The Estimation of Gross Primary Productivity over North Korea Using MODIS FPAR and WRF Meteorological Data

Abstract

: NASA MODIS GPP provides a useful tool to monitor global terrestrial vegetation productivity. Two major problems of NASA GPP in regional applications are coarse spatial resolution (1.25˚ 1˚) of DAO meteorological data and cloud contamination of MODIS FPAR product. In this study, we improved the NASA GPP by using enhanced input data of high spatial resolution (3 km 3 km) WRF meteorological data and cloud-corrected FPAR over the North Korea. ), FPAR enhancement increased GPP (861) but utilization of WRF data decreased GPP (710). Enhancements of both FPAR and meteorological input resulted in GPP increase (809) and the improvement was the greatest for mixed forest regions (+10.2%). The improved GPP showed better spatial heterogeneity reflecting local topography due to high resolution WRF data. It is remarkable that the improved and NASA GPPs showed distinctly different interannual variations with each other. Our study indicates improvement of NASA GPP by enhancing input variables is necessary to monitor region-scale terrestrial vegetation productivity.

Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data

“…The MODIS07_L2 atmosphere profiles product covers the entire globe at 1-2 day intervals, and produces various atmosphere variables, including T a , at 5 km spatial resolution. The temperature variables derived from MODIS07_L2 product have been widely used to estimate surface radiation budget parameters, evapotranspiration, and plant productivity in previous applications [31][32][33][34][35][36][37][38]. However, the MODIS IR and temperature retrievals are significantly degraded by atmosphere aerosol, smoke and cloud cover contamination, which hampers terrestrial monitoring applications.…”

Comparisons of Collection 5 and 6 Aqua MODIS07_L2 air and Dew Temperature Products with Ground-Based Observation Dataset

“…(Seemann et al, 2006;Borbas et al, 2011). MODIS 로부터 생산된 기온과 이슬점온도 프로파일 정보는 지 표 수준의 미기상 요소 추정연구 (Urban et al, 2013;Hill, 2013;Rhee and Im, 2014;Williamson et al, 2014), 대기의 연직구조 및 안정도 연구 (Park et al, 2006;Kim and Kwon, 2007;Mitra et al, 2012), 지표복사수지 산출 (Bisht et al, Korean Journal of Remote Sensing, Vol.30, No.5, 2014 -5 72- 2005; Ryu et al, 2008;Jang et al, 2009;Bisht and Bras, 2010), 육상 증발산 추정 (Batra et al, 2006;Jeong et al, 2009;Jang et al, 2010;Jang et al, 2013) 및 식생 일차생 산성 추정연구 (Lee et al, 2011;Ryu et al, 2011;Do et al, 2012 …”