Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi-Source and Multi-Scale Data

Estimating gross primary production (GPP) and net primary production (NPP) are significant important in studying carbon cycles. Using models driven by multi-source and multi-scale data is a promising approach to estimate GPP and NPP at regional and global scales. With a focus on data that are openly accessible, this paper presents a GPP and NPP model driven by remotely sensed data and meteorological data with spatial resolutions varying from 30 m to 0.25 degree and temporal resolutions ranging from 3 hours to 1 month, by integrating remote sensing techniques and eco-physiological process theories. Our model is also designed as part of the Multi-source data Synergized Quantitative (MuSyQ) Remote Sensing Production System. In the presented MuSyQ-NPP algorithm, daily GPP for a 10-day period was calculated as a product of incident photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (FPAR) using a light use efficiency (LUE) model. The autotrophic respiration (Ra) was determined using eco-physiological process theories and the daily NPP was obtained as the balance between GPP and Ra. To test its feasibility at regional scales, our model was performed in an arid and semi-arid region of Heihe River Basin, China to generate daily GPP and NPP during the growing season of 2012. The results indicated that both GPP and NPP exhibit clear spatial and temporal patterns in their distribution over Heihe River Basin during the growing season due to the temperature, water and solar influx conditions. After validated against ground-based measurements, MODIS GPP product (MOD17A2H) and results reported in recent literature, we found the MuSyQ-NPP algorithm could yield an RMSE of 2.973 gC m-2 d-1 and an R of 0.842 when compared with ground-based GPP while an RMSE of 8.010 gC m-2 d-1 and an R of 0.682 can be achieved for MODIS GPP, the estimated NPP values were also well within the range of previous literature, which proved the reliability of our modelling results. This research suggested that the utilization of multi-source data with various scales would help to the establishment of an appropriate model for calculating GPP and NPP at regional scales with relatively high spatial and temporal resolution.

[1]  Y. Gan,et al.  Hydrological controls on the sources of dissolved sulfate in the Heihe River, a large inland river in the arid northwestern China, inferred from S and O isotopes , 2013 .

[2]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[3]  C. Tucker,et al.  Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 , 2001 .

[4]  Gérard Dedieu,et al.  Methodology for the estimation of terrestrial net primary production from remotely sensed data , 1994 .

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

[6]  R. Leuning,et al.  Estimating catchment evaporation and runoff using MODIS leaf area index and the Penman‐Monteith equation , 2008 .

[7]  Mingguo Ma,et al.  Interannual variability of vegetation cover in the Chinese Heihe River Basin and its relation to meteorological parameters , 2006 .

[8]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[9]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[10]  Amon Murwira,et al.  Remote sensing net primary productivity (NPP) estimation with the aid of GIS modelled shortwave radiation (SWR) in a Southern African Savanna , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[11]  M. Ma,et al.  Interannual variability of vegetation cover in the Chinese Heihe River Basin and its relation to meterological parameters , 2006 .

[12]  R. Sun,et al.  Observation and simulation of net primary productivity in Qilian Mountain, western China. , 2007, Journal of environmental management.

[13]  L Li,et al.  Measurements and simulation of forest leaf area index and net primary productivity in Northern China. , 2007, Journal of environmental management.

[14]  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 .

[15]  H. Odum,et al.  Primary Productivity of the Biosphere , 1978, Ecological Studies.

[16]  Piao Shi,et al.  APPLICATION OF CASA MODEL TO THE ESTIMATION OF CHINESE TERRESTRIAL NET PRIMARY PRODUCTIVITY , 2001 .

[17]  J. Chen,et al.  Net primary productivity distribution in the BOREAS region from a process model using satellite and surface data , 1999 .

[18]  G. Cheng,et al.  Quantifying landscape structure of the Heihe River Basin, north-west China using FRAGSTATS , 2001 .

[19]  M. Tamura,et al.  Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia , 2002 .

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

[21]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[22]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[23]  Wenzhi Zhao,et al.  Water requirements and stability of oasis ecosystem in arid region, China , 2009 .

[24]  Shaomin Liu,et al.  A comparison of eddy-covariance and large aperture scintillometer measurements with respect to the energy balance closure problem , 2011 .

[25]  Lei Zhang,et al.  A Study of Shelterbelt Transpiration and Cropland Evapotranspiration in an Irrigated Area in the Middle Reaches of the Heihe River in Northwestern China , 2015, IEEE Geoscience and Remote Sensing Letters.

[26]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[27]  Jie He,et al.  Comparison of Downscaled Precipitation Data over a Mountainous Watershed: A Case Study in the Heihe River Basin , 2014 .

[28]  Bunkei Matsushita,et al.  Estimation of regional net primary productivity (NPP) using a process-based ecosystem model: How important is the accuracy of climate data? , 2004 .

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

[30]  Robert Frouin,et al.  Estimating Photosynthetically Active Radiation (PAR) at the earth's surface from satellite observations , 1995 .

[31]  Robert J. Scholes,et al.  Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide , 1993 .

[32]  Jing M. Chen,et al.  Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications , 1999 .

[33]  S. Running,et al.  A continuous satellite‐derived global record of land surface evapotranspiration from 1983 to 2006 , 2010 .

[34]  Frank Veroustraete,et al.  Vegetation primary production estimation at maize and alpine meadow over the Heihe River Basin, China , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[35]  S. Running,et al.  8 – Generalization of a Forest Ecosystem Process Model for Other Biomes, BIOME-BGC, and an Application for Global-Scale Models , 1993 .

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

[37]  Yong Tang,et al.  Evaluation of the Airborne CASI/TASI Ts-VI Space Method for Estimating Near-Surface Soil Moisture , 2015, Remote. Sens..

[38]  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.

[39]  P. North,et al.  Satellite-driven modelling of Net Primary Productivity (NPP): Theoretical analysis , 2009 .

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

[41]  Takeshi Horie,et al.  Leaf Nitrogen, Photosynthesis, and Crop Radiation Use Efficiency: A Review , 1989 .

[42]  D. Paslier,et al.  Net Exchange of CO2 in a Mid-Latitude Forest , 1993, Science.

[43]  H. Lieth.  Modeling the Primary Productivity of the World , 1975 .

[44]  A. McGuire,et al.  Global climate change and terrestrial net primary production , 1993, Nature.

[45]  Wenjie Fan,et al.  Algorithm of Leaf Area Index product for HJ-CCD over Heihe River Basin , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[46]  J. Chen,et al.  A process-based boreal ecosystem productivity simulator using remote sensing inputs , 1997 .

[47]  S. T. Gower,et al.  A cross‐biome comparison of daily light use efficiency for gross primary production , 2003 .

[48]  W. Oechel,et al.  Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements , 2001 .

[49]  H. Seino,et al.  Agroclimatic Evaluation of Net Primary Productivity of Natural Vegetations , 1985 .

[50]  W. Cohen,et al.  Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation , 2003 .

[51]  S. Goetz,et al.  Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005 , 2008 .

[52]  Takashi Hirano,et al.  Net ecosystem CO2 exchange over a larch forest in Hokkaido, Japan , 2004 .

[53]  Qing Xiao,et al.  Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design , 2013 .

[54]  Steven W. Running,et al.  User's Guide Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm , 2015 .

[55]  David W. Kicklighter,et al.  Equilibrium Responses of Soil Carbon to Climate Change: Empirical and Process-Based Estimates , 1995 .

[56]  Li Xin,et al.  Estimation of Net Primary Productivity of Heihe River Basin Using Remote Sensing , 2005 .

[57]  Liu Qinhuo,et al.  Land cover mapping using time series HJ-1/CCD data , 2014 .

[58]  Zhuo Wen,et al.  ESTIMATION OF NET PRIMARY PRODUCTIVITY OF CHINESE TERRESTRIAL VEGETATION BASED ON REMOTE SENSING , 2007 .

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

[60]  Jiemin Wang,et al.  Intercomparison of surface energy flux measurement systems used during the HiWATER‐MUSOEXE , 2013 .

[61]  Zhang Yushu.  Spatial-temporal Variations of Net Primary Productivity of Qilian Mountains Vegetation Based on CASA Model , 2014 .

[62]  S. Running,et al.  Generalization of a forest ecosystem process model for other biomes, Biome-BGC, and an application for global-scale models. Scaling processes between leaf and landscape levels , 1993 .

[63]  Wenjie Fan,et al.  A New FAPAR Analytical Model Based on the Law of Energy Conservation: A Case Study in China , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[64]  S. Running,et al.  A general model of forest ecosystem processes for regional applications I. Hydrologic balance, canopy gas exchange and primary production processes , 1988 .

[65]  Lei Zhang,et al.  Diurnal and Seasonal Variations in Carbon Dioxide Exchange in Ecosystems in the Zhangye Oasis Area, Northwest China , 2015, PloS one.

[66]  C. Tucker,et al.  A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data , 2000 .