基于重构的Landsat 8时间序列数据和温度植被指数的区域旱情监测
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摘要
为了快速且准确地估算大区域范围内土壤水分信息,实现松嫩平原北部区域旱情的监测。基于Landsat 8时间序列数据,计算归一化植被指数(NDVI)和地表温度指数(LST)的时间序列数据,在此基础上利用Savitzky-Golay(S-G)滤波对所得时间序列数据进行了重构,弥补因受云和大气影响而产生的噪声。然后根据重构后的NDVI和LST数据,求得温度植被指数(TVDI);探讨TDVI和土壤湿度之间的关系,构建土壤湿度反演模型,并结合野外实测数据对模型精度进行了验证。结果表明:(1)S-G滤波可以有效地弥补因受云和大气影响而产生的不足,提高Landsat 8时间序列数据的质量;(2)温度植被干旱指数可以有效地反映土壤湿度状况,经过S-G滤波处理后的数据反演精度更高(RMSE=2.14%);(3)经过S-G滤波处理后的Landsat 8数据可以更为精确实现大区域范围内时间序列的旱情监测,为区域旱情的监测提供借鉴。
In order to estimate the range of large area quickly and accurately in the soil moisture information,realize the monitoring of regional drought in northern Nenjiang,based on the time series data of Landsat 8,we calculated the normalized difference vegetation index(NDVI)time series data and surface temperature index(LST)time series data.The Savitzky-Golay(S-G)filter was used to reconstruct the time series data to compensate for the noise caused by cloud and atmosphere.According to the reconstructed NDVI and LST data,the temperature vegetation index(TVDI)was calculated;on the basis of the relationship between TDVI and soil moisture,soil moisture inversion model was constructed.Finally,combining with the local field data verified the accuracy of the model of soil moisture.The results show that:(1)S-G filtering can effectively compensate for the defects caused by cloud,and improve the quality of Landsat 8 time series data;(2)the temperature vegetation drought index can reflect the soil moisture data effectively;after S-G filtering data,the inversion accuracy of soil moisture is higher(RMSE =2.43%);(3)drought monitoring after S-G filtering Landsat 8 time series data can be achieved within the study area,and provide reference for regional drought monitoring.
In order to estimate the range of large area quickly and accurately in the soil moisture information,realize the monitoring of regional drought in northern Nenjiang,based on the time series data of Landsat 8,we calculated the normalized difference vegetation index(NDVI)time series data and surface temperature index(LST)time series data.The Savitzky-Golay(S-G)filter was used to reconstruct the time series data to compensate for the noise caused by cloud and atmosphere.According to the reconstructed NDVI and LST data,the temperature vegetation index(TVDI)was calculated;on the basis of the relationship between TDVI and soil moisture,soil moisture inversion model was constructed.Finally,combining with the local field data verified the accuracy of the model of soil moisture.The results show that:(1)S-G filtering can effectively compensate for the defects caused by cloud,and improve the quality of Landsat 8 time series data;(2)the temperature vegetation drought index can reflect the soil moisture data effectively;after S-G filtering data,the inversion accuracy of soil moisture is higher(RMSE =2.43%);(3)drought monitoring after S-G filtering Landsat 8 time series data can be achieved within the study area,and provide reference for regional drought monitoring.
引文
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[8]鲍艳松,刘利,孔令寅,等.基于ASAR的冬小麦不同生育期土壤湿度反演[J].农业工程学报,2010,26(9):224-232.
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[11]于敏,王春丽.不同卫星遥感干旱指数在黑龙江的对比应用[J].应用气象学报,2011,22(2):221-231.
[12]姚春生,张增祥,汪潇.使用温度植被干旱指数法(TVDI)反演新疆土壤湿度[J].遥感技术与应用,2004,19(6):473-478.
[13]扎西央宗,杨秀海,边巴次仁,等.基于TVDI的西藏地区旱情遥感监测[J].气象科技,2010,38(4):495-499.
[14]王娇,丁建丽,袁泽,等.基于TS-NDVI特征空间的绿洲土壤水分监测算法改进[J].中国沙漠,2016,36(6):1606-1612.
[15]季国华,胡德勇,王兴玲,等.基于Landsat 8数据和温度—植被指数的干旱监测[J].自然灾害学报,2016,25(2):43-52.
[16]宋戈,王盼盼.松嫩高平原县域土地利用空间格局地形梯度特征分析:以巴彦县为例[J].地理科学,2017,37(8):1218-1225.
[17]卢洪健,莫兴国,孟德娟,等.气候变化背景下东北地区气象干旱的时空演变特征[J].地理科学,2015,35(8):1051-1059.
[18]黄志刚,肖烨,张国,等.气候变化背景下松嫩平原玉米灌溉需水量估算及预测[J].生态学报,2017,37(7):2368-2381.
[19]覃志豪,Zhang M H,Arnon K,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456-466.
[20]胡德勇,乔琨,王兴玲,等.单窗算法结合Landsat 8热红外数据反演地表温度[J].遥感学报,2015,19(6):964-976.
[21]Chen J,J9nsson P,Tamura M,et al.A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J].Remote Sensing of Environment,2004,91(3/4):332-344.
[22]黄耀欢,王建华,江东,等.利用S-G滤波进行MODISEVI时间序列数据重构[J].武汉大学学报:信息科学版,2009,34(12):1440-1443.
[23]J9nsson P,Eklundh L.TIMESAT-aprogram for analyzing time-series of satellite sensor data[J].Computers&Geosciences,2004,30(8):833-845.
[24]Olofsson P,Eklundh L,Lagergren F,et al.Estimating net primary production for scandinavian forests using data from Terra/MODIS[J].Advances in Space Research,2007,39(1):125-130.
[25]Eklundh L,Johansson T,Solberg S.Mapping insect defoliation in Scots pine with MODIS time-series data[J].Remote Sensing of Environment,2009,113(7):1566-1573.
[26]曹云峰,王正兴,邓芳萍.3种滤波算法对NDVI高质量数据保真性研究[J].遥感技术与应用,2010,25(1):118-125.
[27]宋春桥,游松财,柯林红,等.藏北地区3种时序NDVI重建方法与应用分析[J].地球信息科学学报,2011,13(1):133-143.
[28]Chen J,J9nsson P,Tamura M,et al.A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J].Remote Sensing of Environment,2004,91(3):332-344.
[29]黎治华.基于MODIS反演重构时间序列数据的长江三角洲地区生态环境演变研究[D].上海:华东师范大学,2011.
[30]李正国,王仰麟,吴健生,等.基于植被—温度特征的黄土高原地表水分季节变化[J].生态学报,2007,27(11):4563-4575.
[31]Gillies R R.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 Reviews,1994,9(1):161-173.
[32]Goetz S J.Multi-sensor analysis of NDVI,surface temperature and biophysical variables at a mixed grassland site[J].International Journal of Remote Sensing,1997,18(1):71-94.
[33]Gillies R R,Kustas W P,Humes K S.A verification of the‘triangle’method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index(NDVI)and surface[J].International Journal of Remote Sensing,1997,18(15):3145-3166.
[34]Price J C.Using spatial context in satellite data to infer regional scale evapotranspiration[J].Ieee Transactions on Geoscience&Remote Sensing,1990,28(5):940-948.
[35]Carlson T N,Capehart W J,Gillies R R.A new look at the simplified method for remote sensing of daily evapotranspiration[J].Remote Sensing of Environment,2012,54(2):161-167.
[36]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.
[2]吴春雷,秦其明,李梅,等.基于光谱特征空间的农田植被区土壤湿度遥感监测[J].农业工程学报,2014,30(16):106-112.
[3]何连,秦其明,任华忠,等.利用多时相Sentinel-1SAR数据反演农田地表土壤水分[J].农业工程学报,2016,32(3):142-147.
[4]陈仲新,任建强,唐华俊,等.农业遥感研究应用进展与展望[J].遥感学报,2016,20(5):748-767.
[5]鲍艳松,严婧,闵锦忠,等.基于温度植被干旱指数的江苏淮北地区农业旱情监测[J].农业工程学报,2014,30(7):163-172.
[6]D′Urso G,minacapilli M.A semi-empirical approach for surface soil water content estimation from radar data without a-priori information on surface roughness[J].Journal of Hydrology,2006,321(1):297-310.
[7]Cashion J,Lakshmi V,Bosch D,et al.Microwave remote sensing of soil moisture:evaluation of the TRMM microwave imager(TMI)satellite for the Little River Watershed Tifton,Georgia[J].Journal of Hydrology,2005,307(1):242-253.
[8]鲍艳松,刘利,孔令寅,等.基于ASAR的冬小麦不同生育期土壤湿度反演[J].农业工程学报,2010,26(9):224-232.
[9]吴黎,张有智,解文欢,等.土壤水分的遥感监测方法概述[J].国土资源遥感,2014,26(2):19-26.
[10]王家强,梁继业,李志军,等.利用植被指数—地表温度特征空间反演干旱区土壤干湿状况[J].土壤通报,2014,45(1):39-46.
[11]于敏,王春丽.不同卫星遥感干旱指数在黑龙江的对比应用[J].应用气象学报,2011,22(2):221-231.
[12]姚春生,张增祥,汪潇.使用温度植被干旱指数法(TVDI)反演新疆土壤湿度[J].遥感技术与应用,2004,19(6):473-478.
[13]扎西央宗,杨秀海,边巴次仁,等.基于TVDI的西藏地区旱情遥感监测[J].气象科技,2010,38(4):495-499.
[14]王娇,丁建丽,袁泽,等.基于TS-NDVI特征空间的绿洲土壤水分监测算法改进[J].中国沙漠,2016,36(6):1606-1612.
[15]季国华,胡德勇,王兴玲,等.基于Landsat 8数据和温度—植被指数的干旱监测[J].自然灾害学报,2016,25(2):43-52.
[16]宋戈,王盼盼.松嫩高平原县域土地利用空间格局地形梯度特征分析:以巴彦县为例[J].地理科学,2017,37(8):1218-1225.
[17]卢洪健,莫兴国,孟德娟,等.气候变化背景下东北地区气象干旱的时空演变特征[J].地理科学,2015,35(8):1051-1059.
[18]黄志刚,肖烨,张国,等.气候变化背景下松嫩平原玉米灌溉需水量估算及预测[J].生态学报,2017,37(7):2368-2381.
[19]覃志豪,Zhang M H,Arnon K,等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456-466.
[20]胡德勇,乔琨,王兴玲,等.单窗算法结合Landsat 8热红外数据反演地表温度[J].遥感学报,2015,19(6):964-976.
[21]Chen J,J9nsson P,Tamura M,et al.A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J].Remote Sensing of Environment,2004,91(3/4):332-344.
[22]黄耀欢,王建华,江东,等.利用S-G滤波进行MODISEVI时间序列数据重构[J].武汉大学学报:信息科学版,2009,34(12):1440-1443.
[23]J9nsson P,Eklundh L.TIMESAT-aprogram for analyzing time-series of satellite sensor data[J].Computers&Geosciences,2004,30(8):833-845.
[24]Olofsson P,Eklundh L,Lagergren F,et al.Estimating net primary production for scandinavian forests using data from Terra/MODIS[J].Advances in Space Research,2007,39(1):125-130.
[25]Eklundh L,Johansson T,Solberg S.Mapping insect defoliation in Scots pine with MODIS time-series data[J].Remote Sensing of Environment,2009,113(7):1566-1573.
[26]曹云峰,王正兴,邓芳萍.3种滤波算法对NDVI高质量数据保真性研究[J].遥感技术与应用,2010,25(1):118-125.
[27]宋春桥,游松财,柯林红,等.藏北地区3种时序NDVI重建方法与应用分析[J].地球信息科学学报,2011,13(1):133-143.
[28]Chen J,J9nsson P,Tamura M,et al.A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter[J].Remote Sensing of Environment,2004,91(3):332-344.
[29]黎治华.基于MODIS反演重构时间序列数据的长江三角洲地区生态环境演变研究[D].上海:华东师范大学,2011.
[30]李正国,王仰麟,吴健生,等.基于植被—温度特征的黄土高原地表水分季节变化[J].生态学报,2007,27(11):4563-4575.
[31]Gillies R R.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 Reviews,1994,9(1):161-173.
[32]Goetz S J.Multi-sensor analysis of NDVI,surface temperature and biophysical variables at a mixed grassland site[J].International Journal of Remote Sensing,1997,18(1):71-94.
[33]Gillies R R,Kustas W P,Humes K S.A verification of the‘triangle’method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index(NDVI)and surface[J].International Journal of Remote Sensing,1997,18(15):3145-3166.
[34]Price J C.Using spatial context in satellite data to infer regional scale evapotranspiration[J].Ieee Transactions on Geoscience&Remote Sensing,1990,28(5):940-948.
[35]Carlson T N,Capehart W J,Gillies R R.A new look at the simplified method for remote sensing of daily evapotranspiration[J].Remote Sensing of Environment,2012,54(2):161-167.
[36]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.