基于表观热惯量与温度植被指数的FY-3B土壤水分降尺度研究

doi:10.6046/gtzyyg.2020244

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摘要

为进一步研究风云三号(FY-3B)土壤水分降尺度获取高分辨率土壤水分的方法,使其更适用于农业、水文、生态等区域尺度的应用要求,以MODIS为数据源,青藏高原那曲地区为研究区,利用表观热惯量模型(apparent thermal inertia,ATI)与温度植被指数(temperature vegetation index, TVI)模型在不同植被覆盖度下适用的特点,构建综合ATI与TVI的土壤水分反演模型; 结合低分辨率FY-3B土壤水分产品,利用土壤水分降尺度方法,获取高分辨率下土壤水分反演模型系数,并得到高分辨率土壤水分。通过与地面观测数据对比,降尺度后土壤水分与实测数据的R²在0.4以上,RMSE在0.055~0.103 cm³/cm³之间,表明降尺度后的土壤水分能够较好地反映区域土壤水分的空间分布与变化。

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	宋承运
	胡光成
	王艳丽
	汤超

关键词 ：降尺度, FY-3B土壤水分, 表观热惯量, 温度植被指数

Abstract：

In order to further study the method of obtaining high-resolution soil moisture by downscaling FY-3B soil moisture and make it more suitable for agricultural and hydrological simulation, the authors constructed a comprehensive ATI and TVI by using MODIS data in Naqu area. Combined with low resolution FY-3B soil moisture products, the coefficients of soil moisture inversion model under high resolution were obtained by using soil moisture downscaling method, and the high-resolution soil moisture was obtained. Compared with the ground observation data, the R² of the downscaling soil moisture and the measured data is above 0.4, and the RMSE is between 0.055 and 0.103 cm³/cm³, indicating that the downscaling soil moisture can better reflect the spatial distribution and change of soil moisture.

Key words： downscaling FY-3B soil moisture apparent thermal inertia temperature vegetation index

收稿日期: 2020-08-07 出版日期: 2021-07-21

ZTFLH:

TP79

基金资助:安徽省高等学校自然科学研究项目“不同植被覆盖度下风云三号土壤水分产品降尺度方法研究”(KJ2017A072);国家重点研发计划“主要粮食作物气象灾害监测技术体系研发”(2017YFD0300402);国家自然科学基金项目“基于全球涡动相关数据及地表蒸散发模型的微波遥感土壤湿度融合研究”(41701495)

作者简介: 宋承运(1981-),男,博士,讲师,主要从事定量遥感、环境遥感研究。Email: schyun007@163.com。

引用本文:

宋承运, 胡光成, 王艳丽, 汤超. 基于表观热惯量与温度植被指数的FY-3B土壤水分降尺度研究[J]. 国土资源遥感, 2021, 33(2): 20-26.
SONG Chengyun, HU Guangcheng, WANG Yanli, TANG Chao. Downscaling FY-3B soil moisture based on apparent thermal inertia and temperature vegetation index. Remote Sensing for Land & Resources, 2021, 33(2): 20-26.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2020244 或 https://www.gtzyyg.com/CN/Y2021/V33/I2/20

Fig.1 研究区高程及地面观测站点示意图

Fig.2 技术路线

Tab.1 模型(式(7))回归分析相关系数R与模型系数

Fig.3 相关系数时间序列图

Fig.4 FY-3B土壤水分与降尺度后土壤水分空间分布图

Fig.5 降尺度土壤水分与地面观测值散点图

Fig.6 FY-3B土壤水分与地面观测值时间序列

[1]	Chen C F, Son N T, Chang L Y, et al. Monitoring of soil moisture variability in relation to rice cropping systems in the Vietnamese Mekong Delta using MODIS data[J]. Applied Geography, 2011, 31(2):463-475. doi: 10.1016/j.apgeog.2010.10.002
[2]	Njoku E G, Jackson T J, Lakshmi V, et al. Soil moisture retrieval from AMSR-E[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(2):215-229. doi: 10.1109/TGRS.2002.808243
[3]	Merlin O, Walker J P, Chehbouni A, et al. Towards deterministic downscaling of SMOS soil moisture using MODIS derived soil evaporative efficiency[J]. Remote Sensing of Environment, 2008, 112(10):3935-3946. doi: 10.1016/j.rse.2008.06.012
[4]	Narayan U, Lakshmi V, Njoku E G. Retrieval of soil moisture from passive and active L/S band sensor (PALS) observations during the Soil Moisture Experiment in 2002 (SMEX02)[J]. Remote Sensing of Environment, 2004, 92(4):483-496. doi: 10.1016/j.rse.2004.05.018
[5]	Zhao W, Li A. A comparison study on empirical microwave soil moisture downscaling methods based on the integration of microwave-optical/IR data on the Tibetan Plateau[J]. International Journal of Remote Sensing, 2015, 36(19-20):4986-5002. doi: 10.1080/01431161.2015.1041178
[6]	Chauhan N S, Miller S, Ardanuy P. Spaceborne soil moisture estimation at high resolution:A microwave-optical/IR synergistic approach[J]. International Journal of Remote Sensing, 2003, 24(22):4599-4622. doi: 10.1080/0143116031000156837
[7]	Piles M, Camps A, Mercè V, et al. Downscaling SMOS-derived soil moisture using MODIS visible/infrared data[J]. IEEE Transactions on Geoence and Remote Sensing, 2011, 49:3156-3166.
[8]	曹永攀, 晋锐, 韩旭军, 等. 基于MODIS和AMSR-E遥感数据的土壤水分降尺度研究[J]. 遥感技术与应用, 2011, 26(5):590-597.
	Cao Y P, Jin R, Han X J, et al. A downscaling method for AMSR-E soil moisture using MODIS derived dryness index[J]. Remote Sensing Technology and Application, 2011, 26(5):590-597.
[9]	孟祥金, 毛克彪, 孟飞, 等. 基于空间权重分解的降尺度土壤水分产品的中国土壤水分时空格局研究[J]. 高技术通讯, 2019, 29(4):104-114.
	Meng X J, Mao K B, Meng F, et al. Temporal and spatial patterns of soil moisture in China based on spatial weight decomposition and downscaling soil moisture products[J]. High Technology Letters, 2019, 29(4):104-114.
[10]	Song C Y, Jia L. A method for downscaling FengYun-3B soil moisture based on apparent thermal inertia[J]. Remote Sensing, 2016, 8(9):1-16. doi: 10.3390/rs8010001
[11]	Kim S, Balakrishnan K, Liu Y, et al. Spatial disaggregation of coarse soil moisture data by using high-resolution remotely sensed vegetation products[J]. IEEE Geoence & Remote Sensing Letters, 2017, 14(9):1604-1608.
[12]	赵英时. 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2003.
	Zhao Y S. Analysis principle and method of remote sensing applications[M]. Beijing: Science Press, 2003.
[13]	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, 26(5):412-418.
	Wang P X, Gong J Y, Li X W. Vegetation-temperature condition index and its application for drought monitoring[J]. Geomatics and Information Science of Wuhan University, 2001, 26(5):412-418.
[14]	Yang K, Qin J, Zhao L, et al. A multiscale soil moisture and freeze-thaw monitoring network on the Third Pole[J]. Bulletin of the American Meteorological Society, 2013, 94(12):1907-1916. doi: 10.1175/BAMS-D-12-00203.1
[15]	Shi J C, Jiang L M, Zhang L, et al. Physically based estimation of bare-surface soil moisture with the passive radiometers[J]. IEEE Transactions on Geoscience & Remote Sensing, 2006, 44:3145-3153.
[16]	Zhang N, Shi J, Sun G, et al. A simple algorithm for retrieval of the optical thickness at L-band from SMOS data[C]// IEEE International Geoscience & Remote Sensing Symposium.IEEE, 2012:198-210.
[17]	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. doi: 10.1016/0034-4257(85)90038-0
[18]	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.doi: 10.6046/gtzyyg.2013.01.08. doi: 10.6046/gtzyyg.2013.01.08
	Wu L, Zhang Y Z, Xie W H, 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.doi: 10.6046/gtzyyg.2013.01.08. doi: 10.6046/gtzyyg.2013.01.08
[19]	魏伟, 任皓晨, 赵军, 等. 基于MODIS的ATI和TVI组合法反演石羊河流域土壤含水量[J]. 国土资源遥感, 2011, 23(2):104-109.doi: 10.6046/gtzyyg.2011.02.19. doi: 10.6046/gtzyyg.2011.02.19
	Wei W, Ren H C, Zhao J, et al. Retrieving soil moisture of shiyang river basin by ATI and TVI based on EOS /MODIS data[J]. Remote Sensing for Land and Resources, 2011, 23(2):104-109.doi: 10.6046/gtzyyg.2011.02.19. doi: 10.6046/gtzyyg.2011.02.19
[20]	张文, 任燕, 马晓琳, 等. 基于综合干旱指数的淮河流域土壤含水量反演[J]. 国土资源遥感, 2018, 30(2):73-79.doi: 10.6046/gtzyyg.2018.02.10. doi: 10.6046/gtzyyg.2018.02.10
	Zhang W, Ren Y, Ma X L, et al. Estimation of soil moisture with drought indices in Huaihe river basin of east China[J]. Remote Sensing for Land and Resources, 2018, 30(2):73-79.doi: 10.6046/gtzyyg.2018.02.10. doi: 10.6046/gtzyyg.2018.02.10
[21]	Carlson T N, Ripley D A. On the relation between NDVI,fractional vegetation cover,and leaf area index[J]. Remote Sensing of Environment, 1997, 62(3):241-252. doi: 10.1016/S0034-4257(97)00104-1
[22]	Cui Y, Chen X, Xiong W, et al. A soil moisture spatial and temporal resolution improving algorithm based on multi-source remote sensing data and GRNN model[J]. Remote Sensing, 2020, 12(45):1-13. doi: 10.3390/rs12010001
[23]	Zhang Y, Chen Y, Li J, et al. A simple method for converting 1-km resolution daily clear-sky LST into real LST[J]. Remote Sensing, 2020, 12(10):1-23. doi: 10.3390/rs12010001

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