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TitleEffects of subpixel water area fraction on mapping leaf area index and modeling net primary productivity in Canada
AuthorXu, SORCID logo; Chen, J M; Fernandes, RORCID logo; Cihlar, J
SourceRemote sensing for water resources studies; by Gong, P (ed.); Chen, J M (ed.); Canadian Journal of Remote Sensing vol. 30, no. 5, 2004 p. 797-804,
Alt SeriesEarth Sciences Sector, Contribution Series 2005580
PublisherInforma UK Limited
Mediapaper; on-line; digital
File formatpdf
ProvinceCanada; British Columbia; Alberta; Saskatchewan; Manitoba; Ontario; Quebec; New Brunswick; Nova Scotia; Prince Edward Island; Newfoundland and Labrador; Northwest Territories; Yukon; Nunavut
NTS1; 2; 3; 10; 11; 12; 13; 14; 15; 16; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 52; 53; 54; 55; 56; 57; 58; 59; 62; 63; 64; 65; 66; 67; 68; 69; 72; 73; 74; 75; 76; 77; 78; 79; 82; 83; 84; 85; 86; 87; 88; 89; 92; 93; 94; 95; 96; 97; 98; 99; 102; 103; 104; 105; 106; 107; 114O; 114P; 115; 116; 117; 120; 340; 560
Lat/Long WENS-141.0000 -50.0000 90.0000 41.7500
Subjectsremote sensing; mapping techniques; modelling; ecosystems; surface waters; Data processing
Illustrationsgraphs; remote sensing images; tables
Released2014 06 02
AbstractRetrieving biophysical parameters, such as the leaf area index (LAI) and the net primary productivity (NPP), from remote sensing imagery is very useful for modeling terrestrial ecosystems. Spatial heterogeneity of the land surface often leads to biases in the retrieved parameters when algorithms derived based on high-resolution images are directly used for coarse-resolution images. In the northern environment in Canada, open water bodies are one of the major features of surface heterogeneity, and in remote sensing images a considerable number of land pixels are mixed with open water bodies of different fractions. In an image of Canada at 1 km resolution, there are 47% water-containing land pixels, and their average water area fraction is 14%. In this article, we investigate the effects of subpixel water area fraction on LAI retrieval and NPP estimation for all land areas in Canada. A linear mixture model was developed to use the available subpixel information for this purpose. Previous Canada-wide LAI and NPP maps at 1 km resolution derived without considering the subpixel information were used for comparison. The following conclusions are drawn from this investigation: (i) LAI retrieval errors are proportional to water area fraction in a pixel, and on average for all of Canada's land mass the retrieved LAI per unit land area without considering the open water effect is 13% smaller than the correct LAI; (ii) the influence of the subpixel water area on LAI retrieval is the largest for the conifer cover type and the smallest for the deciduous cover type among forests; and (iii) because of the LAI bias, NPP per unit land area is also negatively biased by 9% when the subpixel water effect is not considered. The annual Canada-wide NPP summed from the land portion calculated using the water-corrected LAI is only 0.69% higher than that summed from the total pixel area calculated using LAI without the water correction. However, the difference in these two NPP values can be either positive or negative among different provinces and territories, depending on the water area fraction and its distribution.

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