Abstract
Context
The open and free access to Landsat and MODIS products have greatly promoted scientific investigations on spatiotemporal change in land mosaics and ecosystem functions at landscape to regional scales. Unfortunately, there is a major mismatch in spatial resolution between MODIS products at coarser resolution (≥ 250 m) and landscape structure based on classified Landsat scenes at finer resolution (30 m).
Objectives
Based on practical needs for downscaling popular MODIS products at 500 m resolution to match classified land cover at Landsat 30 m resolution, we proposed an innovative modelling approach so that landscape structure and ecosystem functions can be directly studied for their interconnections. As a proof-of-concept of our downscaling approach, we selected the watershed of the Kalamazoo River in southwestern Michigan, USA as the testbed.
Methods
MODIS products for three fundamental variables of ecosystem function are downscaled to ensure the approach can be extrapolated to multiple functional measurements. They are blue-sky albedo (0–1), evapotranspiration (ET, mm), and gross primary production (GPP, Mg C ha−1 year−1). An object-oriented classification of Landsat images in 2011 was processed to generate a land cover map for landscape structure. The downscaling model was tested for the five Level IV ecoregions within the watershed.
Results
We achieved satisfactory downscaling models for albedo, ET, and GPP for all five ecoregions. The adjusted R2 was > 0.995 for albedo, 0.915–0.997 for ET, and 0.902–0.962 for GPP. The estimated albedo, ET, and GPP values appear different in the region. The estimated albedo was the lowest for water (0.076–0.107) and the highest for cropland (0.166–0.172). Estimated ET was the highest for the built-up cover type (525.6–687.1 mm) and the lowest for forest (209.7–459.7 mm). The estimated GPP was the highest for the build-up cover type (8.65–9.85 Mg C ha−1 year−1) and the lowest for forest.
Conclusions
Estimated values for albedo, ET, and GPP appear reasonable for their ranges in the Kalamazoo River region and are consistent with values reported in the literature. Despite these promising results, the downscaling approach relies on strong assumptions and can carry substantial uncertainty. It is only valid at a spatial scale where similar climate, soil, and landforms exist (i.e., values in isolated patches of the same cover type are similar). Plausibly, the uncertainties associated with each estimation, as well as the model residuals, can be explored for other pattern-process relationships within the landscape.
Similar content being viewed by others
References
Abraha M, Chen J, Chu H et al (2015) Evapotranspiration of annual and perennial biofuel crops in a variable climate. Glob Chang Biol Bioenergy 7(6):1344–1356
Alberti M, Asbjornsen H, Baker LA et al (2011) Research on coupled human and natural systems (CHANS): approach, challenges, and strategies. Bull Ecol Soc Am 92:218–228
Anderson JR, Hardy EE, Roach JT et al (1976) A land use and land cover classification system for use with remote sensor data. Prof Pap. https://doi.org/10.3133/pp964
Atkinson PM (2013) Downscaling in remote sensing. Int J Appl Earth Obs Geoinf 22:106–114
Barnes CA, Roy DP (2010) Radiative forcing over the conterminous United States due to contemporary land cover land use change and sensitivity to snow and interannual albedo variability. J Geophys Res 115:G04033. https://doi.org/10.1029/2010JG001428
Bass DG (2009) Inferring dissolved phosphorus cycling in a TMDL watershed using biogeochemistry and mixed linear models. PhD disertation, Michigan State University, East Lansing, Michigan
Bonan GB (1997) Effects of land use on the climate of the United States. Clim Change 37(3):449–486
Bresee MK, Le Moine J, Mather S et al (2004) Disturbance and landscape dynamics in the Chequamegon National Forest Wisconsin, USA, from 1972 to 2001. Landsc Ecol 19(3):291–309
Campbell GS, Norman JM (1998) Introduction to environmental biophysics. Springer, New York, p 286p
Chapman KA, Brewer R (2008) Prairie and Savanna in southern lower Michigan: history, classification, ecology. Michigan Bot 47:1–48
Chen J (1991) Edge effects: microclimatic pattern and biological responses in old-growth Douglas-fir forests. PhD dissertation, University of Washington, Seattle, WA
Chen J, Wan S, Henebry G et al (eds) (2013) Dryland east Asia: land dynamics amid social and climate change. DE GRUYTER, Berlin
Chrysoulakis N, Mitraka Z, Gorelick N (2018) Exploiting satellite observations for global surface albedo trends monitoring. Theor Appl Climatol. https://doi.org/10.1007/s00704-018-2663-6
Cressie N, Wikle CK (2015) Statistics for spatio-temporal data. Wiley, New Jersey, p 624p
Di Giulio M, Holderegger R, Tobias S (2009) Effects of habitat and landscape fragmentation on humans and biodiversity in densely populated landscapes. J Environ Manage 90:2959–2968
Dieye AM, Roy DP, Hanan NP et al (2012) Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal. Biogeosciences 9:631–648
Drusch M, Del Bello U, Carlier S et al (2012) Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25–36
Dwyer JL, Roy DP, Saue B et al (2018) Analysis ready data enabling analysis of the Landsat archive. Remote Sens 10(1363):19. https://doi.org/10.3390/rs10091363
Fensholt R, Proud SR (2012) Evaluation of earth observation based global long term vegetation trends—comparing GIMMS and MODIS global NDVI time series. Remote Sens Environ 119:131–147
Fisher F Peter, Langford Mitchel (1996) Modeling sensitivity to accuracy in classified imagery: a study of areal interpolation by dasymetric map**. Prof Geog 48(3):299–309
Fongers D (2008) Kalamazoo River watershed hydrologic study. Michigan Department of Environmental Quality, Lansing, MI, p 67p
Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80(1):185–201
Forman RTT (1995) Land mosaics: the ecology of landscapes and regions. Cambridge University Press, Cambridge, p 217p
Franklin JF, Forman RTT (1987) Creating landscape patterns by forest cutting: ecological consequences and principles. Landsc Ecol 1:5–18
Friedl MA, Sulla-Menashe D, Tan B et al (2010) MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens Environ 114:168–182
Giglio L, Boschetti L, Roy DP et al (2018) The collection 6 MODIS burned area map** algorithm and product. Remote Sens Environ 217:72–85
Gitelson AA, Peng Y, Masek JG et al (2012) Remote estimation of crop gross primary production with Landsat data. Remote Sens Environ 121:404–414
Goodin DG, Henebry GM (1998) Seasonality of finely-resolved spatial structure of NDVI and its component reflectances in tallgrass prairie. Int J Remote Sens 19(16):3213–3220
Hansen MC, Loveland TR (2012) A review of large area monitoring of land cover change using Landsat data. Remote Sens Environ 122:66–74
Hargrove WW, Pickering J (1992) Pseudoreplication: a sine qua non for regional ecology. Landsc Ecol 6(4):251–258
Helder D, Markham B, Morfitt R et al (2018) Observations and recommendations for the calibration of Landsat 8 OLI and Sentinel 2 MSI for improved data interoperability. Remote Sens 10(9):1340. https://doi.org/10.3390/rs10091340
Henebry GM (1993) Detecting change in grasslands using measures of spatial dependence with Landsat TM data. Remote Sen Environ 46(2):223–234
Jia P, Gaughan AE (2016) Dasymetric modeling: a hybrid approach using land cover and tax parcel data for map** population in Alachua County, Florida. Appl Geogr 66:100–108
John R, Chen J, Kim Y et al (2016) Differentiating anthropogenic modification and precipitation-driven change on vegetation productivity on the Mongolian Plateau. Landsc Ecol 31:547–566
Justice CO, Giglio L, Korontzi S et al (2002) An overview of MODIS land data processing and product status. Remote Sens Environ 83:3–15
Kennedy RE, Andréfouët S, Cohen WB et al (2014) Bringing an ecological view of change to Landsat-based remote sensing. Front Ecol Environ 12:339–346
Kottek M, Grieser J, Beck C et al (2006) World maps of Köppen-Geiger climate classification updated. Meteorol Z 15(3):259–263
Krehbiel CP, Zhang X, Henebry GM (2017) Impacts of thermal time on land surface phenology in urban areas. Remote Sens 9(5):499. https://doi.org/10.3390/rs9050499
LeMoine JM, Chen J (2003) Placing our research objectives and results in time and space. Acta Phytoecol Sin 27:1–10
Levin SA (1992) The problem of pattern and scale in ecology: the Robert H. MacArthur award lecture. Ecology 73(6):1943–1967
McFeeters S (1996) The use of Normalized Difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432
Mills K, Schillereff D, Saulnier-Talbot É et al (2017) Deciphering long-term records of natural variability and human impact as recorded in lake sediments: a palaeolimnological puzzle. Wiley Interdiscip Rev Water 4:e1195. https://doi.org/10.1002/wat2.1195
Moon M, Zhang X, Henebry GM et al (2019) Long-term continuity in land surface phenology measurements: a comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products. Remote Sens Environ 226:74–92
Mu Q, Heinsch FA, Zhao M et al (2007) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens Environ 111:519–536
Nagle NN, Buttenfield BP, Leyk S, Spielman S (2014) Dasymetric modeling and uncertainty. Ann Am Assoc Geogr 104(1):80–95
O’Loughlin FE, Paiva RCD, Durand M et al (2016) A multi-sensor approach towards a global vegetation corrected SRTM DEM product. Remote Sens Environ 182:49–59
Omernik JM, Griffith GE (2014) Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environ Manage 54(6):1249–1266
Papale D, Black TA, Carvalhais N et al (2015) Effect of spatial sampling from European flux towers for estimating carbon and water fluxes with artificial neural networks. J Geophys Res Biogeosci 120(10):1941–1957
Petrov A (2012) One hundred years of dasymetric map**: back to the origin. Cartogr J 49(3):256–264
Plourde JD, Pijanowski BC, Pekin BK (2013) Evidence for increased monoculture crop** in the Central United States. Agric Ecosyst Environ 165:50–59
Robinson NP, Allred BW, Smith WK et al (2018) Terrestrial primary production for the conterminous United States derived from Landsat 30 m and MODIS 250 m. Remote Sens Ecol Conserv 4(3):264–280
Rodriguez-Iturbe I, D’Odorico P, Rinaldo A (1998) Configuration entropy of fractal landscapes. Geophys Res Lett 25(7):1015–1018
Roy DP, Wulder MA, Loveland TR et al (2014) Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ 145:154–172
Saunders SC, Chen J, Drummer TD et al (2005) Identifying scales of pattern in ecological data: a comparison of lacunarity, spectral and wavelet analyses. Ecol Complex 2(1):87–105
Schaaf CB, Gao F, Strahler AH et al (2002) First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sens Environ 83(1–2):135–148
Schaetzl RJ, Darden JT, Brandt DS (2009) Michigan geography and geology. Pearson Learning Solutions
Semmens KA, Anderson MC, Kustas WP et al (2016) Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens Environ 185:155–170
Stoorvogel JJ, Bakkenes M, Temme AJAM et al (2017) S-World: a global soil map for environmental modelling. L Degrad Dev 28:22–33
Sun G, Alstad K, Chen J et al (2011) A general predictive model for estimating monthly ecosystem evapotranspiration. Ecohydrology 4(2):245–255
Trlica A, Hutyra LR, Schaaf CL et al (2017) Albedo, land cover, and daytime surface temperature variation across an urbanized landscape. Earth’s Futur 5:1084–1101
Tucker CJ, Pinzon JE, Brown ME et al (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26(20):4485–4498
Turner MG, Gardner RH (2015) Landscape ecology in theory and practice: pattern and process, 2nd edn. Springer, New York, p 482p
Vermote EF, El Saleous NZ, Justice CO (2002) Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sens Environ 83:97–111
Wan Z (2013) MODIS land surface temperature products users’ guide. Institute for Computational Earth System Science. University of California, Santa Barbara, CA
Wang Z, Schaaf CB, Sun Q et al (2017) Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product. Int J Appl Earth Obs Geoinf 59:104–117
Wang Z, Schaaf CB, Sun Q et al (2018) Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote Sens Environ 207:50–64
Wolfe R, Nishihama M, Fleig A et al (2002) Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens Environ 83:31–49
Wu J, Hobbs RJ (eds) (2007) Key topics in landscape ecology. Cambridge University Press, Cambridge, p 297p
Wulder MA, Coops NC, Roy DP et al (2018) Land Cover 2.0. Int J Remote Sens 39(12):4254–4284
Wulder MA, Loveland TR, Roy DP et al (2019) Current status of Landsat program, science, and applications. Remote Sens Environ 224:127–147
**ao X, Hollinger D, Aber J et al (2004) Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens Environ 89:519–534
Yang W, Shabanov NV, Huang D et al (2006) Analysis of leaf area index products from combination of MODIS Terra and Aqua data. Remote Sens Environ 104(3):297–312
Yao Y, Liang S, Li X et al (2017) Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method. J Hydrol 553:508–526
Yuan W, Liu S, Yu G et al (2010) Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens Environ 114(7):1416–1431
Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically map** urban areas from TM imagery. Inl J Remote Sens 24(3):583–594
Zhang X, Friedl MA, Schaaf CB et al (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84(3):471–475
Zhang XY, Liu L, Liu Y et al (2018) Generation and evaluation of the VIIRS land surface phenology product. Remote Sens Environ 216:212–229
Zhang HK, Roy DP (2017) Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification. Remote Sens Environ 197:15–34
Zhao M, Heinsch FA, Nemani RR et al (2005) Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens Environ 95(2):164–176
Zhao M, Running SW, Nemani RR (2006) Sensitivity of moderate resolution imaging spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. J Geophys Res Biogeosci. https://doi.org/10.1029/2004jg000004
Zheng D, Rademacher J, Chen J et al (2004) Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens Environ 93(3):402–411
Zhou H, Liang S, He T et al (2019) Evaluating the spatial representativeness of the MODerate Resolution Image Spectroradiometer albedo product (MCD43) at AmeriFlux sites. Remote Sens 11(5):547. https://doi.org/10.3390/rs11050547
Acknowledgements
This study was supported, in part, by the NASA Carbon Cycle & Ecosystems program (NNX17AE16G), the Great Lakes Bioenergy Research Center funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Numbers DE-SC0018409 and DE-FC02-07ER64494; and the Long-term Ecological Research Program (DEB 1637653) at the Kellogg Biological Station, and the NASA Science of Terra and Aqua program (NNX14AJ32G). We thank the fruitful discussion at LEES Lab meetings where several members made constructive suggestions for model development. Isabel Arroca assisted in formatting the references. The reviews from two anonymous reviewers helped improving the quality of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chen, J., Sciusco, P., Ouyang, Z. et al. Linear downscaling from MODIS to landsat: connecting landscape composition with ecosystem functions. Landscape Ecol 34, 2917–2934 (2019). https://doi.org/10.1007/s10980-019-00928-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10980-019-00928-2