Dailiang Peng

Assessing photosynthetic light-use efficiency using a solar-induced chlorophyll fluorescence and photochemical reflectance index

Photosynthetic light-use efficiency (LUE) is an important indicator of plant photosynthesis, but assessment by remote sensing needs to be further explored. In this study, two protective mechanisms for photosynthesis, chlorophyll fluorescence (ChlF) and heat dissipation in the deep oxidation state of the xanthophyll cycle, were explored to estimate photosynthetic LUE from canopy radiance spectra. Four independent experiments were carried out on summer maize (C4 plant) on 5 July 2008, and winter wheat (C3 plant) on 18 April 2008, 16 April 2010, and 13 May 2010, by synchronously measuring daily canopy radiance spectra and photosynthetic LUE. The competitive relationship between ChlF and photochemical yield made it possible to estimate photosynthetic LUE. LUE–ChlF models were developed for ChlF at 688 nm (R 2 = 0.72) and 760 nm (R 2 = 0.59) based on the experimental data from 13 May 2010 at the Guantao flux site. The LUE–ChlF models were validated by three independent experiments, and the results showed that the LUE–ChlF relationship was weakened, possibly by variation in species, canopy density, and environmental conditions. As an easy, rapid, non-intrusive method, a photochemical reflectance index (PRI) provides an instantaneous assessment of dynamic photosynthetic LUE. The significant negative relationship between non-photochemical quenching and PRI was confirmed. Although there was a significantly positive relationship between LUE and PRI in all four independent experiments, this was evidently weakened by the canopy and environmental conditions. Difference in PRI (ΔPRI) from the minimum reference PRI around noontime can largely eliminate interference factors. The LUE–ΔPRI model was developed based on experimental data from 13 May 2010 at the Guantao flux site (with an R 2 value of 0.85), and validated by the three other independent experiments. The validation result indicated that different species can markedly affect the precision of the PRI difference method.

Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data

We developed a sophisticated method to depict the spatial and seasonal characterization of net primary productivity (NPP) and climate variables. The role of climate variability in the seasonal variation of NPP exerts delayed and continuous effects. This study expands on this by mapping the seasonal characterization of NPP and climate variables from space using geographic information system (GIS) technology at the pixel level. Our approach was developed in southeastern China using moderate-resolution imaging spectroradiometer (MODIS) data. The results showed that air temperature, precipitation and sunshine percentage contributed significantly to seasonal variation of NPP. In the northern portion of the study area, a significant positive 32-d lagged correlation was observed between seasonal variation of NPP and climate (P<0.01), and the influences of changing climate on NPP lasted for 48 d or 64 d. In central southeastern China, NPP showed 16-d, 48-d, and 96-d lagged correlation with air temperature, precipitation, and sunshine percentage, respectively (P<0.01); the influences of air temperature and precipitation on NPP lasted for 48 d or 64 d, while sunshine influence on NPP only persisted for 16 d. Due to complex topography and vegetation distribution in the southern part of the study region, the spatial patterns of vegetation-climate relationship became complicated and diversiform, especially for precipitation influences on NPP. In the northern part of the study area, all vegetation NPP had an almost similar response to seasonal variation of air temperature except for broad crops. The impacts of seasonal variation of precipitation and sunshine on broad and cereal crop NPP were slightly different from other vegetation NPP.

Assessing the Response of Seasonal Variation of Net Primary Productivity to Climate Using Remote Sensing Data and Geographic Information System Techniques in Xinjiang

Net primary productivity (NPP) is a key component of energy and matter transformation in the terrestrial ecosystem, and the responses of NPP to global change locally and regionally have been one of the most important aspects in climate-vegetation relationship studies. In order to isolate causal climatic factors, it is very important to assess the response of seasonal variation of NPP to climate. In this paper, NPP in Xinjiang was estimated by NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) data and geographic information system (GIS) techniques. The impact of climatic factors (air temperature, precipitation and sunshine percentage) on seasonal variations of NPP was studied by time lag and serial correlation ageing analysis. The results showed that the NPP for different land cover types have a similar correlation with any one of the three climatic factors, and precipitation is the major climatic factor influencing the seasonal variation of NPP in Xinjiang. It was found that the positive correlation at 0 lag appeared between NPP and precipitation and the serial correlation ageing was 0 d in most areas of Xinjiang, which indicated that the response of NPP to precipitation was immediate. However, NPP of different land cover types showed significant positive correlation at 2 month lag with air temperature, and the impact of which could persist 1 month as a whole. No correlation was found between NPP and sunshine percentage.

Monitoring the distribution of C3 and C4 grasses in a temperate grassland in northern China using moderate resolution imaging spectroradiometer normalized difference vegetation index trajectories

Using remote-sensing technologies, this study sought to provide an up-to-date map of C3/C4 distribution representative of temperate grassland in northern China. Several studies focused on the central grasslands of North America have demonstrated that C4 species coverage can be discriminated from C3 species by using time-series vegetation index data based on their phenological differences. Considering that the hydrothermal patterns and C4 percentage of grass flora in the study area and North America are different, we first examined temporal features of C3/C4 communities by using multitemporal moderate resolution imaging spectroradiometer normalized difference vegetation index data throughout the 2010 growing season. It was found that the asynchronous seasonality exhibited by communities with varied C3/C4 compositions also existed in our study region. Based on this asynchrony and separation rate, a hierarchical decision tree was developed to classify four grassland types with varied C3/C4 compositions. As a result, a classification map of the mixed C3/C4 grassland was generated with an overall accuracy of 87.3% and a kappa coefficient of 0.83. The geographic distribution of C3 and C4 species showed that the study area was dominated by C3 grasses, but C4-dominated grasslands accounted for 39% of the land cover. Thus C4 species also made an important contribution to grassland biomass, especially in dry and low-lying saline-alkaline habitats. The results also indicated that the trajectory-based methods for C3/C4 mapping rooted in asynchronous seasonality worked effectively in the climate regimes of both northern China and North America.

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

Monitoring nitrogen concentration of oilseed rape from hyperspectral data using radial basis function

Remote sensing technology is the important tool of digital earth, it can facilitate nutrient management in sustainable cropping systems. In the study, two types of radial basis function (RBF) neural network approaches, the standard radial basis function (SRBF) neural networks and the modified type of RBF, generalized regression neural networks (GRNN), were investigated in estimating the nitrogen concentrations of oilseed rape canopy using vegetation indices (VIs) and hyperspectral reflectance. Comparison analyses were performed to the spectral variables and the approaches. The Root Mean Square Error (RMSE) and determination coefficients (R2) were used to assess their predictability of nitrogen concentrations. For all spectral variables (VIs and hyperspectral reflectance), the GRNN method produced more accurate estimates of nitrogen concentrations than did the SRBF method at all ranges of nitrogen concentrations, and the better agreements between the measured and the predicted nitrogen concentration were obtained with the GRNN method. This indicated that the GRNN method is prior to the SRBF method in estimation of nitrogen concentrations. Among the VIs, the Modified Chlorophyll Absorption in Reflectance Index (MCARI), MCARI1510, and Transformed Chlorophyll Absorption in Reflectance Index are better than the others in estimating oilseed rape canopy nitrogen concentrations. Compared to the results from VIs, the hyperspectral reflectance data also gave an acceptable estimation. The study showed that nitrogen concentrations of oilseed rape canopy could be monitored using remotely sensed data and the RBF method, especially the GRNN method, is a useful explorative tool for oilseed rape nitrogen concentration monitoring when applied on hyperspectral data.

A new quantitative model of ecological compensation based on ecosystem capital in Zhejiang Province, China.

Ecological compensation is becoming one of key and multidiscipline issues in the field of resources and environmental management. Considering the change relation between gross domestic product (GDP) and ecological capital (EC) based on remote sensing estimation, we construct a new quantitative estimate model for ecological compensation, using county as study unit, and determine standard value so as to evaluate ecological compensation from 2001 to 2004 in Zhejiang Province, China. Spatial differences of the ecological compensation were significant among all the counties or districts. This model fills up the gap in the field of quantitative evaluation of regional ecological compensation and provides a feasible way to reconcile the conflicts among benefits in the economic, social, and ecological sectors.

Country-level net primary production distribution and response to drought and land cover change.

Carbon sequestration by terrestrial ecosystems can offset emissions and thereby offers an alternative way of achieving the target of reducing the concentration of CO2 in the atmosphere. Net primary production (NPP) is the first step in the sequestration of carbon by terrestrial ecosystems. This study quantifies moderate-resolution imaging spectroradiometer (MODIS) NPP from 2000 to 2014 at the country level along with its response to drought and land cover change. Our results indicate that the combined NPP for 53 countries represents >90% of global NPP. From 2000 to 2014, 29 of these 53 countries had increasing NPP trends, most notably the Central African Republic (23gC/m2/y). The top three and top 12 countries accounted for 30% and 60% of total global NPP, respectively, whereas the mean national NPP per unit area in the countries with the 12 lowest values was only around ~300gC/m2/y - the exception to this was Brazil, which had an NPP of 850gC/m2/y. Large areas of Russia, Argentina, Peru and several countries in southeast Asia showed a marked decrease in NPP (~15gC/m2/y). About 37% of the NPP decrease was caused by drought while ~55% of NPP variability was attributed to changes in water availability. Land cover change explained about 20% of the NPP variability. Our findings support the idea that government policies should aim primarily to improve water management in drought-afflicted countries; land use/land cover change policy could also be used as an alternative method of increasing NPP.

The Influences of Drought and Land-Cover Conversion on Inter-Annual Variation of NPP in the Three-North Shelterbelt Program Zone of China Based on MODIS Data

Terrestrial ecosystems greatly contribute to carbon (C) emission reduction targets through photosynthetic C uptake.Net primary production (NPP) represents the amount of atmospheric C fixed by plants and accumulated as biomass. The Three-North Shelterbelt Program (TNSP) zone accounts for more than 40% of China’s landmass. This zone has been the scene of several large-scale ecological restoration efforts since the late 1990s, and has witnessed significant changes in climate and human activities.Assessing the relative roles of different causal factors on NPP variability in TNSP zone is very important for establishing reasonable local policies to realize the emission reduction targets for central government. In this study, we examined the relative roles of drought and land cover conversion(LCC) on inter-annual changes of TNSP zone for 2001–2010. We applied integrated correlation and decomposition analyses to a Standardized Evapotranspiration Index (SPEI) and MODIS land cover dataset. Our results show that the 10-year average NPP within this region was about 420 Tg C. We found that about 60% of total annual NPP over the study area was significantly correlated with SPEI (p<0.05). The LCC-NPP relationship, which is especially evident for forests in the south-central area, indicates that ecological programs have a positive impact on C sequestration in the TNSP zone. Decomposition analysis generally indicated that the contributions of LCC, drought, and other Natural or Anthropogenic activities (ONA) to changes in NPP generally had a consistent distribution pattern for consecutive years. Drought and ONA contributed about 74% and 23% to the total changes in NPP, respectively, and the remaining 3% was attributed to LCC. Our results highlight the importance of rainfall supply on NPP variability in the TNSP zone.

MODIS observations of water color of the largest 10 lakes in China between 2000 and 2012

ABSTRACT Forel-Ule (FU) index of water color is an important parameter in traditional water quality investigations. We retrieved the FU index of the largest 10 lakes in China during 2000-2012 from MODerate-resolution Imaging Spectroradiometer surface reflectance product (MOD09) images. Since FU index is an optical parameter, it can be derived from optical remote sensing data by direct formulas, which is invariant with region and season. Based on validation by in situ measured reflectance data, the FU index products are reliable, with average relative error of 7.7%. FU index can be used to roughly assess water clarity: the clearer a water body is, and the bluer it is in color, the smaller its FU index is. FU index can also be used to roughly classify trophic state into three classes: oligotrophic, mesotrophic, and eutrophic. We analyzed the spatial, interannual, and seasonal variations of the FU index and its implications for water clarity and trophic state, and the findings are mostly consistent with the results from related literature. All in all, it might be a feasible way to roughly assess inland water quality by FU index in large region and over long time period.