A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

Remote Sensing - Tập 9 Số 3 - Trang 296
B. Franch1, Éric Vermote, Jean‐Claude Roger1,2,3, Emilie Murphy, Inbal Becker‐Reshef, Chris Justice, Martin Claverie, Jyoteshwar Nagol, Ivan Csiszar, David Meyer, Frédéric Baret, E. Masuoka, Robert E. Wolfe, Sadashiva Devadiga
1Department of Geographical Sciences [College Park] (University of Maryland, 2181 Samuel J. LeFrak Hall, 7251 Preinkert Drive, College Park, MD 20742 - United States)
2GSFC - NASA Goddard Space Flight Center (Greenbelt, MD 20771 - United States)
3LaMP - Laboratoire de Météorologie Physique (4 avenue Blaise Pascal, TSA 60026 / CS 60026, 63178 Aubière Cedex - France)

Tóm tắt

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.

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Tài liệu tham khảo

Zhang, 2015, Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data, Remote Sens. Environ., 156, 457, 10.1016/j.rse.2014.10.012

Vermote, 1995, Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views, Int. J. Remote Sens., 16, 2317, 10.1080/01431169508954561

Vermote, 2006, Calibration of NOAA16 AVHRR over a desert site using MODIS data, Remote Sens. Environ., 105, 214, 10.1016/j.rse.2006.06.015

Arbelo, 2012, Burned area mapping time series in Canada (1984–1999) from NOAA-AVHRR LTDR: A comparison with other remote sensing products and fire perimeters, Remote Sens. Environ., 117, 407, 10.1016/j.rse.2011.10.017

Liras, 2010, Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II, Sensors, 10, 1291, 10.3390/s100201291

Verger, A., Baret, F., Weiss, M., Lacaze, R., Makhmara, H., Pacholczyk, P., Smets, B., Kandasamy, S., and Vermote, E. (2012, January 22–27). LAI, FAPAR and FCOVER products derived from AVHRR long time series: Principles and evaluation. Proceedings of the EGU General Assembly 2012, Vienna, Austria.

Vermote, 2010, A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data, Remote Sens. Environ., 114, 1312, 10.1016/j.rse.2010.01.010

Franch, 2015, Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information, Remote Sens. Environ., 161, 131, 10.1016/j.rse.2015.02.014

Pedelty, J., Devadiga, S., Masuoka, E., Brown, M., Pinzon, J., Tucker, C., Roy, D., Ju, J., Vermote, E., and Prince, S. (2007, January 23–28). Generating a long-term land data record from the AVHRR and MODIS Instruments. Proceedings of the IGARSS 2007—2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.

Vermote, 2000, Improvements in the global biospheric record from the Advanced Very High Resolution Radiometer (AVHRR), Int. J. Remote Sens., 21, 1251, 10.1080/014311600210164

Cosnefroy, 1996, Selection and characterization of Saharan and Arabian desert sites for the calibration of optical satellite sensors, Remote Sens. Environ., 58, 101, 10.1016/0034-4257(95)00211-1

Schaaf, 2002, First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., 83, 135, 10.1016/S0034-4257(02)00091-3

Vermote, 1997, Second Simulation of the Satellite Signal in the Solar Spectrum (6S): An overview, IEEE Trans. Geosci. Remote Sens., 35, 675, 10.1109/36.581987

Stowe, 1999, Scientific Basis and Initial Evaluation of the CLAVR-1 Global Clear/Cloud Classification Algorithm for the Advanced Very High Resolution Radiometer, J. Atmos. Ocean. Technol., 16, 656, 10.1175/1520-0426(1999)016<0656:SBAIEO>2.0.CO;2

Holben, 1998, AERONET—A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sens. Environ., 66, 1, 10.1016/S0034-4257(98)00031-5

Morisette, 2002, A framework for the validation of MODIS Land products, Remote Sens. Environ., 83, 77, 10.1016/S0034-4257(02)00088-3

Vermote, 2009, Towards a generalized approach for correction of the BRDF effect in MODIS directional reflectances, IEEE Trans. Geosci. Remote Sens., 47, 898, 10.1109/TGRS.2008.2005977

Baret, 2006, Evaluation of the representativeness of networks of sites for the global validation and intercomparison of land biophysical products: Proposition of the CEOS-BELMANIP, Geosci. Remote Sens. IEEE Trans., 44, 1794, 10.1109/TGRS.2006.876030

Wolfe, 2002, Achieving sub-pixel geolocation accuracy in support of MODIS land science, Remote Sens. Environ., 83, 31, 10.1016/S0034-4257(02)00085-8

Leroy, 1994, Sun and view angle corrections on reflectances derived from NOAA/AVHRR data, IEEE Trans. Geosci. Remote Sens., 32, 684, 10.1109/36.297985

Bicheron, 2011, Geolocation Assessment of MERIS GlobCover Orthorectified Products, IEEE Trans. Geosci. Remote Sens., 49, 2972, 10.1109/TGRS.2011.2122337

Evans, R.H., Casey, K.S., and Cornillon, P.C. (2010). Transition of AVHRR SST Pathfinder to Version 6, Continued Evolution of a CDR, American Geophysical Union.

Rao, 1996, Post-launch Calibration of the Visible and Near-Infrared Channels of the Advanced Very High Resolution Radiometer on the NOAA-14 Spacecraft, Int. J. Remote Sens., 17, 2743, 10.1080/01431169608949104

Tucker, 2005, An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26, 4485, 10.1080/01431160500168686

Claverie, M., Vermote, E., and Program, N.C. (2014). NOAA Climate Data Record (CDR) of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 4, NOAA National Climatic Data Center.

Claverie, M., Matthews, J.L., Vermote, E.F., and Justice, C.O. (2016). A 30+ Year AVHRR LAI and FAPAR Climate Data Record: Algorithm Description and Validation. Remote Sens., 8.

Garrigues, S., Lacaze, R., Baret, F., Morisette, J.T., Weiss, M., Nickeson, J.E., Fernandes, R., Plummer, S., Shabanov, N.V., and Myneni, R.B. (2008). Validation and intercomparison of global Leaf Area Index products derived from remote sensing data. J. Geophys. Res. Biogeosci., 113.

Nash, 1970, River flow forecasting through conceptual models part I—A discussion of principles, J. Hydrol., 10, 282, 10.1016/0022-1694(70)90255-6