Multisite analysis of land surface phenology in North American temperate and boreal deciduous forests from Landsat

Remote Sensing of Environment

Volumn 186, Issue , 2016, Pages 452-464

  Melaas, Eli K ^a   Sulla Menashe, Damien ^a   Gray, Josh M ^a   Black, T Andrew ^b   Morin, Timothy H ^c   Richardson, Andrew D ^d   Friedl, Mark A ^a  

^a BOSTON UNIVERSITY   (United States)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c OHIO STATE UNIVERSITY   (United States)

^d HARVARD UNIVERSITY   (United States)

Author keywords

Boreal forests; Eddy covariance; Landsat; PhenoCam; Phenology; Temperate forests

Indexed keywords

BIOLOGY; CLIMATE CHANGE; EARTH (PLANET); IMAGE RESOLUTION; QUALITY CONTROL; REMOTE SENSING; SURFACE MEASUREMENT; SYSTEMS ENGINEERING; TIME SERIES; TIME SERIES ANALYSIS;

BOREAL FORESTS; EDDY COVARIANCE; LANDSAT; PHENOCAM; PHENOLOGY; TEMPERATE FORESTS;

FORESTRY;

ALGORITHM; BOREAL FOREST; CARBON CYCLE; CLIMATE CHANGE; CLOUD COVER; DECIDUOUS FOREST; EDDY COVARIANCE; EOS; GROWING SEASON; LAND SURFACE; LANDSAT; MODIS; NORTHERN HEMISPHERE; PHENOLOGY; REMOTE SENSING; SEASONAL VARIATION; SENTINEL; TEMPERATE FOREST; TERRESTRIAL ECOSYSTEM;

NORTH AMERICA;

EID: 84988040365     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2016.09.014     Document Type: Article

References (76)

1. Ahl, D.E., Gower, S.T., Burrows, S.N., Shabanov, N.V., Myneni, R.B., Knyazikhin, Y., Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens. Environ. 104:1 (2006), 88–95 http://doi.org/10.1016/j.rse.2006.05.003.
2. Badeck, F.-W., Bondeau, A., Böttcher, K., Doktor, D., Lucht, W., Schaber, J., Sitch, S., Responses of spring phenology to climate change. New Phytol. 162:2 (2004), 295–309 http://doi.org/10.1111/j.1469-8137.2004.01059.x.
3. Bailey, A., Routine Phenology Measurements, Hubbard Brook Data Archive. 2016 Retrieved January 25, 2016 from http://hubbardbrook.org/data/dataset.php?id=51.
4. Barr, A.G., Black, T.A., Hogg, E.H., Kljun, N., Morgenstern, K., Nesic, Z., Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agric. For. Meteorol. 126:3–4 (2004), 237–255 http://doi.org/10.1016/j.agrformet.2004.06.011.
5. Beaubien, E.G., Freeland, H.J., Spring phenology trends in Alberta, Canada: links to ocean temperature. Int. J. Biometeorol. 44:2 (2000), 53–59 http://doi.org/10.1007/s004840000050.
6. Beck, P.S.A., Atzberger, C., Høgda, K.A., Johansen, B., Skidmore, A.K., Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sens. Environ. 100:3 (2006), 321–334 http://doi.org/10.1016/j.rse.2005.10.021.
7. Bennett, E.M., Peterson, G.D., Gordon, L.J., Understanding relationships among multiple ecosystem services. Ecol. Lett. 12:12 (2009), 1394–1404 http://doi.org/10.1111/j.1461-0248.2009.01387.x.
8. Betts, R.A., Offset of the potential carbon sink from boreal forestation by decreases in surface albedo. Nature 408:6809 (2000), 187–190 http://doi.org/10.1038/35041545.
9. Bonan, G.B., Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320:5882 (2008), 1444–1449 http://doi.org/10.1126/science.1155121.
10. Delbart, N., Kergoat, L., Le Toan, T., Lhermitte, J., Picard, G., Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens. Environ. 97:1 (2005), 26–38 http://doi.org/10.1016/j.rse.2005.03.011.
11. D'Odorico, P., Gonsamo, A., Gough, C.M., Bohrer, G., Morison, J., Wilkinson, M.,. Buchmann, N., The match and mismatch between photosynthesis and land surface phenology of deciduous forests. Agric. For. Meteorol. 214–215 (2015), 25–38 http://doi.org/10.1016/j.agrformet.2015.07.005.
12. Drolet, G.G., Huemmrich, K.F., Hall, F.G., Middleton, E.M., Black, T.A., Barr, A.G., Margolis, H.A., A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest. Remote Sens. Environ. 98:2–3 (2005), 212–224 http://doi.org/10.1016/j.rse.2005.07.006.
13. Eklundh, L., Jin, H., Schubert, P., Guzinski, R., Heliasz, M., An optical sensor network for vegetation phenology monitoring and satellite data calibration. Sensors 11:8 (2011), 7678–7709 http://doi.org/10.3390/s110807678.
14. Elmore, A.J., Guinn, S.M., Minsley, B.J., Richardson, A.D., Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Chang. Biol. 18:2 (2012), 656–674.
15. Eugster, W., Rouse, W.R., Pielke, R.A. Sr., Mcfadden, J.P., Baldocchi, D.D., Kittel, T.G.F.,. Chambers, S., Land–atmosphere energy exchange in Arctic tundra and boreal forest: available data and feedbacks to climate. Glob. Chang. Biol. 6:S1 (2000), 84–115 http://doi.org/10.1046/j.1365-2486.2000.06015.x.
16. Fisher, J.I., Mustard, J.F., Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sens. Environ. 109:3 (2007), 261–273 http://doi.org/10.1016/j.rse.2007.01.004.
17. Friedl, M.A., Gray, J.M., Melaas, E.K., Richardson, A.D., Hufkens, K., Keenan, T.F.,. O'Keefe, A tale of two springs: using recent climate anomalies to characterize the sensitivity of temperate forest phenology to climate change. Environ. Res. Lett., 9(5), 2014, 054006 http://doi.org/10.1088/1748-9326/9/5/054006.
18. Fry, J.A., Xian, G., Jin, S., Dewitz, J.A., Homer, C.G., Limin, Y.,. Wickham, J.D., Completion of the 2006 national land cover database for the conterminous United States. Photogramm. Eng. Remote. Sens. 77:9 (2011), 858–864 http://doi.org/10.1016/j.rse.2014.02.001.
19. Gao, F., Masek, J., Schwaller, M., Hall, F., On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44:8 (2006), 2207–2218 http://10.1109/TGRS.2006.872081.
20. Garrity, S.R., Bohrer, G., Maurer, K.D., Mueller, K.L., Vogel, C.S., Curtis, P.S., A comparison of multiple phenology data sources for estimating seasonal transitions in deciduous forest carbon exchange. Agric. For. Meteorol. 151:12 (2011), 1741–1752 http://doi.org/10.1016/j.agrformet.2011.07.008.
21. Gill, A.L., Gallinat, A.S., Sanders-DeMott, R., Rigden, A.J., Gianotti, D.J.S., Mantooth, J.A., Templer, P.H., Changes in autumn senescence in northern hemisphere deciduous trees: a meta-analysis of autumn phenology studies. Ann. Bot., 2015, mcv055 http://doi.org/10.1093/aob/mcv055.
22. Goetz, S.J., Bunn, A.G., Fiske, G.J., Houghton, R.A., Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. Proc. Natl. Acad. Sci. U. S. A. 102:38 (2005), 13521–13525 http://doi.org/10.1073/pnas.0506179102.
23. Gough, C.M., Vogel, C.S., Schmid, H.P., Su, H.-B., Curtis, P.S., Multi-year convergence of biometric and meteorological estimates of forest carbon storage. Agric. For. Meteorol. 148:2 (2008), 158–170 http://doi.org/10.1016/j.agrformet.2007.08.004.
24. Goward, S.N., Markham, B., Dye, D.G., Dulaney, W., Yang, J., Normalized difference vegetation index measurements from the advanced very high resolution radiometer. Remote Sens. Environ. 35:2 (1991), 257–277 http://doi.org/10.1016/0034-4257(91)90017-Z.
25. Hmimina, G., Dufrêne, E., Pontailler, J.-Y., Delpierre, N., Aubinet, M., Caquet, B.,. Soudani, K., Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements. Remote Sens. Environ. 132 (2013), 145–158 http://doi.org/10.1016/j.rse.2013.01.010.
26. Hogg, E.H.(.T.)., Brandt, J.P., Michaelian, M., Impacts of a regional drought on the productivity, dieback, and biomass of western Canadian aspen forests. Can. J. For. Res. 38:6 (2008), 1373–1384 http://doi.org/10.1139/X08-001.
27. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83:1–2 (2002), 195–213 http://doi.org/10.1016/S0034-4257(02)00096-2.
28. Hufkens, K., Friedl, M., Sonnentag, O., Braswell, B.H., Milliman, T., Richardson, A.D., Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology. Remote Sens. Environ. 117 (2012), 307–321 http://doi.org/10.1016/j.rse.2011.10.006.
29. Jenkins, J.P., Richardson, A.D., Braswell, B.H., Ollinger, S.V., Hollinger, D.Y., Smith, M.-L., Refining light-use efficiency calculations for a deciduous forest canopy using simultaneous tower-based carbon flux and radiometric measurements. Agric. For. Meteorol. 143:1–2 (2007), 64–79 http://doi.org/10.1016/j.agrformet.2006.11.008.
30. Jeong, S.-J., Ho, C.-H., Gim, H.-J., Brown, M.E., Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Chang. Biol. 17:7 (2011), 2385–2399 http://doi.org/10.1111/j.1365-2486.2011.02397.x.
31. Jiang, Z., Huete, A.R., Didan, K., Miura, T., Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 112:10 (2008), 3833–3845 http://doi.org/10.1016/j.rse.2008.06.006.
32. Jin, H., Eklundh, L., A physically based vegetation index for improved monitoring of plant phenology. Remote Sens. Environ. 152 (2014), 512–525 http://doi.org/10.1016/j.rse.2014.07.010.
33. Jönsson, A.M., Eklundh, L., Hellström, M., Bärring, L., Jönsson, P., Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology. Remote Sens. Environ. 114:11 (2010), 2719–2730 http://doi.org/10.1016/j.rse.2010.06.005.
34. Justice, C.O., Townshend, J.R.G., Holben, B.N., Tucker, C.J., Analysis of the phenology of global vegetation using meteorological satellite data. Int. J. Remote Sens. 6:8 (1985), 1271–1318 http://doi.org/10.1080/01431168508948281.
35. Keenan, T.F., Darby, B., Felts, E., Sonnentag, O., Friedl, M.A., Hufkens, K.,. Richardson, A.D., Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment. Ecol. Appl. 24:6 (2014), 1478–1489 http://doi.org/10.1890/13-0652.1.
36. Keenan, T.F., Gray, J., Friedl, M.A., Toomey, M., Bohrer, G., Hollinger, D.Y.,. Richardson, A.D., Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Chang. 4:7 (2014), 598–604 http://doi.org/10.1038/nclimate2253.
37. Klosterman, S.T., Hufkens, K., Gray, J.M., Melaas, E., Sonnentag, O., Lavine, I.,. Richardson, A.D., Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery. Biogeosciences 11:16 (2014), 4305–4320 http://doi.org/10.5194/bg-11-4305-2014.
38. Krishnan, P., Black, T.A., Grant, N.J., Barr, A.G., Hogg, E.(.T.).H., Jassal, R.S., Morgenstern, K., Impact of changing soil moisture distribution on net ecosystem productivity of a boreal aspen forest during and following drought. Agric. For. Meteorol. 139:3-4 (2006), 208–223 http://doi.org/10.1016/j.agrformet.2006.07.002.
39. Liu, Y., Wu, C., Peng, D., Xu, S., Gonsamo, A., Jassal, R.S.,. Chen, J.M., Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America. Remote Sens. Environ. 176 (2016), 152–162 http://doi.org/10.1016/j.rse.2016.01.021.
40. Masek, J.G., Huang, C., Wolfe, R., Cohen, W., Hall, F., Kutler, J., Nelson, P., North American forest disturbance mapped from a decadal Landsat record. Remote Sens. Environ. 112:6 (2008), 2914–2926 http://doi.org/10.1016/j.rse.2008.02.010.
41. Melaas, E.K., Friedl, M.A., Zhu, Z., Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM + data. Remote Sens. Environ. 132 (2013), 176–185 http://doi.org/10.1016/j.rse.2013.01.011.
42. Melaas, E.K., Richardson, A.D., Friedl, M.A., Dragoni, D., Gough, C.M., Herbst, M.,. Moors, E., Using FLUXNET data to improve models of springtime vegetation activity onset in forest ecosystems. Agric. For. Meteorol. 171–172 (2013), 46–56 http://doi.org/10.1016/j.agrformet.2012.11.018.
43. Myneni, R.B., Hall, F.G., The interpretation of spectral vegetation indexes. IEEE Trans. Geosci. Remote Sens. 33:2 (1995), 481–486 http://doi.org/10.1109/36.377948.
44. Myneni, R.B., Keeling, C.D., Tucker, C.J., Asrar, G., Nemani, R.R., Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386:6626 (1997), 698–702 http://doi.org/10.1038/386698a0.
45. Myneni, R.B., Tucker, C.J., Asrar, G., Keeling, C.D., Interannual variations in satellite-sensed vegetation index data from 1981 to 1991. J. Geophys. Res.-Atmos. 103:D6 (1998), 6145–6160 http://doi.org/10.1029/97JD03603.
46. Nijland, W., Bolton, D.K., Coops, N.C., Stenhouse, G., Imaging phenology; scaling from camera plots to landscapes. Remote Sens. Environ. 177 (2016), 13–20 http://doi.org/10.1016/j.rse.2016.02.018.
47. O'Keefe, J. (n.d.). Phenology of Woody Species at Harvard Forest since 1990. Retrieved January 25, 2016, from http://harvardforest.fas.harvard.edu:8080/exist/apps/datasets/showData.html?id=hf003.
48. Pan, Y., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A.,. Hayes, D., A large and persistent carbon sink in the world's forests. Science 333:6045 (2011), 988–993 http://doi.org/10.1126/science.1201609.
49. Pudas, E., Leppälä, M., Tolvanen, A., Poikolainen, J., Venäläinen, A., Kubin, E., Trends in phenology of Betula pubescens across the boreal zone in Finland. Int. J. Biometeorol. 52:4 (2007), 251–259 http://doi.org/10.1007/s00484-007-0126-3.
50. Reed, B.C., Brown, J.F., VanderZee, D., Loveland, T.R., Merchant, J.W., Ohlen, D.O., Measuring phenological variability from satellite imagery. J. Veg. Sci. 5:5 (1994), 703–714 http://doi.org/10.2307/3235884.
51. Richardson, A.D., O'Keefe, J., Phenological differences between understory and overstory: a case study using the long-term harvard forest records. Noormets, A., (eds.) Phenology of ecoystem processes, 2009, Springer, New York, NY, 87–117.
52. Richardson, A.D., Bailey, A.S., Denny, E.G., Martin, C.W., O'keefe, J., Phenology of a northern hardwood forest canopy. Glob. Chang. Biol. 12:7 (2006), 1174–1188 http://doi.org/10.1111/j.1365-2486.2006.01164.x.
53. Richardson, A.D., Black, T.A., Ciais, P., Delbart, N., Friedl, M.A., Gobron, N.,. Varlagin, A., Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 365, 2010, 3227–3246 (1555). http://doi.org/10.1098/rstb.2010.0102.
54. Richardson, A.D., Jenkins, J.P., Braswell, B.H., Hollinger, D.Y., Ollinger, S.V., Smith, M.-L., Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:2 (2007), 323–334.
55. Richardson, A.D., Keenan, T.F., Migliavacca, M., Ryu, Y., Sonnentag, O., Toomey, M., Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169 (2013), 156–173 http://doi.org/10.1016/j.agrformet.2012.09.012.
56. Riitters, K.H., Wickham, J.D., O'Neill, R.V., Jones, K.B., Smith, E.R., Coulston, J.W.,. Smith, J.H., Fragmentation of continental United States forests. Ecosystems 5:8 (2002), 0815–0822 http://doi.org/10.1007/s10021-002-0209-2.
57. Schmid, H.P., Grimmond, C.S.B., Cropley, F., Offerle, B., Su, H.-B., Measurements of CO2 and energy fluxes over a mixed hardwood forest in the mid-western United States. Agric. For. Meteorol. 103:4 (2000), 357–374 http://doi.org/10.1016/S0168-1923(00)00140-4.
58. Serbin, S.P., Ahl, D.E., Gower, S.T., Spatial and temporal validation of the MODIS LAI and FPAR products across a boreal forest wildfire chronosequence. Remote Sens. Environ. 133 (2013), 71–84 http://doi.org/10.1016/j.rse.2013.01.022.
59. Slayback, D.A., Pinzon, J.E., Los, S.O., Tucker, C.J., Northern hemisphere photosynthetic trends 1982–99. Glob. Chang. Biol. 9:1 (2003), 1–15 http://doi.org/10.1046/j.1365-2486.2003.00507.x.
60. Soja, A.J., Tchebakova, N.M., French, N.H.F., Flannigan, M.D., Shugart, H.H., Stocks, B.J.,. Stackhouse, P.W. Jr., Climate-induced boreal forest change: predictions versus current observations. Glob. Planet. Chang. 56:3–4 (2007), 274–296 http://doi.org/10.1016/j.gloplacha.2006.07.028.
61. Sonnentag, O., Hufkens, K., Teshera-Sterne, C., Young, A.M., Friedl, M., Braswell, B.H.,. Richardson, A.D., Digital repeat photography for phenological research in forest ecosystems. Agric. For. Meteorol. 152 (2012), 159–177 http://doi.org/10.1016/j.agrformet.2011.09.009.
62. Studer, S., Stöckli, R., Appenzeller, C., Vidale, P.L., A comparative study of satellite and ground-based phenology. Int. J. Biometeorol. 51:5 (2007), 405–414 http://doi.org/10.1007/s00484-006-0080-5.
63. Sulla-Menashe, D., Friedl, M.A., Woodcock, C.E., Sources of bias and variability in long-term Landsat time series over Canadian boreal forests. Remote Sens. Environ. 177 (2016), 206–219 http://doi.org/10.1016/j.rse.2016.02.041.
64. Urbanski, S., Barford, C., Wofsy, S., Kucharik, C., Pyle, E., Budney, J.,. Munger, J.W., Factors controlling CO2 exchange on timescales from hourly to decadal at Harvard Forest. J. Geophys. Res. Biogeosci., 112(G2), 2007, G02020 http://doi.org/10.1029/2006JG000293.
65. Verma, M., Friedl, M.A., Finzi, A., Phillips, N., Multi-criteria evaluation of the suitability of growth functions for modeling remotely sensed phenology. Ecol. Model. 323 (2016), 123–132 http://doi.org/10.1016/j.ecolmodel.2015.12.005.
66. Vermote, E.F., El Saleous, N., Justice, C.O., Kaufman, Y.J., Privette, J.L., Remer, L.,. Tanré, D., Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation. J. Geophys. Res.-Atmos. 102:D14 (1997), 17131–17141 http://doi.org/10.1029/97JD00201.
67. White, K., Pontius, J., Schaberg, P., Remote sensing of spring phenology in northeastern forests: a comparison of methods, field metrics and sources of uncertainty. Remote Sens. Environ. 148 (2014), 97–107 http://doi.org/10.1016/j.rse.2014.03.017.
68. Wu, C., Gonsamo, A., Chen, J.M., Kurz, W.A., Price, D.T., Lafleur, P.M.,. Munger, J.W., Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: a North America flux data synthesis. Glob. Planet. Chang. 92–93 (2012), 179–190 http://doi.org/10.1016/j.gloplacha.2012.05.021.
69. Wulder, M.A., Dechka, J.A., Gillis, M.A., Luther, J.E., Hall, R.J., Beaudoin, A., Franklin, S.E., Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program. For. Chron. 79:6 (2003), 1075–1083 http://doi.org/10.5558/tfc791075-6.
70. Xu, L., Myneni, R., Chapin Iii, F., Callaghan, T., Pinzon, J., Tucker, C., Temperature and vegetation seasonality diminishment over northern lands. Nat. Clim. Chang. 3:6 (2013), 581–586.
71. Yang, X., Tang, J., Mustard, J.F., Beyond leaf color: Comparing camera-based phenological metrics with leaf biochemical, biophysical, and spectral properties throughout the growing season of a temperate deciduous forest. J. Geophys. Res. Biogeosci., 119(3), 2014, 2013JG002460 http://doi.org/10.1002/2013JG002460.
72. Zhang, X., Friedl, M.A., Schaaf, C.B., Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res. Biogeosci., 111(G4), 2006, G04017 http://doi.org/10.1029/2006JG000217.
73. Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F.,. Huete, A., Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84:3 (2003), 471–475 http://doi.org/10.1016/S0034-4257(02)00135-9.
74. Zhou, L., Kaufmann, R.K., Tian, Y., Myneni, R.B., Tucker, C.J., Relation between interannual variations in satellite measures of northern forest greenness and climate between 1982 and 1999. J. Geophys. Res.-Atmos., 108(D1), 2003, 4004 http://doi.org/10.1029/2002JD002510.
75. Zhu, Z., Woodcock, C.E., Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118 (2012), 83–94 http://doi.org/10.1016/j.rse.2011.10.028.
76. Zhu, Z., Woodcock, C.E., Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ. 144 (2014), 152–171 http://doi.org/10.1016/j.rse.2014.01.011.