基于时序LAI的地块尺度玉米长势监测方法
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摘要
农作物长势监测可为田间管理措施调整和农作物产量预测提供及时、准确的信息。针对中国地块面积小的情况,本文采用中高分辨率的多源遥感影像Landsat-7 ETM+影像、Landsat-8 OLI影像、高分一号(GF-1)影像、HJ-1 A/B卫星影像为数据源;针对目前过多依赖NDVI等易饱和植被指数的问题,研究中选择LAI为长势监测指标,并利用PROSAIL辐射传输模型反演LAI,以保证该指标的准确性;长势实时监测采用RPLAI、LVCI、MLVCI指标,从多个角度表征长势的实时监测结果。研究结果表明:①同时相GF-1影像和Landsat-8 OLI影像具有较高的相关性,2种影像在近红外波段、绿波段、红波段的相关性决定系数R~2分别为0.9320、0.7339、0.7153。②基于PROSAIL模型可以反演得到高精度的LAI,基于时序LAI的玉米生长过程监测结果表明:2015年,黑龙江农垦总局八五二农场6月下旬玉米冠层LAI快速增加,7月底、8月初LAI达到最大,并持续一段时间,进入8月下旬后,LAI开始下降。③利用RPLAI、LVCI、MLVCI指标对研究区玉米长势实时监测的结果表明,与2011—2014年相比,2015年八五二农场玉米长势一般,研究区北部长势较好,南部区域长势较差。从研究结果我们得出如下结论:①同时相的Landsat-8 OLI影像与GF-1遥感影像,经过相对辐射定标后可以结合使用于农作物长势监测中;②利用PROSAIL模型反演时序LA,可用于地块尺度的农作物长势精细监测。
Crop growth monitoring is an important work for crop management and yield prediction. Aimed to solve the problem of mixed spectrum resulting from small agricultural land plots in China, this study used high and medium spatial resolution images including Landsat-7 ETM+, Landsat-8 OLI, GF-1, and HJ-1 A/B images for corn growth monitoring. In order to avoid too much reliance on the Normalized Difference Vegetation Index(NDVI) parameters, leaf area index(LAI) was selected as the corn growth monitoring parameter and the PROSAIL radiative transfer model was used to retrieve LAI. Three indices(LAI ratio to previous year(RPLAI),vegetation condition index based on LAI(LVCI), and mean vegetation condition index based on LAI(MLVCI)) were used for real-time monitoring of corn growth. The results of a case study in the852 Farm of Heilongjiang Farms & Land Reclamation Administration in 2015 indicate that:(1)The simultaneous GF-1 image reflentance and the Landsat-8 OLI image reflentance are highly correlated. The correlation coefficients R~2 of near-infrared bands, green bands, and red bands of the GF-1 image and the Landsat-8 OLI image are 0.9320, 0.7339, and 0.7153, respectively. This is the precondition for establishing the time series LAI for corn growth monitoring using multi-source remote sensing images.(2) The accuracy of retrieving LAI using PROSAIL radiative transfer model is high——the correlation coefficient R~2 is 0.8030 and the root mean squared error(RMSE)is 0.7675. The retrieved time series LAI indicate that LAI increased quickly at the end of June,reached the maximum at the end of July or the beginning of August, and started to decrease at the end of August.(3) The RPLAI, LVCI, and MLVCI indices were used for the real-time monitoring of corn growth and the results indicate that the growth in 2015 was at an average level, and corn growth in the northern part of the area was better than in the southern part.
Crop growth monitoring is an important work for crop management and yield prediction. Aimed to solve the problem of mixed spectrum resulting from small agricultural land plots in China, this study used high and medium spatial resolution images including Landsat-7 ETM+, Landsat-8 OLI, GF-1, and HJ-1 A/B images for corn growth monitoring. In order to avoid too much reliance on the Normalized Difference Vegetation Index(NDVI) parameters, leaf area index(LAI) was selected as the corn growth monitoring parameter and the PROSAIL radiative transfer model was used to retrieve LAI. Three indices(LAI ratio to previous year(RPLAI),vegetation condition index based on LAI(LVCI), and mean vegetation condition index based on LAI(MLVCI)) were used for real-time monitoring of corn growth. The results of a case study in the852 Farm of Heilongjiang Farms & Land Reclamation Administration in 2015 indicate that:(1)The simultaneous GF-1 image reflentance and the Landsat-8 OLI image reflentance are highly correlated. The correlation coefficients R~2 of near-infrared bands, green bands, and red bands of the GF-1 image and the Landsat-8 OLI image are 0.9320, 0.7339, and 0.7153, respectively. This is the precondition for establishing the time series LAI for corn growth monitoring using multi-source remote sensing images.(2) The accuracy of retrieving LAI using PROSAIL radiative transfer model is high——the correlation coefficient R~2 is 0.8030 and the root mean squared error(RMSE)is 0.7675. The retrieved time series LAI indicate that LAI increased quickly at the end of June,reached the maximum at the end of July or the beginning of August, and started to decrease at the end of August.(3) The RPLAI, LVCI, and MLVCI indices were used for the real-time monitoring of corn growth and the results indicate that the growth in 2015 was at an average level, and corn growth in the northern part of the area was better than in the southern part.
引文
[1]杨邦杰,裴志远.农作物长势的定义与遥感监测[J].农业工程学报, 1999, 15(3):214-218.[Yang B J, Pei Z Y. Definition of crop condition and crop monitoring using remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering, 1999, 15(3):214-218.]
[2]吴炳方,张峰,刘成林,等.农作物长势综合遥感监测方法[J].遥感学报, 2004, 8(6):498-514.[Wu B F, Zhang F, Liu C L, et al.An integrated method for crop condition monitoring[J]. Journal of Remote Sensing, 2004, 8(6):498-514.]
[3]邹文涛,吴炳方,张淼,等.农作物长势综合监测:以印度为例[J].遥感学报, 2015, 19(4):539-549.[Zou W T, Wu B F, Zhang M, et al. Synthetic method for crop condition analysis:A case study in India[J]. Journal of Remote Sensing, 2015, 19(4):539-549.]
[4] Doraiswamy P C, Hatfield J L, Jackson T J, et al. Crop condition and yield simulations using Landsat and MODIS[J]. Remote Sensing of Environment, 2004, 92(4):548-559.
[5] USDA NASS. History of Remote Sensing for Crop Acreage[EB/OL].(2009-05-27)[2013-03-01]. http://www.nass.usda.gov/Surveys/Remotely_Sensed_Data_Crop_Acreage.
[6]吴炳方,蒙继华,李强子,等.“全球农情遥感速报系统(Crop Watch)”新进展[J].地球科学进展, 2010, 25(10):1013-1022.[Wu B F, Meng J H, Li Q Z, et al. Latest development of“Crop Watch”:A global crop monitoring system with remote sensing[J].Advances in Earth Science, 2010, 25(10):1013-1022.]
[7]杨邦杰.农情遥感监测[M].北京:中国农业出版社, 2005.[Yang B J. Agricultural Condition Monitoring Using Remote Sensing[M]. Beijing:China Agriculture Press, 2005.]
[8]蒙继华,杜鑫,张淼,等.物候信息在大范围作物长势遥感监测中的应用[J].遥感技术与应用, 2014, 29(2):278-285.[Meng J H, Du X, Zhang M, et al. Integrating crop phenophase information in large-area crop condition evaluation with remote sensing[J]. Remote Sensing Technology and Application, 2014, 29(2):278-285.]
[9] Kogan F, Salazar L, Roytman L. Forecasting crop production using satellite-based vegetation health indices in Kansas, USA[J]. International Journal of Remote Sensing, 2012, 30(1-4):2798-2814.
[10] Huang J, Sedano F, Huang Y, et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation[J]. Agricultural&Forest Meteorology, 2016, 216:188-202.
[11] Yang Z, Di L, Yu G, et al. Vegetation Condition Indices for Crop Vegetation Condition Monitoring[C]. Vancouver:2011 IEEE International Geoscience and Remote Sensing Symposium, 2011.
[12] Jacquemoud S, Baret F. PROSPECT:A model of leaf optical properties spectra[J]. Remote Sensing of Environment, 1990, 34(2):75-91.
[13] Baret F, Jacquemoud S, Guyot G, et al. Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands[J]. Remote Sensing of Environment,1992, 41(2-3):133-142.
[14] Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling:The SAIL model[J]. Remote Sensing of Environment, 1984, 16(2):125-141.
[15] Verhoef W, Jia L, Xiao Q, et al. Unified optical-thermal fourstream radiative transfer theory for homogeneous vegetation canopies[J]. IEEE Transactions on Geoscience&Remote Sensing, 2007,45(6):1808-1822.
[16] Jacquemoud S, Verhoef W, Baret F, et al. PROSPECT+SAIL models:A review of use for vegetation characterization[J]. Remote Sensing of Environment, 2009, 113(S1):S56-S66.
[17]苏伟,郭皓,赵冬玲,等.基于优化PROSAIL叶倾角分布函数的玉米LAI反演方法[J].农业机械学报, 2016, 47(3):234-241.[Su W, Guo H, Zhao D L, et al. Leaf area index retrieval for maize canopy using optimized leaf angle distribution function of PROSAIL model[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(3):234-241.]
[18]苏伟,吴代英,武洪峰,等.基于最大熵模型的玉米冠层LAI升尺度方法[J].农业工程学报, 2016, 34(7):165-172.[Su W, Wu D Y, Wu H F, et al. Upscaling method for corn canopy LAI using Max Ent model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(7):165-172.]
[2]吴炳方,张峰,刘成林,等.农作物长势综合遥感监测方法[J].遥感学报, 2004, 8(6):498-514.[Wu B F, Zhang F, Liu C L, et al.An integrated method for crop condition monitoring[J]. Journal of Remote Sensing, 2004, 8(6):498-514.]
[3]邹文涛,吴炳方,张淼,等.农作物长势综合监测:以印度为例[J].遥感学报, 2015, 19(4):539-549.[Zou W T, Wu B F, Zhang M, et al. Synthetic method for crop condition analysis:A case study in India[J]. Journal of Remote Sensing, 2015, 19(4):539-549.]
[4] Doraiswamy P C, Hatfield J L, Jackson T J, et al. Crop condition and yield simulations using Landsat and MODIS[J]. Remote Sensing of Environment, 2004, 92(4):548-559.
[5] USDA NASS. History of Remote Sensing for Crop Acreage[EB/OL].(2009-05-27)[2013-03-01]. http://www.nass.usda.gov/Surveys/Remotely_Sensed_Data_Crop_Acreage.
[6]吴炳方,蒙继华,李强子,等.“全球农情遥感速报系统(Crop Watch)”新进展[J].地球科学进展, 2010, 25(10):1013-1022.[Wu B F, Meng J H, Li Q Z, et al. Latest development of“Crop Watch”:A global crop monitoring system with remote sensing[J].Advances in Earth Science, 2010, 25(10):1013-1022.]
[7]杨邦杰.农情遥感监测[M].北京:中国农业出版社, 2005.[Yang B J. Agricultural Condition Monitoring Using Remote Sensing[M]. Beijing:China Agriculture Press, 2005.]
[8]蒙继华,杜鑫,张淼,等.物候信息在大范围作物长势遥感监测中的应用[J].遥感技术与应用, 2014, 29(2):278-285.[Meng J H, Du X, Zhang M, et al. Integrating crop phenophase information in large-area crop condition evaluation with remote sensing[J]. Remote Sensing Technology and Application, 2014, 29(2):278-285.]
[9] Kogan F, Salazar L, Roytman L. Forecasting crop production using satellite-based vegetation health indices in Kansas, USA[J]. International Journal of Remote Sensing, 2012, 30(1-4):2798-2814.
[10] Huang J, Sedano F, Huang Y, et al. Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation[J]. Agricultural&Forest Meteorology, 2016, 216:188-202.
[11] Yang Z, Di L, Yu G, et al. Vegetation Condition Indices for Crop Vegetation Condition Monitoring[C]. Vancouver:2011 IEEE International Geoscience and Remote Sensing Symposium, 2011.
[12] Jacquemoud S, Baret F. PROSPECT:A model of leaf optical properties spectra[J]. Remote Sensing of Environment, 1990, 34(2):75-91.
[13] Baret F, Jacquemoud S, Guyot G, et al. Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands[J]. Remote Sensing of Environment,1992, 41(2-3):133-142.
[14] Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling:The SAIL model[J]. Remote Sensing of Environment, 1984, 16(2):125-141.
[15] Verhoef W, Jia L, Xiao Q, et al. Unified optical-thermal fourstream radiative transfer theory for homogeneous vegetation canopies[J]. IEEE Transactions on Geoscience&Remote Sensing, 2007,45(6):1808-1822.
[16] Jacquemoud S, Verhoef W, Baret F, et al. PROSPECT+SAIL models:A review of use for vegetation characterization[J]. Remote Sensing of Environment, 2009, 113(S1):S56-S66.
[17]苏伟,郭皓,赵冬玲,等.基于优化PROSAIL叶倾角分布函数的玉米LAI反演方法[J].农业机械学报, 2016, 47(3):234-241.[Su W, Guo H, Zhao D L, et al. Leaf area index retrieval for maize canopy using optimized leaf angle distribution function of PROSAIL model[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(3):234-241.]
[18]苏伟,吴代英,武洪峰,等.基于最大熵模型的玉米冠层LAI升尺度方法[J].农业工程学报, 2016, 34(7):165-172.[Su W, Wu D Y, Wu H F, et al. Upscaling method for corn canopy LAI using Max Ent model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(7):165-172.]
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