Comparison of Two Remote Sensing Based Models for Regional Net Primary Productivity Estimation—A Case Study in Semi-Arid Central Kazakhstan

Modelling net primary productivity (NPP) is an important instrument for analysing carbon exchange between atmosphere and vegetation as well as for quantification of carbon sinks and sources. Remote-sensing-based models allow for regional NPP estimation and are potentially transferable to new regions. Comparative model analyses, however, are lacking, especially for semi-arid environments. In this study, two recent remote-sensing-based NPP models were applied for the first time to a study region in semi-arid Kazakhstan: RBM, a light-use-efficiency model based on MODIS products, and BETHY/DLR, a soil-vegetation-atmosphere-transfer model. Differences in intermediate products, their influence on calculated NPP, as well as output products are evaluated and discussed. BETHY/DLR calculates higher NPP (mean annual NPP 2010 and 2011: 136.87 g C m-2 and 106.69 g C m-2) than RBM (62.14 g C m-2 and 54.61 g C m-2) and shows stronger inter-annual changes. Spatial and seasonal patterns present well phenological differences. Comparison to field data from 2011 showed better results for BETHY/DLR, though both results were highly correlated to the field observations (BETHY/DLR: R2=0.95, RMSE=8.36 g C m-2; RBM: R2=0.98, RMSE=22.49 g C m-2). The parameterization of the light use efficiency is critical for RBM; also MODIS based 16-day time steps might be too long to capture variable climatic conditions. For BETHY/DLR, the MODIS land cover product applied in this study differentiates insufficient classes within the semi-arid environment; a more detailed land cover map is needed to improve the regional analysis.

[1]  W. Paul Menzel,et al.  Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS) , 1992, IEEE Trans. Geosci. Remote. Sens..

[2]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[3]  J. Monteith.  Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[4]  Yoram J. Kaufman,et al.  The MODIS Near-IR Water Vapor Algorithm , 1998 .

[5]  R. J. Olson,et al.  Estimating net primary productivity from grassland biomass dynamics measurements , 2002 .

[6]  Timothy M. Lenton,et al.  An analysis of some diverse approaches to modelling terrestrial net primary productivity , 2004 .

[7]  H. Velthuizen,et al.  Harmonized World Soil Database (version 1.2) , 2008 .

[8]  E. Lioubimtsevaa,et al.  Impacts of climate and landcover changes in arid lands of Central Asia , 2005 .

[9]  K. Hibbard,et al.  A Global Terrestrial Monitoring Network Integrating Tower Fluxes, Flask Sampling, Ecosystem Modeling and EOS Satellite Data , 1999 .

[10]  Ian McCallum,et al.  How sensitive are estimates of carbon fixation in agricultural models to input data? , 2012, Carbon Balance and Management.

[11]  Lars Eklundh,et al.  Net primary production and light use efficiency in a mixed coniferous forest in Sweden , 2005 .

[12]  Alan H. Strahler,et al.  Quality assessment of BRDF/albedo retrievals in MODIS operational system , 2008 .

[13]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[14]  Martin Kappas,et al.  Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing , 2012 .

[15]  Martin Kappas,et al.  Modeling Net Ecosystem Exchange for Grassland in Central Kazakhstan by Combining Remote Sensing and Field Data , 2009, Remote. Sens..

[16]  Xiangming Xiao,et al.  Light absorption by leaf chlorophyll and maximum light use efficiency , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[17]  U. Gessner,et al.  Regional land cover mapping and change detection in Central Asia using MODIS time-series , 2012 .

[18]  F. Baret,et al.  Estimating light absorption by chlorophyll, leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model , 2005 .

[19]  Jiyuan Liu,et al.  Estimation of the global net primary productivity using NOAA images and meteorological data: changes between 1988 and 1993 , 2004 .

[20]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[21]  A. Roth,et al.  The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar , 2003 .

[22]  Stefan Dech,et al.  Derivation of biomass information for semi-arid areas using remote-sensing data , 2012 .

[23]  Maosheng Zhao,et al.  Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses , 2006 .

[24]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[25]  Pang-Ning Tan,et al.  Continental-scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982–1998 , 2003 .

[26]  J. Garratt.  The Atmospheric Boundary Layer , 1992 .

[27]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[28]  N. Jones,et al.  Seasonal variation of carbon monoxide in northern Japan: Fourier transform IR measurements and source-labeled model calculations , 2006 .

[29]  Markus Tum,et al.  Validation of modelled forest biomass in Germany using BETHY/DLR , 2011 .

[30]  Nicholas C. Coops,et al.  Comparison of three models for predicting gross primary production across and within forested ecoregions in the contiguous United States , 2009 .

[31]  Stefan Dech,et al.  Quantitative comparison of the operational NOAA-AVHRR LST product of DLR and the MODIS LST product V005 , 2012 .

[32]  Jochen J. Richters,et al.  BIOMASS CHANGES IN NORTH-WESTERN NAMIBIA: FIRST RESULTS FROM A REMOTE SENSING MODELLING APPROACH , 2005 .

[33]  W. Ju,et al.  Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing. , 2007, Journal of environmental management.

[34]  S. Dech,et al.  Comparison and enhancement of MODIS cloud mask products for Southeast Asia , 2013 .

[35]  Z. Wan.  New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products , 2008 .

[36]  D. Schimel,et al.  Terrestrial biogeochemical cycles: Global estimates with remote sensing , 1995 .

[37]  J. Monteith.  SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[38]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[39]  Fabio Maselli,et al.  Integration of high- and low-resolution satellite data to estimate pine forest productivity in a Mediterranean coastal area , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Ranga B. Myneni,et al.  Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the terra MODIS sensor: 2000-2005 , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[41]  J. Randerson,et al.  Global net primary production: Combining ecology and remote sensing , 1995 .

[42]  Nicholas C. Coops,et al.  Comparison of MODIS, eddy covariance determined and physiologically modelled gross primary production (GPP) in a Douglas-fir forest stand , 2007 .

[43]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[44]  Markus Tum,et al.  Validating modelled NPP using statistical yield data , 2011 .

[45]  P. Shi,et al.  Modelling net primary productivity of terrestrial ecosystems in East Asia based on an improved CASA ecosystem model , 2009 .

[46]  C. Priestley,et al.  On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters , 1972 .

[47]  P. Hiernaux,et al.  Sahelian rangeland response to changes in rainfall over two decades in the Gourma region, Mali , 2009 .

[48]  Markus Tum,et al.  Estimating the Carbon Cycle of South Africa with BETHY/DLR , 2010 .

[49]  J. Townshend,et al.  Global Percent Tree Cover at a Spatial Resolution of 500 Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm , 2003 .

[50]  Jonathan M. Adams,et al.  Impacts of Climate and Land-cover Changes in Arid Lands of Central Asia , 2005 .

[51]  Thilo Wehrmann,et al.  Land Cover and Land Use Classification with TWOPAC: towards Automated Processing for Pixel- and Object-Based Image Classification , 2012, Remote. Sens..

[52]  Liangyun Liu,et al.  Detection of vegetation light use efficiency based on chlorophyll fluorescence spectrum , 2010 .

[53]  F. Woodward,et al.  Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models , 2001 .

[54]  Jed O. Kaplan,et al.  A continuous Late Glacial and Holocene record of vegetation changes in Kazakhstan , 1997 .

[55]  Martin Kappas,et al.  Review of Available Products of Leaf Area Index and Their Suitability over the Formerly Soviet Central Asia , 2012, J. Sensors.

[56]  Kurt P. Günther,et al.  Modeling Carbon Sinks and Sources in semi-arid Environments for a Land Degradation Assessment Approach , 2010, EnviroInfo.

[57]  D. Niemeijer.  Developing indicators for environmental policy: data-driven and theory-driven approaches examined by example , 2002 .

[58]  Christopher Conrad,et al.  Snow-cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products , 2013 .

[59]  A. Bondeau,et al.  Comparing global models of terrestrial net primary productivity (NPP): overview and key results , 1999 .

[60]  Jochen J. Richters.  Biomass production, pasture balance, and their ecologic consequences in NW Namibia , 2006, SPIE Optics + Photonics.

[61]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[62]  Universitäts und Landesbibliothek Bonn,et al.  Modellierung der regionalen CO2-Aufnahme durch Vegetation , 2006 .

[63]  Wolfgang Knorr,et al.  Uncertainties in global terrestrial biosphere modeling: 1. A comprehensive sensitivity analysis with a new photosynthesis and energy balance scheme , 2001 .