VIIRS-TVDI法反演干旱区农田土壤湿度

摘要/Abstract

摘要： 通过在国内首次使用MODIS/AVHRR的后继VIIRS产品，依其每日地表反射率产品（GIGTO-VI1～5BO）计算NDVI，结合每日陆表温度产品（GMTCO-VLSTO），计算新疆农七师125团2015年6月29日～7月10日的TVDI结果，利用同期地面实测土壤含水率进行验证，R²=0.846，两者之间不相关的双尾检验值>0.001。结果表明：构建的土壤湿度模型对农田土壤含水率有较好的反应，并将该模型推广到全天山北坡经济带地区。利用VIIRS数据构建的TVDI特征空间表现优秀，拟合结果良好；该数据因其自身幅宽、高时间分辨率等优势，应用于新疆农田土壤湿度监测，具有一定优势，可弥补VIIRS产品在我国该领域使用的空白。

关键词: 土壤含水量, VIIRS, 温度植被干旱指数, 新疆

Abstract: Soil moisture plays a significant role in agricultural monitoring and yield estimation. Monitoring soil moisture by remote sensing features such merits as fast effectiveness,strong dynamic contrast and large monitoring areas. Meanwhile,TVDI integrates the reaction of both vegetation growth state and land surface temperature to soil moisture,thus satisfying the demand of this study. The VIIRS,sensor carried by Suomi NPP,as the successor of MODIS/AVHRR,has not been used in soil moisture monitoring in domestic yet. VIIRS sensor data EDR daily surface reflectance product(GIGTO-VI1~5BO) is studied in this paper and the normalized differential vegetation index(NDVI) of the Regiment 125 of the Agricultural Division 7,the Xinjiang Production and Construction Corps,at Kuitun City,Xinjiang,China from June 29 to July 10,2015 is calculated. In addition,temperature vegetation dryness index(TVDI) is optimized and dry and wet edges are extracted to form characteristic space based on the EDR daily land surface temperature product(GMTCO-VLSTO) so as to calculate TVDI results. In the TVDI feature space,when NDVI is less than 0.1,TVDI cannot response soil moisture condition with no vegetation coverage;0.1 < NDVI < 0.7,respectively,Ts_max and Ts_min value decrease and increase with the increase of the vegetation coverage;0.7<NDVI,the sensitivity of TVDI is reduced,owning to the saturated NDVI. The measured ground soil moisture content data in the same period are adopted for verification and this model is promoted to the economic belt regions in the whole north slope of Tianshan Mountains. The study results show as follows:(1) The correlation index R² between TVDI result and measured ground data is 0.846;the uncorrelated two-tailed test value between them is smaller than 0.001. The soil moisture model built has sound reaction to water content in unsaturated farmland soil;(2) The characteristic space built shows excellent performance and the dry and wet edges are superior in conforming to theoretical triangular space distribution with better fitting result compared with the previously studied AVHRR/MODIS data;(3) The large breadth and fine-time resolution ratio of VIIRS data further improves timeliness of data processing in large area monitoring;only with 1-4 images can all areas in Xinjiang be completely covered for daily monitoring. This paper pioneers in China in the application of VIIRS sensor data product to realize soil moisture monitoring. This sensor,as an upgrade of AVHRR/MODIS sensor,has been optimized in performance,wave band setting and product format,thus rendering it with better performance. MODIS sensor is designed to serve until 2020s,thus the status of the VIIRS products will rise in the future. Therefore,the monitoring on soil water content in Xinjiang fields could be further strengthened with the application of VIIRS-TVDI to monitor soil moisture and is of certain practical value,which will fill a vacancy in the use of VIIRS data.

Key words: soil moisture, VIIRS, TVDI, Xinjiang

中图分类号:

TP79

武彬, 张清, 李希灿, 田野, 郑玉彬. VIIRS-TVDI法反演干旱区农田土壤湿度[J]. 干旱区地理, 2016, 39(4): 861-867.

WU Bin, ZHANG Qing, LI Xi-can, TIAN Ye, ZHENG Yu-bin. Evaluating soil moisture of prime farmland using the VIIRS-TVDI in arid Land[J]. , 2016, 39(4): 861-867.

参考文献

[1] 杨忠娜,唐继军,喻晓玲. 新疆棉花产业对国民经济的影响及对策研究[J]. 农业现代化研究,2013,34(3):298-302.[YANG Zhongna,TANG Jijun,YU Xiaoling. Xinjiang cotton industry present situation and countermeasure research[J]. Research of Agricultural Modernization,2013,34(3):298-302.]

[2] 农业部新疆农业发展专题研究课题组. 新疆农业发展问题研究(上)[J]. 中国农业资源与区划,2008,29(1):1-6.[Xinjiang Agricultural Development Special Research Task Group of the Ministry of Agriculture. Studies on the issue of agriculture development in Xinjiang[J]. Chinese Journal of Agricultural Resources and Regional Planning,2008,29(1):1-6.]

[3] 姜逢清,胡汝骥,马虹. 新疆气候与环境的过去、现在及未来情景[J]. 干旱区地理,1998,21(1):1-9.[JIANG Fengqing,HU Ruji,MA Hong. Studies in past climate and its possible trend in Xinjiang[J]. Arid Land Geography,1998,21(1):1-9.]

[4] 胡红武,胡梅,龙玲,等. 区域干旱遥感监测研究综述[J]. 安徽农业科学,2008,36(36):14817-14819.[HU Hongwu,HU Mei, LONG Ling,et al. Research on drought monitoring by remote sensing in attachment[J]. Journal of Anhui Agri,2008,36(36):14817-14819.]

[5] 刘兴文,冯勇进. 应用热惯量编制土壤水分图及土壤水分探测效果[J]. 土壤学报,1987,24(3):272-280.[LIU Xingwen, FENG Yongjin. Compilation of soil moisture map by means of soil thermal inertia image[J]. Acta Pedologica Sinica,1987,24(3):272-280.]

[6] 王艳姣,闫峰. 旱情监测中高植被覆盖区热惯量模型的应用[J]. 干旱区地理,2014,37(3):539-547.[WANG Yanjiao,YAN Feng. Application of thermal inertia model in high vegetation coverage area for drought monitoring[J]. Arid Land Geography, 2014,37(3):539-547.]

[7] 吴黎,张有智,解文欢,等. 改进的表观热惯量法反演土壤含水量[J]. 国土资源遥感,2013,25(1):44-49.[WU Li,ZHANG Youzhi,XIE Wenhuan,et al. The inversion of soil water content by the improved apparent thermal inertia[J]. Remote Sensing for Land and Resources,2013,25(1):44-49.]

[8] IDSO S B,CLAWSON K L,ANDERSON M G. Foliage temperature:effects of environmental factors with implication for plant water stress assessment and CO₂ effects of climate[J]. Water Resource Research,1986,22:1702-1716.

[9] JACKSON R D,IDSO S B,REGINATO R J,et al. Canopy temperature as a crop water stress indicator[J].Water Resources Research, 1981,17:1133-1138.

[10] 何广均,冯学智,肖鹏峰,等. 一种基于植被指数-地表温度特征空间的蒸散指数[J]. 干旱区地理,2015,38(5):887-899.[HE Guangjun,FENG Xuezhi,XIAO Pengfeng,et al. An evapotranspiration index from Ts-VI feature space[J]. Arid Land Geography, 2015,38(5):887-899.

[11] PRICE J C. Using spatial context in satellite data to infer regional scale evapotranspiration[J]. IEEE,Transactions on Geoscience and Remote Sensing,1990,28:940-948.

[12] CARLSON T N,GILLIES R R,PERRY E M. A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover[J]. Remote Sensing Review,1994,52:45-59.

[13] MORAN M S,CLARKE T R,INOUE Y,et al. Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index[J]. Remote Sensing of Environment, 1994,49:246-263.

[14] 王鹏新,龚健雅,李小文. 条件植被温度指数及其在干旱监测中的应用[J]. 武汉大学学报:信息科学版,2001(5):412-418.[WANG Pengxin,GONG Jianya,LI Xiaowen. Vegetation-temperature condition index and its application for drought monitoring[J]. Geomatics and Information Science of Wuhan University, 2001(5):412-418.]

[15] 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,2002,79:213-224.

[16] 姚春生,张增祥,汪潇. 使用温度植被干旱指数法反演新疆土壤湿度[J]. 遥感技术与应用,2004,19(6):473-478.[YAO Chunsheng,ZHANG Zengxiang,WANG Xiao. Evaluating soil moisture status in Xinjiang using the temperature vegetation dryness index(TVDI)[J]. Remote Sensing Technology and Application, 2004,19(6):473-478.]

[17] 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,2013,35(5):930-937.[CHEN Bin,ZHANG Xuexia,HUA Kai,et al. Application study of temperature vegetation drought index(TVDI) in grassland drought monitoring[J]. Arid Land Geography,2013,35(5):930-937.]

[18] 刘玉琴,沙晋明,王德生. 基于TVDI的福州地区土壤干湿状况遥感监测[J]. 亚热带资源与环境学报,2013,8(4):75-80.[LIU Yuqin,SHA Jinming,WANG Desheng. Spatial pattern of soil moisture in Fuzhou:an investigation based on TVDI method[J]. Journal of Subtropical Resources and Environment,2013,8(4):75-80.]

[19] 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):420-428.[QI Shuhua,WANG Changyao,NIU Zheng. Evaluating soil moisture status in China using the temperature/vegetation dryness index (TVDI)[J]. Journal of Remote Sensing,2003,7(5):420-428.]

[20] 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.