Developing a gross primary production model for coniferous forests of northeastern USA from MODIS data

Accurate estimation of ecosystem carbon fluxes is crucial for understanding the feedbacks between the terrestrial biosphere and the atmosphere and for making climate-policy decisions. A statistical model is developed to estimate the gross primary production (GPP) of coniferous forests of northeastern USA using remotely sensed (RS) radiation (land surface temperature and near-infra red albedo) and ecosystem variables (enhanced vegetation index and global vegetation moisture index) acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This GPP model (called R-GPP-Coni), based only on remotely sensed data, was first calibrated with GPP estimates derived from the eddy covariance flux tower of the Howland forest main tower site and then successfully transferred and validated at three other coniferous sites: the Howland forest west tower site, Duke pine forest and North Carolina loblolly pine site, which demonstrate its transferability to other coniferous ecoregions of northeastern USA. The proposed model captured the seasonal dynamics of the observed 8-day GPP successfully by explaining 84–94% of the observed variations with a root mean squared error (RMSE) ranging from 1.10 to 1.64 g C/m²/day over the 4 study sites and outperformed the primary RS-based GPP algorithm of MODIS.     

The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation

Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally.     

Remotely-sensed phenology of Italian forests: Going beyond the species

Remotely sensed observations of seasonal greenness dynamics represent a valuable tool for studying vegetation phenology at regional and ecosystem-level scales. We investigated the seasonal variability of forests in Italy, examining the different mechanisms of phenological response to biophysical drivers. For each point of the Italian National Forests Inventory, we processed a multitemporal profile of the MODIS Enhanced Vegetation Index. Then we applied a multivariate approach for the purpose of (i) classifying the Italian forests into phenological clusters (i.e. pheno-clusters), (ii) identifying the main phenological characteristics and the forest compositions of each pheno-cluster and (iii) exploring the role of climate and physiographic variables in the phenological timing of each cluster. Results identified four pheno-clusters, following a clear elevation gradient and a distinct separation along the Mediterranean-to-temperate climatic transition of Italy. The “High-elevation coniferous” and the “High elevation deciduous” resulted mainly affected by elevation, with the former characterized by low annual productivity and the latter by high seasonality. To the contrary, the “Low elevation deciduous” showed to be mostly associated to moderate climate conditions and a prolonged growing season. Finally, summer drought was the main driving variable for the “Mediterranean evergreen”, characterized by low seasonality. The discrimination of vegetation phenology types can provide valuable information useful as a baseline framework for further studies on forests ecosystem and for management strategies.     

陆地总初级生产力遥感估算精度分析

准确估算陆地总初级生产力GPP(Gross Primary Productivity)数值对碳循环过程模拟有重要影响。本文介绍了多种基于植被指数以及基于光能利用率的遥感GPP算法,综述了不同算法在其研究区域的估算精度;并分析了MODIS/GPP以及BESS/GPP两种遥感GPP产品在不同植被类型的估算精度。通过对比全球碳通量站网络GPP数据表明,MODIS/GPP产品在全球估算结果具显著相关性(R2=0.59)及中等标准误差(RMSE=2.86 g C/m2/day),估算精度较高的植被类型有落叶阔叶林,草地等;估算精度较低类型包括常绿阔叶林,稀树草原等。本文对GPP产品中存在的不确定性进行分析,通过综述前人研究中发现的遥感估算GPP方法中存在的问题,指出可能的提高卫星遥感GPP产品估算精度的方法及发展趋势。     

冠层光能利用率的叶绿素荧光光谱探测

设计了玉米生长期日变化试验,同步获取玉米冠层光谱和通量观测数据,探究从植被发射荧光光谱角度实现植被光能利用率可靠反演的可能性。运用涡度相关法获取群体生态系统净生产力(NEP),通过呼吸作用拟合得到冠层总初级生产力(GPP);在此基础上结合吸收光合有效辐射(APAR)获取冠层光能利用率(LUE);同时,利用叶绿素荧光光谱分离算法,提取了光合作用叶绿素荧光绝对强度和相对强度。结论表明,植被发射荧光光谱与光合有效辐射(PAR)显著正相关,760nm波段荧光与PAR的复相关系数R2在0.99以上;叶绿素荧光绝对强度与NEP和GPP显著正相关,荧光和NEP对环境日变化具有类似的响应特征;688nm和760nm植被发射的叶绿素荧光相对强度与LUEGPP存在可靠负相关关系,即叶绿素荧光强度越大,光能利用率越低。同时,通过比较几种植被指数与各种光合参量的相关性表明,叶绿素荧光能够更好的跟踪植被光合状态的变化,更适宜于植被光能利用率的探测。     

Decreased vegetation growth in response to summer drought in Central Asia from 2000 to 2012

Climate change scenarios predict that Central Asia may experience an increase in the frequency and magnitude of temperature and precipitation extremes by the end of the 21st century, but the response regularity of different types of vegetation to climate extremes is uncertain. Based on remote-sensed vegetation index and in-situ meteorological data for the period of 2000–2012, we examined the diverse responses of vegetation to climate mean/extremes and differentiated climatic and anthropogenic influence on the vegetation in Central Asia. Our results showed that extensive vegetation degradation was related to summer water deficit as a result of the combined effect of decreased precipitation and increased potential evapotranspiration. Water was a primary climatic driver for vegetation changes regionally, and human-induced changes in vegetation confined mainly to local areas. Responses of vegetation to water stress varied in different vegetation types. Grasslands were most responsive to water deficit followed by forests and desert vegetation. Climate extremes caused significant vegetation changes, and different vegetation types had diverse responses to climate extremes. Grasslands represented a symmetric response to wet and dry periods. Desert vegetation was more responsive during wet years than in dry years. Forests responded more strongly to dry than to wet years due to a severe drought occurred in 2008. This study has important implications for predicting how vegetation ecosystems in drylands respond to climate mean/extremes under future scenarios of climate change.     

Comparison of three remotely sensed drought indices for assessing the impact of drought on winter wheat yield

ABSTRACT

Agricultural drought threatens food security. Numerous remote-sensing drought indices have been developed, but their different principles, assumptions and physical quantities make it necessary to compare their suitability for drought monitoring over large areas. Here, we analyzed the performance of three typical remote sensing-based drought indices for monitoring agricultural drought in two major agricultural production regions in Shaanxi and Henan provinces, northern China (predominantly rain-fed and irrigated agriculture, respectively): vegetation health index (VHI), temperature vegetation dryness index (TVDI) and drought severity index (DSI). We compared the agreement between these indices and the standardized precipitation index (SPI), soil moisture, winter wheat yield and National Meteorological Drought Monitoring (NMDM) maps. On average, DSI outperformed the other indices, with stronger correlations with SPI and soil moisture. DSI also corresponded better with soil moisture and NMDM maps. The jointing and grain-filling stages of winter wheat are more sensitive to water stress, indicating that winter wheat required more water during these stages. Moreover, the correlations between the drought indices and SPI, soil moisture, and winter wheat yield were generally stronger in Shaanxi province than in Henan province, suggesting that remote-sensing drought indices provide more accurate predictions of the impacts of drought in predominantly rain-fed agricultural areas.     

Africa and the global carbon cycle

The African continent has a large and growing role in the global carbon cycle, with potentially important climate change implications. However, the sparse observation network in and around the African continent means that Africa is one of the weakest links in our understanding of the global carbon cycle. Here, we combine data from regional and global inventories as well as forward and inverse model analyses to appraise what is known about Africa's continental-scale carbon dynamics. With low fossil emissions and productivity that largely compensates respiration, land conversion is Africa's primary net carbon release, much of it through burning of forests. Savanna fire emissions, though large, represent a short-term source that is offset by ensuing regrowth. While current data suggest a near zero decadal-scale carbon balance, interannual climate fluctuations (especially drought) induce sizeable variability in net ecosystem productivity and savanna fire emissions such that Africa is a major source of interannual variability in global atmospheric CO₂. Considering the continent's sizeable carbon stocks, their seemingly high vulnerability to anticipated climate and land use change, as well as growing populations and industrialization, Africa's carbon emissions and their interannual variability are likely to undergo substantial increases through the 21st century.     

黑龙江省植被大气调节功能时空分布特征

为研究生态系统对环境大气气体调节的贡献,应用碳税法和造林成本法,该文对2000—2010年黑龙江省农田、草地、灌丛等7类生态系统的大气调节功能价值进行估算,分析其时空分布特征。结果表明,2000—2010年黑龙江省净初级生产力逐年退化,下降的面积为127 811km~2,占总面积29.79%,在伊春市、哈尔滨市东部、牡丹江市等地表现得尤为明显;2000—2003、2004—2007、2008—2010年3个时段年均大气调节功能价值分别为1 366.16、1 342.45、1 275.68亿元,呈下降趋势,2008—2010时段较前两个时段价值下降幅度增大;空间上呈现出西南和东南向西北方向递减的趋势;价值构成上表现为阔叶林落叶针叶林草地针阔混交林农田灌丛其他类型。     

Carbon fluxes in ecosystems of Yellowstone National Park predicted from remote sensing data and simulation modeling

Background

A simulation model based on remote sensing data for spatial vegetation properties has been used to estimate ecosystem carbon fluxes across Yellowstone National Park (YNP). The CASA (Carnegie Ames Stanford Approach) model was applied at a regional scale to estimate seasonal and annual carbon fluxes as net primary production (NPP) and soil respiration components. Predicted net ecosystem production (NEP) flux of CO₂ is estimated from the model for carbon sinks and sources over multi-year periods that varied in climate and (wildfire) disturbance histories. Monthly Enhanced Vegetation Index (EVI) image coverages from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (from 2000 to 2006) were direct inputs to the model. New map products have been added to CASA from airborne remote sensing of coarse woody debris (CWD) in areas burned by wildfires over the past two decades.

Results

Model results indicated that relatively cooler and wetter summer growing seasons were the most favorable for annual plant production and net ecosystem carbon gains in representative landscapes of YNP. When summed across vegetation class areas, the predominance of evergreen forest and shrubland (sagebrush) cover was evident, with these two classes together accounting for 88% of the total annual NPP flux of 2.5 Tg C yr^-1 (1 Tg = 10¹² g) for the entire Yellowstone study area from 2000-2006. Most vegetation classes were estimated as net ecosystem sinks of atmospheric CO₂ on annual basis, making the entire study area a moderate net sink of about +0.13 Tg C yr^-1. This average sink value for forested lands nonetheless masks the contribution of areas burned during the 1988 wildfires, which were estimated as net sources of CO₂ to the atmosphere, totaling to a NEP flux of -0.04 Tg C yr^-1 for the entire burned area. Several areas burned in the 1988 wildfires were estimated to be among the lowest in overall yearly NPP, namely the Hellroaring Fire, Mink Fire, and Falls Fire areas.

Conclusions

Rates of recovery for burned forest areas to pre-1988 biomass levels were estimated from a unique combination of remote sensing and CASA model predictions. Ecosystem production and carbon fluxes in the Greater Yellowstone Ecosystem (GYE) result from complex interactions between climate, forest age structure, and disturbance-recovery patterns of the landscape.     

土壤湿度信息遥感研究

土壤湿度是农业生产与应用过程中非常重要的因素,决定农作物的水分供应状况.本文利用MODIS产品数据获取的归一化植被指数(NDVI)和陆面地表温度(Ts)构建Ts-NDVI特征空间,根据温度植被干旱指数(TVDI)的研究原理与方法,对研究区2010年5～8月份土壤湿度分布情况进行遥感监测.结合气象数据与土壤墒情资料对局部...     

An approach to estimate the evapotranspiration using NOAA/AVHRR DATA

Evapotranspiration (ET) is continued process wherein moisture from soil and vegetated surface is transferred to the atmosphere. Changes in evapotranspiration are likely to have large impacts on terrestrial vegetation. Evapotranspiration is a seasonally varying property at a given place; changes in it reflect the status of soil moisture and terrestrial vegetation. Through water balance, ET can include major shifts in vegetative patterns and or its condition leading to climate change. Therefore, in this paper, it is attempted to estimate the evapotranspiration over various land cover using National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR) data at coarse spatial resolution of 1.1 km. For this purpose, a semi-empirical model has been proposed to estimate the ET. Regression analysis has been carried out to develop an empirical relation between individual land cover surface temperature and ET, which will be helpful to know the effect of each land cover surface temperature on ET. In which, it is observed that surface temperature over grassland is more effective on ET in comparison to other land cover in March 1999 on the Mupfure, Zimbabwe catchment area. This type of estimation will be helpful for climate modeler, climatologists, ecosystem modeler and regional planner.     

2000-2012年鄂尔多斯高原植被动态及干旱响应

针对鄂尔多斯高原植被覆盖变化受干旱胁迫的状况,该文结合降水和气温的协同变化,以2000-2012年生长季的MODIS-NDVI数据和同期降水、温度和帕尔默干旱指数为依据,采用线性趋势分析、标准偏差分析和相关性分析等方法,对鄂尔多斯高原植被与气候变化的相关关系和干旱异常变化对植被动态的影响进行了研究.结果表明:鄂尔多斯高原生长季及季节(春季、夏季和秋季)植被归一化植被指数主要受降水的控制和干旱的制约,秋季归一化植被指数更多地受到夏季干旱的影响.与气象因子的空间相关分析表明,春季温度上升有利于研究区北部归一化植被指数像元的增加.在荒漠草原和沙漠地区,夏季干旱与归一化植被指数的相关关系最强.秋季降水对典型草原归一化植被指数的提升显著.     

Pacific climate variability and the possible impact on global surface CO2 flux

Background  

Climate variability modifies both oceanic and terrestrial surface CO2 flux. Using observed/assimilated data sets, earlier studies have shown that tropical oceanic climate variability has strong impacts on the land surface temperature and soil moisture, and that there is a negative correlation between the oceanic and terrestrial CO2 fluxes. However, these data sets only cover less than the most recent 20 years and are insufficient for identifying decadal and longer periodic variabilities. To investigate possible impacts of interannual to interdecadal climate variability on CO2 flux exchange, the last 125 years of an earth system model (ESM) control run are examined.     

Crop yield estimation model for Iowa using remote sensing and surface parameters

Numerous efforts have been made to develop various indices using remote sensing data such as normalized difference vegetation index (NDVI), vegetation condition index (VCI) and temperature condition index (TCI) for mapping and monitoring of drought and assessment of vegetation health and productivity. NDVI, soil moisture, surface temperature and rainfall are valuable sources of information for the estimation and prediction of crop conditions. In the present paper, we have considered NDVI, soil moisture, surface temperature and rainfall data of Iowa state, US, for 19 years for crop yield assessment and prediction using piecewise linear regression method with breakpoint. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. A non-linear Quasi-Newton multi-variate optimization method is utilized, which reasonably minimizes inconsistency and errors in yield prediction.     

Simulation of olive grove gross primary production by the combination of ground and multi-sensor satellite data

We developed and tested a methodology to estimate olive (Olea europaea L.) gross primary production (GPP) combining ground and multi-sensor satellite data. An eddy-covariance station placed in an olive grove in central Italy provided carbon and water fluxes over two years (2010–2011), which were used as reference to evaluate the performance of a GPP estimation methodology based on a Monteith type model (modified C-Fix) and driven by meteorological and satellite (NDVI) data. A major issue was related to the consideration of the two main olive grove components, i.e. olive trees and inter-tree ground vegetation: this issue was addressed by the separate simulation of carbon fluxes within the two ecosystem layers, followed by their recombination. In this way the eddy covariance GPP measurements were successfully reproduced, with the exception of two periods that followed tillage operations. For these periods measured GPP could be approximated by considering synthetic NDVI values which simulated the expected response of inter-tree ground vegetation to tillages.     

Spectral anisotropy of subtropical deciduous forest using MISR and MODIS data acquired under large seasonal variation in solar zenith angle

Recent studies in Amazonian tropical evergreen forests using the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) have highlighted the importance of considering the view-illumination geometry in satellite data analysis. However, contrary to the observed for evergreen forests, bidirectional effects have not been evaluated in Brazilian subtropical deciduous forests. In this study, we used MISR data to characterize the reflectance and vegetation index anisotropies in subtropical deciduous forest from south Brazil under large seasonal solar zenith angle (SZA) variation and decreasing leaf area index (LAI) from the summer to winter. MODIS data were used to observe seasonal changes in the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Topographic effects on their determination were inspected by dividing data from the summer to winter and projecting results over a digital elevation model (DEM). By using the PROSAIL, we investigated the relative contribution of LAI and SZA to vegetation indices (VI) of deciduous forest. We also simulated and compared the MISR NDVI and EVI response of subtropical deciduous and tropical evergreen forests as a function of the large seasonal SZA amplitude of 33°. Results showed that the MODIS-MISR NDVI and EVI presented higher values in the summer and lower ones in the winter with decreasing LAI and increasing SZA or greater amounts of canopy shadows viewed by the sensors. In the winter, NDVI reduced local topographic effects due to the red-near infrared (NIR) band normalization. However, the contrary was observed for the three-band EVI that enhanced local variations in shaded and sunlit surfaces due to its strong dependence on the NIR band response. The reflectance anisotropy of the MISR bands increased from the summer to winter and was stronger in the backscattering direction at large view zenith angles (VZA). EVI was much more anisotropic than NDVI and the anisotropy increased from the summer to winter. It also increased from the forward scatter to the backscattering direction with the predominance of sunlit canopy components viewed by MISR, especially at large VZA. Modeling PROSAIL results confirmed the stronger anisotropy of EVI than NDVI for the subtropical deciduous and tropical evergreen forests. PROSAIL showed that LAI and SZA are coupled factors to decrease seasonally the VIs of deciduous forest with the first one having greater importance than the latter. However, PROSAIL seasonal variations in VIs were much smaller than those observed with MODIS data probably because the effects of shadows in heterogeneous canopy structures or/and cast by emergent trees and from local topography were not modeled.     

Responses of global terrestrial water use efficiency to climate change and rising atmospheric CO2 concentration in the twenty-first century

Terrestrial ecosystems play a significant role in global carbon and water cycles because of the substantial amount of carbon assimilated through net primary production and large amount of water loss through evapotranspiration (ET). Using a process-based ecosystem model, we investigate the potential effects of climate change and rising atmospheric CO₂ concentration on global terrestrial ecosystem water use efficiency (WUE) during the twenty-first century. Future climate change would reduce global WUE by 16.3% under high-emission climate change scenario (A2) and 2.2% under low-emission climate scenario (B1) during 2010–2099. However, the combination of rising atmospheric CO₂ concentration and climate change would increase global WUE by 7.9% and 9.4% under A2 and B1 climate scenarios, respectively. This suggests that rising atmospheric CO₂ concentration could ameliorate climate change-induced WUE decline. Future WUE would increase significantly at the high-latitude regions but decrease at the low-latitude regions under combined changes in climate and atmospheric CO₂. The largest increase of WUE would occur in tundra and boreal needleleaf deciduous forest under the combined A2 climate and atmospheric CO₂ scenario. More accurate prediction of WUE requires deeper understanding on the responses of ET to rising atmospheric CO₂ concentrations and its interactions with climate.     

阜新地区干旱监测研究

选定温度植被干旱指数法建立阜新地区干旱监测模型。通过参数的确定,得到温度植被干旱指数,再通过阜新地区的气象站点地面实测土壤含水量数据,建立温度植被干旱指数-土壤含水量（ TVDI-SWC ）经验模型。通过回归分析以及2007年预测分析的实验数据表明, TVDI-SWC模型适用于阜新地区早春的干旱监测,可以使用该方法来实现对阜新地区的整体旱情状况快速,准确的评估。     

Large-scale estimates of gross primary production on the Qinghai-Tibet plateau based on remote sensing data

Vegetation gross primary production (GPP) is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau (QTP). Based on the measurements from 12 eddy covariance flux sites, we validated a light use efficiency model (i.e. EC-LUE) to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP. In general, EC-LUE model performed well in predicting GPP at different time scale over QTP. Annual GPP over the entire QTP ranged from 575 to 703 Tg C, and showed a significantly increasing trend from 1982 to 2013. However, there were large spatial heterogeneities in long-term trends of GPP. Throughout the entire QTP, air temperature increase had a greater influence than solar radiation and precipitation (PREC) changes on productivity. Moreover, our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations. When compared with GPP estimates of the EC-LUE model, most Coupled Model Intercomparison Project (CMIP5) GPP products overestimate the magnitude and increasing trends of regional GPP, which potentially impact the feedback of ecosystems to regional climate changes.