PE&RS February 2018 Full - page 99

complex Earth’s observation models should be explored for
improving the upscaling algorithm.
Acknowledgments
The authors would like to thank the anonymous reviewers for
their helpful suggestions and insightful comments that greatly
improved our manuscript. Partial funding was provided by
the New Hampshire Agricultural Experiment Station. This is
Scientific Contribution Number 2750. This work was support-
ed by the
USDA
National Institute of Food and Agriculture Mc-
Intire Stennis Project #NH00077-M (Accession #1002519). We
thank National Agricultural Statistics Service (
NASS
) Cropland
Data for providing
CDL
data. We also thank China Scholarship
Council (
CSC
) for providing foundation for the first author as a
visiting researcher in the Basic and Applied Spatial Analysis
Lab (
BASAL
), Department of Natural Resources and the Envi-
ronment, University of New Hampshire, under the direction
of Dr. Russell G. Congalton.
References
Bai, Yan, M. Feng, H. Jiang, J. Wang, Y. Zhu, and Y. Liu, 2014.
Assessing consistency of five global land cover data sets in
China,
Remote Sensing
, 6 (9):8739–8759.
Bartholomé, E., and A.S. Belward, 2005. GLC2000: A new approach
to global land cover mapping from Earth observation data,
International Journal of Remote Sensing
, 26(9):1959–77.
Bian, L., and R. Butler, 1999. Comparing effects of aggregation
methods on statistical and spatial properties of simulated
spatial data,
Photogrammetric Engineering and Remote Sensing
,
65(1):73–84.
Boryan, C., Z. Yang, R. Mueller, and M. Craig, 2011. Monitoring
US agriculture: The US Department of Agriculture, National
Agricultural Statistics Service, Cropland Data Layer Program,
Geocarto International
, 26(5):341–58.
Boucher, A., P.C. Kyriakidis, and C. Cronkite-Ratcliff, 2008.
Geostatistical Solutions for Super-Resolution Land Cover
Mapping,
IEEE Transactions on Geoscience and Remote Sensing
,
46 (1):272–283.
Boucher, A., 2009. Sub-pixel mapping of coarse satellite remote
sensing images with stochastic simulations from training images,
Mathematical Geosciences
, 41(3):265–90.
Boucher, A., and P.C. Kyriakidis, 2006. Super-resolution land
cover mapping with indicator geostatistics,
Remote Sensing of
Environment
, 104(3):264–82.
Chen, Y., X. Song, S. Wang, J. Huang, and L.R. Mansaray, 2016.
Impacts of spatial heterogeneity on crop area mapping in Canada
using MODIS data,
ISPRS Journal of Photogrammetry and
Remote Sensing
, 119:451–61.
Congalton, R.G., J. Gu, K. Yadav, P. Thenkabail, and M. Ozdogan,
2014. Global land cover mapping: A review and uncertainty
analysis,
Remote Sensing
, 6 (12):12070–12093.
Curran, P.J., and P.M. Atkinson, 1998. Geostatistics and remote
sensing,
Progress in Physical Geography
, 22(1):61–78.
Dong, M., B.A. Bryan, J.D. Connor, M. Nolan, and L. Gao, 2015.
Land use mapping error introduces strongly-localised, scale-
dependent uncertainty into land use and ecosystem services
modelling,
Ecosystem Services
, 15, 63-74.
Feddema, J.J., K.W. Oleson, G.B. Bonan, L.O. Mearns, L.E. Buja,
G.A. Meehl, and W.M. Washington, 2005. The importance of
land-cover change in simulating future climates,
Science
, 310
(5754):1674–1678.
Fortin, M., P.M.A. James, A. MacKenzie, S.J. Melles, and B. Rayfield,
2012. Spatial statistics, spatial regression, and graph theory in
ecology,
Spatial Statistics
, 1:100-109.
Friedl, M.A., D. Sulla-Menashe, B. Tan, A. Schneider, N. Ramankutty,
A. Sibley, and X. Huang, 2010. MODIS collection 5 global land
cover: Algorithm refinements and characterization of new
datasets,
Remote Sensing of Environment
, 114(1):168–82.
Frohn, R.C., 1997.
Remote Sensing for Landscape Ecology: New
metric Indicators for Monitoring, Modeling, and Assessment of
Ecosystems
, CRC Press, Boca Raton, Florida.
Frohn, R.C., and Y. Hao, 2006. Landscape metric performance in
analyzing two decades of deforestation in the Amazon basin of
Rondonia, Brazil,
Remote Sensing of Environment
, 100 (2):237–251.
Fuchs, R., M. Herold, P.H. Verburg, J.G.P.W. Clevers, and J. Eberle,
2015. Gross changes in reconstructions of historic land cover/
use for Europe between 1900 and 2010,
Global Change Biology
,
21(1):299–313.
Gao, F., M.C. Anderson, X. Zhang, Z. Yang, J.G. Alfieri, W.P. Kustas, R.
Mueller, D.M. Johnson, and J.H. Prueger, 2017. Toward mapping
crop progress at field scales through fusion of Landsat and
MODIS imagery,
Remote Sensing of Environment
, 188:9–25.
Gardner, R.H., T.R. Lookingbill, P.A. Townsend, and J. Ferrari, 2008.
A new approach for rescaling land cover data,
Landscape
Ecology
, 23(5):513–26.
Ge, Y., Y. Jiang, Y. Chen, A. Stein, D. Jiang, and Y. Jia, 2016. Designing
an experiment to investigate subpixel mapping as an alternative
method to obtain land use/land cover maps,
Remote Sensing
, 8
(5):360.
Gong, P., J. Wang, L. Yu, Y. Zhao, Y. Zhao, L. Liang, and Z. Niu, 2013.
Finer resolution observation and monitoring of global land
cover: First mapping results with Landsat TM and ETM+ data,
International Journal of Remote Sensing
, 34 (7):2607–2654.
Gonzales, D.S., and S.W. Searcy, 2017. GIS-based allocation of
herbaceous biomass in biorefineries and depots,
Biomass and
Bioenergy
, 97:1–10.
Grafius, D.R., R. Corstanje, P.H. Warren, K.L. Evans, S. Hancock, and
J.A. Harris, 2016. The impact of land use/land cover scale on
modelling urban ecosystem services,
Landscape Ecology
, 31
(7):1509–1522.
Hay, G.J., D.J. Marceau, P. Dube, and A. Bouchard, 2001. A multiscale
framework for landscape analysis: Object-specific analysis and
upscaling,
Landscape Ecology
, 16(6):471–490.
Hansen, M.C., and T.R. Loveland, 2012. A review of large area
monitoring of land cover change using Landsat data,
Remote
Sensing of Environment
, 122:66–74.
He, H., S.J. Ventura, and D.J. Mladenoff, 2002. Effects of spatial
aggregation approaches on classified satellite imagery,
International Journal of Geographical Information Science
, 16
(1):93–109.
Inglada, J., A. Vincent, M. Arias, B. Tardy, D. Morin, and I. Rodes,
2017. Operational high resolution land cover map production
at the country scale using satellite image time series,
Remote
Sensing
, 9 (1):95.
Journel, A.G., 2002. Combining Knowledge from Diverse Sources:
An Alternative to Traditional Data Independence Hypotheses.
Mathematical Geology
, 34 (5):573–596.
Ju, J., S. Gopal, and E.D. Kolaczyk, 2005. On the choice of spatial and
categorical scale in remote sensing land cover classification.
Remote Sensing of Environment
, 96 (1):62–77.
Kitron, U., J.A. Clennon, M.C. Cecere, R.E. Gürtler, C.H. King, and G.
Vazquez-Prokopec, 2006. Upscale or downscale: Applications of
fine scale remotely sensed data to Chagas disease in Argentina
and schistosomiasis in Kenya,
Geospatial Health
, 1(1):49–58.
Lam, N.S., 1990. Description and measurement of Landsat TM
images using fractals,
Photogrammetric Engineering and Remote
Sensing
, 56(2):187–195.
Lechner, A.M., K.J. Reinke, Y. Wang, and L. Bastin, 2013. Interactions
between landcover pattern and geospatial processing methods:
Effects on landscape metrics and classification accuracy,
Ecological Complexity
, 15:71–82.
Ling, F., Y. Du, X. Li, Y. Zhang, F. Xiao, S. Fang, and W. Li, 2014.
Super resolution land cover mapping with multiscale
information by fusing local smoothness prior and downscaled
coarse fractions,
IEEE Transactions on Geoscience and Remote
Sensing
, 52(9):5677–92.
Lu, D., and Q. Weng, 2007. A survey of image classification methods
and techniques for improving classification performance,
International Journal of Remote Sensing
, 28 (5):823–70.
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