PDF Download

[1]CHEN Mingxing,ZHANG Yuhu.Retrieval of Soil Moisture in Sanjiang Plain Based on TVDI Model with Four Vegetation Indices[J].Research of Soil and Water Conservation,2019,26(03):93-100,107.

Copy

Retrieval of Soil Moisture in Sanjiang Plain Based on TVDI Model with Four Vegetation Indices

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 26 Number of periods: 2019 03 Page number: 93-100,107 Column: Public date: 2019-04-12

Title:: Retrieval of Soil Moisture in Sanjiang Plain Based on TVDI Model with Four Vegetation Indices

Author(s):: CHEN Mingxing, ZHANG Yuhu; College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China

Keywords:: TVDI; vegetation Index; soil moisture; precipitation; Sanjiang Plain

CLC:: TP79

DOI:: -

Abstract:: The use of remote sensing to monitor soil moisture is conducive to the analysis of soil moisture conditions in large-scale areas. Comparing and analyzing the accuracy of the Temperature Vegetation Dryness Index (TVDI) calculated under different vegetation indices can increase the practical value of TVDI on retrieval of soil moisture. There are four vegetation indices, including NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), MSAVI (Modified Soil Adjusted Vegetation Index), RVI(Ratio Vegetation Index), were respectively used to construct LST-Ⅵ feature space and calculate TVDI by combining with land surface temperature data. The results indicated that:(1) all the TVDIs calculated by different vegetation indices presented the negative correlations with soil moisture data, that is, the higher the TVDI value, the lower the soil moisture value; (2) the values of R² of regression analysis between soil moisture data and TVDI based on different vegetation indices were similar in May, June and September, and they were all suitable for retrieving soil moisture in these three periods; in July, compared to the TVDI results calculated by NDVI and RVI (R² is around 0.15), TVDI based on EVI and MSAVI (R² is around 0.35) was more suitable for retrieving soil moisture in this period; (3) from May to September, the drought mainly occurred in the central and southwest parts of the Sanjiang Plain, and the degree of drought was mainly mild drought. The eastern and northeastern parts remained basically normal or slightly humid in different periods.

References:: [1] 郭广猛,赵冰茹.使用MODIS数据监测土壤湿度[J].土壤,2004,36(2):219-221.
[2] Martínez-Fernández J, González-Zamora A, Sánchez N, et al. Satellite soil moisture for agricultural drought monitoring:Assessment of the SMOS derived Soil Water Deficit Index[J]. Remote Sensing of Environment, 2016,177:277-286.
[3] 安雪丽,武建军,周洪奎,等.土壤相对湿度在东北地区农业干旱监测中的适用性分析[J].地理研究,2017,35(5):837-849.
[4] Carrão H, Russo S, Sepulcre-Canto G, et al. An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,48:74-84.
[5] 张喆,丁建丽,李鑫,等. TVDI用于干旱区农业旱情监测的适宜性[J].中国沙漠,2015,35(1):220-227.
[6] 于健,杨国范,王颖,等.基于MODIS数据反演阜新地区土壤水分的研究[J].遥感技术与应用,2011,26(4):413-419.
[7] Qin J, Yang K, Lu N, et al. Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia[J]. Remote Sensing of Environment, 2013,138:1-9.
[8] Holzman M E, Rivas R, Piccolo M C. Estimating soil moisture and the relationship with crop yield using surface temperature and vegetation index[J]. International Journal of Applied Earth Observation and Geoinformation, 2014,28:181-192.
[9] 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.
[10] Zhou L, Zhang J, Wu J, et al. Comparison of remotely sensed and meteorological data-derived drought indices in mid-eastern China[J]. International Journal of Remote Sensing, 2012,33(6):1755-1779.
[11] 倪波顺,高阳华,李月臣,等.基于FY-3A/VIRR数据的重庆市干旱监测研究[J].西南大学学报:自然科学版,2014,36(7):143-148.
[12] 高炜,安如,王喆.基于微波遥感技术的干旱监测指数及其应用研究:以三江源区为例[J].干旱区研究,2017,34(3):541-550.
[13] 黄对,王文.基于粗糙度定标的IEM模型的土壤含水率反演[J].农业工程学报,2014,30(19):182-190.
[14] 王学,刘全明,屈忠义,等.盐渍化土壤水分微波雷达反演与验证[J].农业工程学报,2017,33(11):108-114.
[15] 闫峰,覃志豪,李茂松,等.农业旱灾监测中土壤水分遥感反演研究进展[J].自然灾害学报,2006,16(6):114-121.
[16] 郭茜,李国春.用表观热惯量法计算土壤含水量探讨[J].中国农业气象,2005,26(4):13-17.
[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,2003,7(5):420-427.
[18] 曹雷,丁建丽,牛增懿.基于TVDI的艾比湖地区土壤水分时空变化分析[J].水土保持研究,2016,23(3):43-47.
[19] Chen J, Wang C, Jiang H, et al. Estimating soil moisture using Temperature-Vegetation Dryness Index(TVDI)in the Huang huai-hai(HHH)plain[J]. International Journal of Remote Sensing, 2011,32(4):1165-1177.
[20] Li Z, Wang Y, Zhou Q, et al. Spatiotemporal variability of land surface moisture based on vegetation and temperature characteristics in Northern Shaanxi Loess Plateau, China[J]. Journal of Arid Environments, 2008,72(6):974-985.
[21] Son N T, Chen C F, Chen C R, et al. Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data[J]. International Journal of Applied Earth Observation and Geoinformation, 2012,18:417-427.
[22] Patel N R, Anapashsha R, Kumar S, et al. Assessing potential of MODIS derived temperature/vegetation condition index(TVDI)to infer soil moisture status[J]. International Journal of Remote Sensing, 2009,30(1):23-39.
[23] Sandholt I, Rasmussen K, Andersen J. A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J]. Remote Sensing of Environment, 2002,79(2):213-224.
[24] 杨玲,杨艳昭.基于TVDI的西辽河流域土壤湿度时空格局及其影响因素[J].干旱区资源与环境,2016,30(2):76-81.
[25] 朱明宇.基于MODIS数据对辽宁地区农业干旱的监测研究[D].沈阳:沈阳农业大学,2017.
[26] 李海霞,杨井,陈亚宁,等.基于MODIS数据的新疆地区土壤湿度反演[J].草业学报,2017,26(6):16-27.
[27] 胡屾,刘骏,毛学刚.中国东北地区2007—2010年植被覆盖度及其变化[J].东北林业大学学报,2017,45(7):45-50.
[28] 李新辉,宋小宁,周霞.半干旱区土壤湿度遥感监测方法研究[J].地理与地理信息科学,2010,26(1):90-93.
[29] 陈斌,张学霞,华开,等.温度植被干旱指数(TVDI)在草原干旱监测中的应用研究[J].干旱区地理,2013,36(5):930-937.
[30] 刘立文,张吴平,段永红,等. TVDI模型的农业旱情时空变化遥感应用[J].生态学报,2014,34(13):3704-3711.
[31] 王丹,南瑞,韩俊杰,等.黑龙江省土壤湿度及其对气温和降水的敏感性分析[J].气象与环境学报,2012,28(2):49-53.
[32] Liyang L, Jishan L, Xiuzhi C, et al. The Microwave Temperature Vegetation Drought Index(MTVDI)based on AMSR-E brightness temperatures for long-term drought assessment across China(2003—2010)[J]. Remote Sensing of Environment, 2017,199:302-320.
[33] 薛飞.利用风云卫星数据的东北地区土地覆盖监测研究[D].成都:电子科技大学,2017.
[34] 孙舒婷.大兴安岭森林地表温度的遥感估算及分析研究[D].哈尔滨:东北林业大学,2015.

Similar References:

Memo

Last Update: 1900-01-01