基于修正NDVI时间序列的大区域冬小麦全生育期墒情监测
|
摘要
针对遥感墒情监测存在的时空分辨率矛盾,提出利用高时间分辨率的共享遥感数据,将修正归一化植被指数(NDVI)作为墒情指示因子,实现对大区域冬小麦全生育期墒情准实时、半定量化监测的思想。首先,对冬小麦种植区域进行地域划分,依据冬小麦的物候特征进行生育期阶段划分;其次,利用遥感数据计算各地域、各阶段的NDVI,针对各阶段冬小麦的生长情况,进行NDVI的自适应修正。经实测墒情数据和修正NDVI相关分析表明,修正NDVI可以作为农田墒情的指示因子;最后,将监测年的修正NDVI与前三年平均值做距平差值分析,得到与前三年平均值相比,冬小麦墒情的时空分布规律,结果与实际观测值相符。可以预见:如果有较长的时间序列积累,可以得到监测年与常年相比,麦田的墒情时空分布规律。研究结果表明:提出的方法,可以用于大区域冬小麦全生育期墒情的半定量化准实时监测,而且遥感数据容易获得,基本不需要实测数据,计算方便,成本低廉。对冬小麦农田管理与决策具有重要的参考价值。
Aiming at the contradiction between temporal and spatial resolution of soil moisture monitoring,using the modified normalized vegetation index(NDVI) as the indicator of soil moisture,this paper puts forward a new method of real-time and semi-quantitative monitoring of soil moisture during winter wheat growth period by using the shared meteorological remote sensing data with high temporal resolution.First of all,the winter wheat planting area was divided into regions,and the growth stages were also divided according to its phenological characteristics;Secondly,NDVI was calculated by using meteorological remote sensing data,and the NDVI was adjusted adaptively in each stage and region.The correlation analysis between measured soil moisture data and modified NDVI showed that NDVI could be used as an indicator of soil moisture.Finally,the temporal and spatial distribution of soil moisture of winter wheat,through compared with those of previous three year averages,was obtained by analyzing the difference between the modified NDVI and the annual mean value,and the result was in agreement with the actual observations.It is foreseeable that if there is a longer time series to accumulate,the temporal and spatial distribution of soil moisture in the wheat field can be obtained through compared with the perennial year.The results show that the method proposed in this paper can be used to semi-quantify and quasi-real-time monitoring of soil moisture in winter wheat in the whole growth period.Moreover,remote sensing data are easy to obtain,almost no need of measured data,convenient calculation and low cost.So,it has important reference value for winter wheat farmland management and decision-making.
Aiming at the contradiction between temporal and spatial resolution of soil moisture monitoring,using the modified normalized vegetation index(NDVI) as the indicator of soil moisture,this paper puts forward a new method of real-time and semi-quantitative monitoring of soil moisture during winter wheat growth period by using the shared meteorological remote sensing data with high temporal resolution.First of all,the winter wheat planting area was divided into regions,and the growth stages were also divided according to its phenological characteristics;Secondly,NDVI was calculated by using meteorological remote sensing data,and the NDVI was adjusted adaptively in each stage and region.The correlation analysis between measured soil moisture data and modified NDVI showed that NDVI could be used as an indicator of soil moisture.Finally,the temporal and spatial distribution of soil moisture of winter wheat,through compared with those of previous three year averages,was obtained by analyzing the difference between the modified NDVI and the annual mean value,and the result was in agreement with the actual observations.It is foreseeable that if there is a longer time series to accumulate,the temporal and spatial distribution of soil moisture in the wheat field can be obtained through compared with the perennial year.The results show that the method proposed in this paper can be used to semi-quantify and quasi-real-time monitoring of soil moisture in winter wheat in the whole growth period.Moreover,remote sensing data are easy to obtain,almost no need of measured data,convenient calculation and low cost.So,it has important reference value for winter wheat farmland management and decision-making.
引文
[1]刘志明,张柏,晏明,等.土壤水分与干旱遥感研究的进展与趋势.地球科学进展[J],2003,18(4):576-583.
[2]武晋雯,孙龙彧,张玉书,等.不同植被覆盖下土壤水分遥感监测方法的比较研究[J].中国农学通报,2014,30(23):303-307.
[3]陈怀亮,毛留喜,冯定原.遥感监测土壤水分的理论、方法及研究进展[J].遥感技术与应用,1999,14(2):55-65.
[4]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,2006,15(6):114-121.
[5]Hardy J R.Survey of methods for the determination of soil mositure content by remote sensing[M].Remote Sensing in Agriculture and Hydrology.Rotterdam A A Balkema,1980:233-247
[6]张忠,蒲胜海,何春燕,等.我国土壤墒情预报模型的研究进程及发展方向[J].新疆农业科学,2007,44(5):720-723.
[7]Moran M S,Clarke T R,Inoue U,et al.Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index.Remote Sensing of Environment[J],1994,49:246-263.
[8]孙倩,阿丽亚·拜都热拉,依力亚斯江·努尔麦麦提.归一化植被指数对陆地储水量和降水变化的相应研究[J].中国农村水利水电,2018,(2):54-59.
[9]Goetz S J.Multisensor analysis of NDVI,surface temperature and biophysical variables at a mixed grassland site[J].International Journal of Remote Sensing,1997,18(1):71-94.
[10]Kogan F N.Remote sensing of weather impacts on vegetation in nonhomogeneous areas[J].International Journal of Remote Sensing,1990,11(8):1 405-1 419.
[11]张仁华.对定量热红外遥感的一些思考[J].国土资源遥感,1999,(1):1-7.
[12]Carlson T N,Perry E M,Schmugge T J.Remote sensing estimation of soil moisture availability and fractional vegetation cover for agricultural fields[J].Agricultural and Forest Meteorology,1990,52(1-2):45-69.
[13]Carlson T N,Gillies R R,Perry E M.A Method to Make Use of Thermal Infrared Temperature and NDVI Measurement to Infer Surface Soil Water Content and Fractional Vegetation Cover[J].Remote Sensing Environment,1994,9(1-2):161-173.
[14]Sandholt I,Rasmussen K,Anderson J.A simple interpretation of the surface temperature/vegetation index space for assessment of the surface moisture status[J].Remote Sensing of Environment,2002,79(2):213-224.
[15]王鹏新,龚健雅,李小文.条件温度植被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.
[16]王鹏新,Wan Zhengming,龚健雅,等.基于植被指数和土地表面温度的干旱监测模型[J].地球科学进展,2003,18(4):527-533.
[17]张洁,武建军,周磊,等.基于MODIS数据的农业干旱监测方法对比分析.遥感信息,2012,27(5):48-54.
[18]张学艺,张晓煜,李剑萍,等.我国干旱遥感监测技术方法研究进展[J].气象科技,2007,35(4):574-578.
[19]汪潇,张增祥,赵晓丽,等.遥感监测土壤水分研究综述[J].土壤学报,2007,44(1):157-163.
[20]石蒙蒙,薛兴燕,魏淑芸,等.河南黄淮海平原风沙区林农间作经济效益调查研究[J].河南科学,2016,34(7):1 069-1 071.
[21]李永文.河南地理[M].北京:北京师范大学出版社,2010.
[22]毛鹏飞.基于多遥感参数的冬小麦长势综合检测指标研究[D].开封:河南大学,2011.
[23]杜晓,王世新,周艺,等.一种新的基于MODIS的地表含水量模型构造与验证[J].武汉大学学报(信息科学版),2007,32(3):205-207,211.
[24]张红卫,陈怀亮,刘忠阳.基于MODIS数据的增强型土壤表层水分含量指数模型构造与应用[J].气象科技,2012,40(6):1 039-1 043.
[25]李耀辉.风云气象数据在农情定量监测中的应用研究[D].郑州:郑州大学,2016.
[26]陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱中监测中的应用[J].环境遥感,1994,9(2):106-112.
[27]马春芽,王景雷,黄修桥.遥感监测土壤水分研究进展[J].节水灌溉,2018,(5):70-74,78.
[2]武晋雯,孙龙彧,张玉书,等.不同植被覆盖下土壤水分遥感监测方法的比较研究[J].中国农学通报,2014,30(23):303-307.
[3]陈怀亮,毛留喜,冯定原.遥感监测土壤水分的理论、方法及研究进展[J].遥感技术与应用,1999,14(2):55-65.
[4]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,2006,15(6):114-121.
[5]Hardy J R.Survey of methods for the determination of soil mositure content by remote sensing[M].Remote Sensing in Agriculture and Hydrology.Rotterdam A A Balkema,1980:233-247
[6]张忠,蒲胜海,何春燕,等.我国土壤墒情预报模型的研究进程及发展方向[J].新疆农业科学,2007,44(5):720-723.
[7]Moran M S,Clarke T R,Inoue U,et al.Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index.Remote Sensing of Environment[J],1994,49:246-263.
[8]孙倩,阿丽亚·拜都热拉,依力亚斯江·努尔麦麦提.归一化植被指数对陆地储水量和降水变化的相应研究[J].中国农村水利水电,2018,(2):54-59.
[9]Goetz S J.Multisensor analysis of NDVI,surface temperature and biophysical variables at a mixed grassland site[J].International Journal of Remote Sensing,1997,18(1):71-94.
[10]Kogan F N.Remote sensing of weather impacts on vegetation in nonhomogeneous areas[J].International Journal of Remote Sensing,1990,11(8):1 405-1 419.
[11]张仁华.对定量热红外遥感的一些思考[J].国土资源遥感,1999,(1):1-7.
[12]Carlson T N,Perry E M,Schmugge T J.Remote sensing estimation of soil moisture availability and fractional vegetation cover for agricultural fields[J].Agricultural and Forest Meteorology,1990,52(1-2):45-69.
[13]Carlson T N,Gillies R R,Perry E M.A Method to Make Use of Thermal Infrared Temperature and NDVI Measurement to Infer Surface Soil Water Content and Fractional Vegetation Cover[J].Remote Sensing Environment,1994,9(1-2):161-173.
[14]Sandholt I,Rasmussen K,Anderson J.A simple interpretation of the surface temperature/vegetation index space for assessment of the surface moisture status[J].Remote Sensing of Environment,2002,79(2):213-224.
[15]王鹏新,龚健雅,李小文.条件温度植被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.
[16]王鹏新,Wan Zhengming,龚健雅,等.基于植被指数和土地表面温度的干旱监测模型[J].地球科学进展,2003,18(4):527-533.
[17]张洁,武建军,周磊,等.基于MODIS数据的农业干旱监测方法对比分析.遥感信息,2012,27(5):48-54.
[18]张学艺,张晓煜,李剑萍,等.我国干旱遥感监测技术方法研究进展[J].气象科技,2007,35(4):574-578.
[19]汪潇,张增祥,赵晓丽,等.遥感监测土壤水分研究综述[J].土壤学报,2007,44(1):157-163.
[20]石蒙蒙,薛兴燕,魏淑芸,等.河南黄淮海平原风沙区林农间作经济效益调查研究[J].河南科学,2016,34(7):1 069-1 071.
[21]李永文.河南地理[M].北京:北京师范大学出版社,2010.
[22]毛鹏飞.基于多遥感参数的冬小麦长势综合检测指标研究[D].开封:河南大学,2011.
[23]杜晓,王世新,周艺,等.一种新的基于MODIS的地表含水量模型构造与验证[J].武汉大学学报(信息科学版),2007,32(3):205-207,211.
[24]张红卫,陈怀亮,刘忠阳.基于MODIS数据的增强型土壤表层水分含量指数模型构造与应用[J].气象科技,2012,40(6):1 039-1 043.
[25]李耀辉.风云气象数据在农情定量监测中的应用研究[D].郑州:郑州大学,2016.
[26]陈维英,肖乾广,盛永伟.距平植被指数在1992年特大干旱中监测中的应用[J].环境遥感,1994,9(2):106-112.
[27]马春芽,王景雷,黄修桥.遥感监测土壤水分研究进展[J].节水灌溉,2018,(5):70-74,78.