کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6346818 | 1621259 | 2014 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Estimating landscape net ecosystem exchange at high spatial-temporal resolution based on Landsat data, an improved upscaling model framework, and eddy covariance flux measurements
ترجمه فارسی عنوان
برآورد تبادل اکوسیستم چشم انداز در تفکیک فضایی و طول عمر بالا بر اساس داده های لندست، یک چارچوب مدل پیشرفته ارتقاء داده شده و اندازه گیری شار کوادیاری
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
تبادل اکوسیستم خالص ادی کوواریانس، درخت رگرسیون، ترکیب تصویر، هواشناسی ردیابی، بالا بردن
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
چکیده انگلیسی
More accurate estimation of the carbon dioxide flux depends on the improved scientific understanding of the terrestrial carbon cycle. Remote-sensing-based approaches to continental-scale estimation of net ecosystem exchange (NEE) have been developed but coarse spatial resolution is a source of errors. Here we demonstrate a satellite-based method of estimating NEE using Landsat TM/ETM + data and an upscaling framework. The upscaling framework contains flux-footprint climatology modeling, modified regression tree (MRT) analysis and image fusion. By scaling NEE measured at flux towers to landscape and regional scales, this satellite-based method can improve NEE estimation at high spatial-temporal resolution at the landscape scale relative to methods based on MODIS data with coarser spatial-temporal resolution. This method was applied to sixteen flux sites from the Canadian Carbon Program and AmeriFlux networks located in North America, covering forest, grass, and cropland biomes. Compared to a similar method using MODIS data, our estimation is more effective for diagnosing landscape NEE with the same temporal resolution and higher spatial resolution (30 m versus 1 km) (r2 = 0.7548 vs. 0.5868, RMSE = 1.3979 vs. 1.7497 g C mâ 2 dayâ 1, average error = 0.8950 vs. 1.0178 g C mâ 2 dayâ 1, relative error = 0.47 vs. 0.54 for fused Landsat and MODIS imagery, respectively). We also compared the regional NEE estimations using Carbon Tracker, our method and eddy-covariance observations. This study demonstrates that the data-driven satellite-based NEE diagnosed model can be used to upscale eddy-flux observations to landscape scales with high spatial-temporal resolutions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 141, 5 February 2014, Pages 90-104
Journal: Remote Sensing of Environment - Volume 141, 5 February 2014, Pages 90-104
نویسندگان
Dongjie Fu, Baozhang Chen, Huifang Zhang, Juan Wang, T. Andy Black, Brian D. Amiro, Gil Bohrer, Paul Bolstad, Richard Coulter, Abdullah F. Rahman, Allison Dunn, J. Harry McCaughey, Tilden Meyers, Shashi Verma,