As an important product of Moderate Resolution Imaging Spectroradiometer(MODIS), MOD17A2 provides dramatic improvements in our ability to accurately and continuously monitor global terrestrial primary production, which is also significant in effort to advance scientific research and eco-environmental management. Over the past decades, forests have moderated climate change by sequestrating about one-quarter of the carbon emitted by human activities through fossil fuels burning and land use/land cover change. Thus, the carbon uptake by forests reduces the rate at which carbon accumulates in the atmosphere. However, the sensitivity of near real-time MODIS gross primary productivity(GPP) product is directly constrained by uncertainties in the modeling process, especially in complicated forest ecosystems. Although there have been plenty of studies to verify MODIS GPP with ground-based measurements using the eddy covariance(EC) technique, few have comprehensively validated the performance of MODIS estimates(Collection 5) across diverse forest types. Therefore, the present study examined the degree of correspondence between MODIS-derived GPP and EC-measured GPP at seasonal and interannual time scales for the main forest ecosystems, including evergreen broadleaf forest(EBF), evergreen needleleaf forest(ENF), deciduous broadleaf forest(DBF), and mixed forest(MF) relying on 16 flux towers with a total of 68 site-year datasets. Overall, site-specific evaluation of multi-year mean annual GPP estimates indicates that the current MOD17A2 product works highly effectively for MF and DBF, moderately effectively for ENF, and ineffectively for EBF. Except for tropical forest, MODIS estimates could capture the broad trends of GPP at 8-day time scale for all other sites surveyed. On the annual time scale, the best performance was observed in MF, followed by ENF, DBF, and EBF. Trend analyses also revealed the poor performance of MODIS GPP product in EBF and DBF. Thus, improvements in the sensitivity of MOD17A2 to forest productivity r
The water quality of lakes can be degraded by excessive riverine nutrients.Riverine water quality generally varies depending on region and season because of the spatiotemporal variations in natural factors and anthropogenic activities.Monthly water quality measurements of eight water quality variables were analyzed for two years at 16 sites of the Tianmuhu watershed.The variables were examined using hierarchical cluster analysis(HCA) and factor analysis/principal component analysis(FA/PCA) to reveal the spatiotemporal variations in riverine nutrients and to identify their potential sources.HCA revealed three geographical groups and three periods.Two drainages comprising towns and large villages were the most polluted, six drainages comprising widely distributed tea plantations and orchards were moderately polluted, and eight drainages without the factors were the least polluted.The river was most polluted in June when the first heavy rain(daily rainfall > 50 mm) occurs after fertilization and the number of rainy days is most(monthly number of rainy days > 20 days).Moderate pollution was observed from October to May, during which morethan 60% of the total nitrogen fertilizer and all of the phosphorus fertilizer are applied to the cropland, the total manure is applied to tea plantations and orchards, and a monthly rainfall ranging from 0 mm to 164 mm occurs.The remaining months were characterized by frequent raining(i.e., number of rainy days per month ranged from 5 to 24) and little use of fertilizers, and were thus least polluted.FA/PCA identified that the greatest pollution sources were the runoff from tea plantations and orchards,domestic pollution and the surface runoff from towns and villages, and rural sewage, which had extremely high contributions of riverine nitrogen, phosphorus,and chemical oxygen demand, respectively.The tea plantations and orchards promoted by the agricultural comprehensive development(ACD) were not environmentally friendly.Riverine nitrogen is a major water pollution parameter in hilly
NIE Xiao-feiLI Heng-pengJIANG Jia-huDIAO Ya-qinLI Peng-cheng
Achieving water purity in Poyang Lake has become a major concern in recent years, thus appropriate evaluation of spatial and temporal water quality variations has become essential. Variations in 11 water quality parameters from 15 sampling sites in Poyang Lake were investigated from 2009 to 2012. An integrative fuzzy variable evaluation(IFVE) model based on fuzzy theory and variable weights was developed to measure variations in water quality. Results showed that: 1) only chlorophyll-a concentration and Secchi depth differed significantly among the 15 sampling sites(P < 0.01), whereas the 11 water quality parameters under investigation differed significantly throughout the seasons(P < 0.01). The annual variations of all water quality variables except for temperature, electrical conductivity, suspended solids and total phosphorus were considerable(P < 0.05). 2) The IFVE model was reasonable and flexible in evaluating water quality status and any possible ′bucket effect′. The model fully considered the influences of extremely poor indices on overall water quality. 3) A spatial analysis indicated that anthropogenic activities(particularly industrial sewage and dredging) and lake bed topography might directly affect water quality in Poyang Lake. Meanwhile, hydrological status and sewage discharged into the lake might be responsible for seasonal water quality variations.
LI BingYANG GuishanWAN RongrongZHANG LuZHANG YanhuiDAI Xue
