Soil respiration mapped by exclusively use of MODIS data for forest landscapes of Saskatchewan,Canada |
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| Affiliation: | 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;2. Department of Geography, University of Toronto, 100 St. George St., Toronto, ON, Canada;3. Biometeorology Research Laboratory, Vancouver Island University, Nanaimo, BC, Canada;4. Faculty of Land and Food Systems, University of British Columbia, Vancouver, BC, Canada;1. Ookusa Animal Clinic, Ookusa 503, Matsue, Shimane 690-0032, Japan;2. Dian Fossey Gorilla Fund International, Atlanta, USA;3. Department of Environmental Sciences and Environmental Health, Emory University and Rollins School of Public Health, Atlanta, USA;4. Division of Pathobiological Analysis, Department of Veterinary Pathobiology, School of Veterinary Medicine, Nippon Veterinary and Life Science University, Tokyo, Japan;1. Department of Electronics & Communications, BLDEA''s Dr. P.G.Halakatti CET, Vijayapur, Karnataka, India;2. Department of Electronics & Communications, Basaveshwar Engineering College, Bagalkot, Karnataka, India;1. Department of Radiotherapy, Ghent University Hospital, Gent, Belgium;2. Department of Radiation Oncology, University Hospital Gasthuisberg, Leuven, Belgium;3. Belgian Hadron Therapy Center Foundation, Brussels, Belgium |
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| Abstract: | Soil respiration (Rs) is of great importance to the global carbon balance. Remote sensing of Rs is challenging because of (1) the lack of long-term Rs data for model development and (2) limited knowledge of using satellite-based products to estimate Rs. Using 8-years (2002–2009) of continuous Rs measurements with nonsteady-state automated chamber systems at a Canadian boreal black spruce stand (SK-OBS), we found that Rs was strongly correlated with the product of the normalized difference vegetation index (NDVI) and the nighttime land surface temperature (LSTn) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The coefficients of the linear regression equation of this correlation between Rs and NDVI × LSTn could be further calibrated using the MODIS leaf area index (LAI) product, resulting in an algorithm that is driven solely by remote sensing observations. Modeled Rs closely tracked the seasonal patterns of measured Rs and explained 74–92% of the variance in Rs with a root mean square error (RMSE) less than 1.0 g C/m2/d. Further validation of the model from SK-OBS site at another two independent sites (SK-OA and SK-OJP, old aspen and old jack pine, respectively) showed that the algorithm can produce good estimates of Rs with an overall R2 of 0.78 (p < 0.001) for data of these two sites. Consequently, we mapped Rs of forest landscapes of Saskatchewan using entirely MODIS observations for 2003 and spatial and temporal patterns of Rs were well modeled. These results point to a strong relationship between the soil respiratory process and canopy photosynthesis as indicated from the greenness index (i.e., NDVI), thereby implying the potential of remote sensing data for detecting variations in Rs. A combination of both biological and environmental variables estimated from remote sensing in this analysis may be valuable in future investigations of spatial and temporal characteristics of Rs. |
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| Keywords: | Soil respiration Forest Soil temperature Remote sensing MODIS NDVI Land surface temperature |
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