Where can pixel counting area estimates meet user-defined accuracy requirements?

Abramowitz, 1972, vol. 55 Atkinson, 2004, Spatial variation in land cover and choice of spatial resolution for remote sensing, Int. J. Remote Sens., 25, 3687, 10.1080/01431160310001654383 Atkinson, 1995, Defining an optimal size of support for remote sensing investigations, IEEE Trans. Geosci. Remote Sens., 33, 768, 10.1109/36.387592 Atkinson, 1997, Choosing an appropriate spatial resolution for remote sensing investigations, Photogramm. Eng. Remote Sens., 63, 1345 Bartalev, 2016, Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique, Remote Sens. Lett., 7, 269, 10.1080/2150704X.2015.1130874 Blaschke, 2010, Object based image analysis for remote sensing, ISPRS J. Photogramm. Remote Sens., 65, 2, 10.1016/j.isprsjprs.2009.06.004 Boschetti, 2004, Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto Boundary, Remote Sens. Environ., 91, 280, 10.1016/j.rse.2004.02.015 Bracewell, 1989, The Fourier transform, Sci. Am., 260, 86, 10.1038/scientificamerican0689-86 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Card, 1992, Using known map category marginal frequencies to improve estimates of thematic map accuracy, Photogramm. Eng. Remote Sens., 48, 431 Chen, 2016, Impacts of spatial heterogeneity on crop area mapping in Canada using MODIS data, ISPRS J. Photogramm. Remote Sens., 119, 451, 10.1016/j.isprsjprs.2016.07.007 Chen, 2016, Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples, Remote Sens., 8, 601, 10.3390/rs8070601 Chhikara, 1986, Crop acreage estimation using a Landsat-based estimator as an auxiliary variable, IEEE Trans. Geosci. Remote Sens., 157, 10.1109/TGRS.1986.289545 Conese, 1992, Use of error matrices to improve area estimates with maximum likelihood classification procedures, Remote Sens. Environ., 40, 113, 10.1016/0034-4257(92)90009-9 Cushnie, 1987, The interactive effect of spatial resolution and degree of internal variability within land-cover types on classification accuracies, Int. J. Remote Sens., 8, 15, 10.1080/01431168708948612 Czaplewski, 1992, Calibration of remotely sensed proportion or area estimates for misclassification error, Remote Sens. Environ., 39, 29, 10.1016/0034-4257(92)90138-A Czaplewski, 1992, Misclassification bias in areal estimates, Photogramm. Eng. Remote Sens., 52, 189 Defourny, 2006, Respective contribution of yield and area estimates to the error in crop production forecasting Deppe, 1998, Forest area estimation using sample surveys and Landsat MSS and TM data, Photogramm. Eng. Remote Sens., 64, 285 Duveiller, 2010, A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing, Remote Sens. Environ., 114, 2637, 10.1016/j.rse.2010.06.001 European Space Agency, 2012 Eva, 1998, Remote sensing of biomass burning in tropical regions: sampling issues and multisensor approach, Remote Sens. Environ., 64, 292, 10.1016/S0034-4257(98)00006-6 Gallego, 2004, Remote sensing and land cover area estimation, Int. J. Remote Sens., 25, 3019, 10.1080/01431160310001619607 González-Alonso, 1993, Remote sensing and agricultural statistics: crop area estimation through regression estimators and confusion matrices, Int. J. Remote Sens., 14, 1215, 10.1080/01431169308904405 Gonzalez-Alonso, 1997, Remote sensing and agricultural statistics: crop area estimation in north-eastern Spain through diachronic Landsat TM and ground sample data, Int. J. Remote Sens., 18, 467, 10.1080/014311697219213 Hay, 1997, Spatial thresholds, image-objects, and upscaling: a multiscale evaluation, Remote Sens. Environ., 62, 1, 10.1016/S0034-4257(97)81622-7 Hay, 1988, The derivation of global estimates from a confusion matrix, Int. J. Remote Sens., 9, 1395, 10.1080/01431168808954945 Hsieh, 2001, Effect of spatial resolution on classification errors of pure and mixed pixels in remote sensing, IEEE Trans. Geosci. Remote Sens., 39, 2657, 10.1109/36.975000 Huang, 2002, Impact of sensor's point spread function on land cover characterization: assessment and deconvolution, Remote Sens. Environ., 80, 203, 10.1016/S0034-4257(01)00298-X Huang, 2013, ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery, IEEE Trans. Geosci. Remote Sens., 51, 257, 10.1109/TGRS.2012.2202912 Immitzer, 2016, First experience with sentinel-2 data for crop and tree species classifications in Central Europe, Remote Sens., 8, 166, 10.3390/rs8030166 Jacoby, 2000, Loess: a nonparametric, graphical tool for depicting relationships between variables, Elect. Stud., 19, 577, 10.1016/S0261-3794(99)00028-1 Löw, 2014, Defining the spatial resolution requirements for crop identification using optical remote sensing, Remote Sens., 6, 9034, 10.3390/rs6099034 Löw, 2015, Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data, ISPRS J. Photogramm. Remote Sens., 108, 191, 10.1016/j.isprsjprs.2015.07.001 Lambert, 2016, Cropland mapping over Sahelian and Sudanian agrosystems: a knowledge-based approach using PROBA-V time series at 100-m, Remote Sens., 8, 232, 10.3390/rs8030232 Landgrebe, 1977, An empirical study of scanner system parameters, IEEE Trans. Geosci. Electron., 15, 120, 10.1109/TGE.1977.6498969 Latty, 1985, Performance comparisons between information extraction techniques using variable spatial resolution data, Photogramm. Eng. Remote Sens., 51, 1459 Lewis, 2001, A generalized confusion matrix for assessing area estimates from remotely sensed data, Int. J. Remote Sens., 22, 3223, 10.1080/01431160152558332 Li, 2000, Spatial quality evaluation of fusion of different resolution images, Int. Arch. Photogramm. Remote Sens., 33, 339 Lu, 2007, A survey of image classification methods and techniques for improving classification performance, Int. J. Remote Sens., 28, 823, 10.1080/01431160600746456 Müller, 2015, Mining dense Landsat time series for separating cropland and pasture in a heterogeneous Brazilian savanna landscape, Remote Sens. Environ., 156, 490, 10.1016/j.rse.2014.10.014 Mallinis, 2012, Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data, Int. J. Remote Sens., 33, 4408, 10.1080/01431161.2011.648284 Marceau, 1994, Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution, Remote Sens. Environ., 49, 105, 10.1016/0034-4257(94)90047-7 Markham, 1981, Land cover classification accuracy as a function of sensor spatial resolution, 1075 Mayaux, 1995, Estimation of tropical forest area from coarse spatial resolution data: a two-step correction function for proportional errors due to spatial aggregation, Remote Sens. Environ., 53, 1, 10.1016/0034-4257(95)00038-3 Mayaux, 1997, Tropical forest area measured from global land-cover classifications: inverse calibration models based on spatial textures, Remote Sens. Environ., 59, 29, 10.1016/S0034-4257(96)00077-6 McCloy, 2007, Optimizing image resolution to maximize the accuracy of hard classification, Photogramm. Eng. Remote Sens., 73, 893, 10.14358/PERS.73.8.893 Moody, 1994, Scale-dependent errors in the estimation of land-cover proportions. Implications for global land-cover datasets, Photogramm. Eng. Remote Sens., 60 Olofsson, 2013, Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation, Remote Sens. Environ., 129, 122, 10.1016/j.rse.2012.10.031 Olofsson, 2014, Good practices for estimating area and assessing accuracy of land change, Remote Sens. Environ., 148, 42, 10.1016/j.rse.2014.02.015 Ozdogan, 2006, Resolution dependent errors in remote sensing of cultivated areas, Remote Sens. Environ., 103, 203, 10.1016/j.rse.2006.04.004 Pax-Lenney, 1997, The effect of spatial resolution on the ability to monitor the status of agricultural lands, Remote Sens. Environ., 61, 210, 10.1016/S0034-4257(97)00003-5 Potgieter, 2013, Determining crop acreage estimates for specific winter crops using shape attributes from sequential MODIS imagery, Int. J. Appl. Earth Obs. Geoinf., 23, 254 Radoux, 2016, Sentinel-2's potential for sub-pixel landscape feature detection, Remote Sens., 8, 488, 10.3390/rs8060488 Roscher, 2012, I 2 VM: incremental import vector machines, Image Vis. Comput., 30, 263, 10.1016/j.imavis.2012.04.004 Schowengerdt, 2006 Shao, 2010, Mapping cropland and major crop types across the Great Lakes Basin using MODIS-NDVI data, Photogramm. Eng. Remote Sens., 76, 73, 10.14358/PERS.76.1.73 Soares, 2008, Crop area estimate from original and simulated spatial resolution data and landscape metrics, Sci. Agric., 65, 459, 10.1590/S0103-90162008000500003 Sonobe, 2016, An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data, Geocarto Int., 1, 10.1080/10106049.2015.1130085 Stehman, 2005, Comparing estimators of gross change derived from complete coverage mapping versus statistical sampling of remotely sensed data, Remote Sens. Environ., 96, 466, 10.1016/j.rse.2005.04.002 Tokarczyk, 2015, Features, color spaces, and boosting: new insights on semantic classification of remote sensing images, IEEE Trans. Geosci. Remote Sens., 53, 280, 10.1109/TGRS.2014.2321423 Toll, 1985, Effect of Landsat Thematic Mapper sensor parameters on land cover classification, Remote Sens. Environ., 17, 129, 10.1016/0034-4257(85)90069-0 Townshend, 2000, Beware of per-pixel characterization of land cover, Int. J. Remote Sens., 21, 839, 10.1080/014311600210641 Treitz, 1992, Application of detailed ground information to vegetation mapping with high spatial resolution digital imagery, Remote Sens. Environ., 42, 65, 10.1016/0034-4257(92)90068-U Tsiligirides, 1998, Remote sensing as a tool for agricultural statistics: a case study of area frame sampling methodology in Hellas, Comput. Electron. Agric., 20, 45, 10.1016/S0168-1699(98)00011-8 Turner, 1989, Effects of changing spatial scale on the analysis of landscape pattern, Landsc. Ecol., 3, 153, 10.1007/BF00131534 Vinciková, 2010, Timely identification of agricultural crops in the Temelín NPP vicinity using satellite data in the event of radiation contamination, J. Agrobiol., 27, 73, 10.2478/s10146-009-0014-z Vintrou, 2012, Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products, Int. J. Appl. Earth Obs. Geoinf., 14, 83 Voisin, 2014, Supervised classification of multisensor and multiresolution remote sensing images with a hierarchical copula-based approach, IEEE Trans. Geosci. Remote Sens., 52, 3346, 10.1109/TGRS.2013.2272581 Waldner, 2015, Operational monitoring of the Desert Locust habitat with Earth Observation: an assessment, ISPRS Int. J. Geo-Inf., 4, 2379, 10.3390/ijgi4042379 Waldner, 2016, Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity, Int. J. Remote Sens., 37, 3196, 10.1080/01431161.2016.1194545 Wall, 1984, Landsat-based inventory system for agriculture in California, Remote Sens. Environ., 14, 267, 10.1016/0034-4257(84)90021-X Wang, 2017, The effect of the point spread function on sub-pixel mapping, Remote Sens. Environ., 193, 127, 10.1016/j.rse.2017.03.002 Wardlow, 2010, A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas, Int. J. Remote Sens., 31, 805, 10.1080/01431160902897858 Williams, 1989, volume112 Williams, 1984, A statistical evaluation of the advantages of Landsat Thematic Mapper data in comparison to Multispectral Scanner data, IEEE Trans. Geosci. Remote Sens., 294, 10.1109/TGRS.1984.350624 Woodcock, 1987, The factor of scale in remote sensing, Remote Sens. Environ., 21, 311, 10.1016/0034-4257(87)90015-0 Wu, 2002, Empirical patterns of the effects of changing scale on landscape metrics, Landsc. Ecol., 17, 761, 10.1023/A:1022995922992 Wu, 2004, Effects of changing scale on landscape pattern analysis: scaling relations, Landsc. Ecol., 19, 125, 10.1023/B:LAND.0000021711.40074.ae Yang, 2007, Using high resolution QuickBird imagery for crop identification and area estimation, Geocarto Int., 22, 219, 10.1080/10106040701204412 Zhu, 2016, Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative, ISPRS J. Photogramm. Remote Sens., 122, 206, 10.1016/j.isprsjprs.2016.11.004