Comparison of different BRDF correction methods to generate daily normalized MODIS 250 m time series

被引:15
作者
de Abelleyra, Diego [1 ]
Veron, Santiago R. [1 ,2 ]
机构
[1] INTA, Inst Clima & Agua, RA-1686 Hurlingham, Argentina
[2] Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina
关键词
Surface reflectance; Directional effects; Hyper-temporal; Crop monitoring MODIS 250 BRDF; MONITORING VEGETATION; MODEL; ALGORITHMS;
D O I
10.1016/j.rse.2013.08.019
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Realizing the full benefits of MODIS' temporal resolution requires, among others, the correction of the directional effect (i.e. the combined impact of the variation of the measurement geometry and of the observed land surface upon the registered radiant flux). While different BRDF methods have been proposed to address this effect, its performance has been evaluated at coarse spatial resolutions making it difficult to assess its applicability to, for example, crop monitoring. Here we test 2 approaches based on two different assumptions: the Classic approach that relies on the hypothesis of stable target and a recent Alternative that is based on the idea that despite reflectance magnitude may change rapidly, the BRDF shape varies slowly in time. Additionally, we segmented the growing season into different numbers of periods for the BRDF correction (a single period along the growing season, 3 periods based in phenology and 9-12 periods of fixed 16-days). The resulting 6 methods were compared over annual crops (wheat, maize and soybean) at 250 m spatial resolution from a site located in the Argentine Pampas. We used MOD and MYD 09 GQ and GA as inputs and compared the corrected daily red and infrared reflectances and the NDVI time series against the filtered benchmark (input time series with quality filters applied) by means of the high frequency variability (i.e. noise). We also tested whether corrected time series were better correlated with soybean PAR interception and biomass. Our results showed that methods' performance was more explained by the number of periods than by the approach (Classic or Alternative). Single period methods decreased noise by 52%, 55% and 4% for red, infrared and NDVI time series. The use of 3 periods improved the correction performance to 63, 64 and 24% for red, infrared and NVDI time series respectively, while the highest reductions (65,68 and 32% for red, infrared and NVDI) were found with 16-day intervals (9-12 periods) considering a magnitude inversion process. Wheat displayed the lowest noise reduction compared to the other crops. BRDF parameters obtained from different methods were associated to crop structure, suggesting that they have biophysical meaning. The decrease in noise obtained with correction methods was translated into a better assessment of the fraction of intercepted PAR and biomass. These promising results suggest the possibility of extensive field crop monitoring at an unprecedented temporal resolution. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:46 / 59
页数:14
相关论文
共 28 条
  • [1] A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data
    Becker-Reshef, I.
    Vermote, E.
    Lindeman, M.
    Justice, C.
    [J]. REMOTE SENSING OF ENVIRONMENT, 2010, 114 (06) : 1312 - 1323
  • [2] Analysis of hot spot directional signatures measured from space -: art. no. 4282
    Bréon, FM
    Maignan, F
    Leroy, M
    Grant, I
    [J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2002, 107 (D16): : AAC1 - 1
  • [3] Correction of MODIS surface reflectance time series for BRDF effects
    Breon, Francois-Marie
    Vermote, Eric
    [J]. REMOTE SENSING OF ENVIRONMENT, 2012, 125 : 1 - 9
  • [4] Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges
    Chander, G
    Markham, B
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41 (11): : 2674 - 2677
  • [5] Testing a LiSK BRDF model with in situ bidirectional reflectance factor measurements over semiarid grasslands
    Chopping, MJ
    [J]. REMOTE SENSING OF ENVIRONMENT, 2000, 74 (02) : 287 - 312
  • [6] COLWELL J E, 1974, Remote Sensing of Environment, V3, P175, DOI 10.1016/0034-4257(74)90003-0
  • [7] An evaluation of MODIS and SeaWiFS bio-optical algorithms in the Baltic Sea
    Darecki, M
    Stramski, D
    [J]. REMOTE SENSING OF ENVIRONMENT, 2004, 89 (03) : 326 - 350
  • [8] Global land cover mapping from MODIS: algorithms and early results
    Friedl, MA
    McIver, DK
    Hodges, JCF
    Zhang, XY
    Muchoney, D
    Strahler, AH
    Woodcock, CE
    Gopal, S
    Schneider, A
    Cooper, A
    Baccini, A
    Gao, F
    Schaaf, C
    [J]. REMOTE SENSING OF ENVIRONMENT, 2002, 83 (1-2) : 287 - 302
  • [9] Detecting vegetation structure using a kernel-based BRDF model
    Gao, F
    Schaaf, CB
    Strahler, AH
    Jin, Y
    Li, X
    [J]. REMOTE SENSING OF ENVIRONMENT, 2003, 86 (02) : 198 - 205
  • [10] Huete A, 2011, REMOTE SENS DIGIT IM, V11, P579, DOI 10.1007/978-1-4419-6749-7_26