space to calculate TVDI.（1）The TVDI shows a significant negative correlation with 0～10 cm soil relative humidity in the same period（P＜0.05）,which can effectively reflect the soil moisture in Henan Province and can be used for soil moisture monitoring in this area.（2） In the past 20 years,the main types of soil moisture in Henan Province changes from humid to normal,the turning point is 2012,and drought occurs every year,among which the drought occurs most frequently in the canyon areas from Sanmenxia,Luoyang to Pingdingshan.（3） The soil moisture has obvious regional differentiation in space,Eastern Henan Plains,Southern Henan Mountainous and Funiu Mountains in western Henan are relatively damp,while central and Northern Henan Mountains and Western Henan hills are arid. During the study period,the drought area expands to the southeast,and the soil moisture shows a slow drying trend as a whole. The areas with wetting trend and drought trend accounts for 29.71% and 60.27% respectively.（4） In meteorological factors,the increase in precipitation leads to the increase in soil moisture,while the increase in temperature leads to the decrease in soil moisture. In both,the influence of temperature on soil moisture is greater.
-
Keywords:
- soil moisture /
- spatial-temporal variation characteristics /
- TVDI /
- temperature /
- precipitation /
- Henan Province
-
-
[1] SENEVIRATNE S I,CORTI T,DAVIN E L,et al. Investigating soil moisture–climate interactions in a changing climate:A review[J].Earth Science Reviews,2010,99(3-4):125-161.
[2] 徐霞,孙文彬,王振.基于TVDI的布尔台矿区土壤湿度变化分析[J].矿业科学学报,2019,4(4):285-291. [3] 王娜,易桂花,张廷斌,等.基于TVDI的青藏高原腹地生长季土壤湿度时空变化及其气候响应[J].草业科学,2020,37(11):2 185-2 197. [4] 姚月锋,李莉.基于Landsat 8 TIRS数据与TVDI的流域地表土壤干旱分析[J].土壤通报,2019,50(2):282-289. [5] 温国涛,白建军,孙嵩松.基于时间序列遥感数据的陕西省2004-2014年干旱变化特征分析[J].干旱地区农业研究,2018,36(1):221-229. [6] 李谢辉,王磊.河南省历史水旱灾害危险性分析[J].资源科学,2012,34(8):1 526-1 532. [7] 朱业玉,潘攀,匡晓燕,等.河南省干旱灾害的变化特征和成因分析[J].中国农业气象,2011,32(2):311-316. [8] WASTON K,ROWAN L,OFFIELD T. Application of thermal modeling in the geologic interpretation of IR images[J]. Remote Sensing of Environment,1971,3:2 017-2 041.
[9] PRICE J C. On the analysis of thermal infrared imagery:the limited utility of apparent thermal inertia[J]. Remote Sensing of Environment,1985,18(1):59-73.
[10] IDSO S B,JACKSON R D,PINTER P J,et al. Normalizing the stress-degree-day parameter for environmental variability[J]. Agricultural Meteorology,1981,24(1):45-55.
[11] 陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱监测中的应用[J].遥感学报,1994,36(2):106-112. [12] KOGAN F N. Droughts of the Late 1980s in the United States as Derived from NOAA Polar-Orbiting Satellite Data[J]. Bulletin of the American Meteorological Society,1995,76(5):655-668.
[13] BHUIYAN C,SINGH R P,KOGAN F N. Monitoring drought dynamics in the Aravalli region(India)using different indices based on ground and remote sensing data[J].International Journal of Applied Earth Observation&Geoinformation,2006,8(4):289-302.
[14] 郑兴明,赵凯,李晓峰,等.利用微波遥感土壤水分产品监测东北地区春涝范围和程度[J].地理科学,2015,35(3):334-339. [15] SANDHOLT I,RASMUSSEN K. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment,2002,79(2-3):213-224.
[16] 宋扬,房世波,卫亚星.农业干旱遥感监测指数及其适用性研究进展[J].科技导报,2016,34(5):45-52. [17] 齐述华,王长耀,牛铮.利用温度植被旱情指数(TVDI)进行全国旱情监测研究[J].遥感学报,2003(5):420-427,436. [18] 刘英,马保东,吴立新,等.基于NDVI-ST双抛物线特征空间的冬小麦旱情遥感监测[J].农业机械学报,2012,43(5):55-63. [19] 卢远,华璀,韦燕飞.利用MODIS数据进行旱情动态监测研究[J].地理与地理信息科学,2007,23(3):55-58. [20] 闫娜,李登科,杜继稳,等.基于MODIS产品LST/NDVI/EVI的陕西旱情监测[J].自然灾害学报,2010,19(4):178-182. [21] 沙莎,王莺,王丽娟,等.土地利用数据在TVDI干旱监测方法中的应用研究:以甘肃省河东地区为例[J].水土保持研究,2017,24(5):375-381. [22] LIANG L,ZHAO S H,QIN Z H,et al. Drought change trend using MODIS TVDI and tts relationship with climate factors in China from2001 to 2010[J]. Journal of Integrative Agriculture,2014,13(7):1 501-1 508.
[23] 庞素菲,魏伟,郭泽呈,等.基于TVDI的甘肃省农业旱情特征及其影响因素[J].生态学杂志,2019,38(6):1 849-1 860. [24] 张文鸽,刘豪,殷会娟.基于改进TVDI指数的河南省干旱监测[J].人民黄河,2016,38(11):50-53,144. [25] 陈少丹,张利平,汤柔馨,等.基于SPEI和TVDI的河南省干旱时空变化分析[J].农业工程学报,2017,33(24):126-132. [26] 刘英,岳辉,李遥,等.基于MODIS的河南省春旱遥感监测[J].干旱地区农业研究,2018,36(3):218-223. [27] 杨肖杰,秦明周,张鹏岩,等.河南省耕地压力与经济发展耦合特征的时空分析[J].地域研究与开发,2020,39(3):144-149. [28] 冉琼,张增祥,张国平,等.温度植被干旱指数反演全国土壤湿度的DEM订正[J].中国水土保持科学,2005,3(2):32-36,50. [29] 何建村,白云岗,张严俊.基于MODIS数据新疆土壤干旱特征分析[J].干旱区地理,2015,38(4):735-742. [30] 汪左,王芳,张运.基于CWSI的安徽省干旱时空特征及影响因素分析[J].自然资源学报,2018,33(5):853-866. [31] 杨育丽,马明国,葛伟.夜间灯光影像探究北京市城市发展空间特征[J].遥感信息,2019,34(5):41-50. [32] 何月,樊高峰,张小伟,等.浙江省植被NDVI动态及其对气候的响应[J].生态学报,2012,32(14):4 352-4 362. [33] 徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,2002:37-70. [34] 薛天翼,白建军.基于TVDI和气象数据的陕西省春季旱情时空分析[J].水土保持研究,2017,24(4):240-246. [35] 刘馨,宋小宁,冷佩,等.基于MODIS数据的黄河源区土壤干湿状况时空格局变化[J].中国科学院大学学报,2019,36(2):178-187. [36] 程伟,辛晓平.基于TVDI的内蒙古草地干旱变化特征分析[J].中国农业科学,2020,53(13):2 728-2 742.
计量
- 文章访问数: 0
- HTML全文浏览量: 0
- PDF下载量: 0
下载:
