Comparison of satellite-based models for estimating gross primary productivity in agroecosystems

Abstract Satellite-based gross primary productivity (GPP) models have been widely used for simulating carbon exchanges of terrestrial ecosystems. However, the performances of various GPP models in agroecosystems have been rarely explored. In this study, we calibrated the model parameters and compared the performances of seven light use efficiency (LUE-GPP) models and five vegetation-index (VI-GPP) models for simulating daily GPP of agroecosystems over 106 crop growing seasons, and examined the effects of model structure on model performance. The simulations were carried out based on 19 eddy covariance (EC) sites from the global flux network and vegetation indices obtained from MODIS. The calibrated potential LUE (emax) for C4 crop (summer maize, 2.59±0.94 g C MJ−1) was higher than that for C3 crops (1.42±0.58 g C MJ−1) in any LUE-GPP models. The performances of models differed across the crops. Generally, all models performed better for C3 crops than C4 crops, and for winter crops (winter wheat-Triticum aestivum L, rape-Brassica napus L, and winter barley-Hordeum vulgare L) than summer crops (summer maize-Zea mays L, potato-Solanum tuberosum L, rice-Oryza sativa L. and soybean-Glycine max (L.) Merr.). Cloudiness index-LUE (CI-LUE) model outperformed the other LUE-GPP models, and vegetation index (VEI) model outperformed the other VI-GPP models. LUE-GPP models demonstrated better performance than VI-GPP models due to the inclusion of water stress (Ws) and temperature stress (Ts). A comparison of the model structures showed that models only considering the effects of Ws produced smaller errors than those only considering the effects of Ts in simulating GPP. Ws algorithms generated the larger variations in LUE-GPP models compared to those of Ts, especially during the drought period. All models obtained higher R2 and smaller errors using the minimum method (Min (Ts, Ws)) than using the multiplication method (Ts × Ws) to integrate the effects of Ts and Ws on GPP, which suggested that the minimum method was better than the multiplication method to integrate Ts and Ws on LUE. These results showed that satellite-based models with calibrated crop-specific parameters have the potential to serve as the basis for estimation of agroecosystem GPP, and can provide direction for future model structure optimization.

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

[2]  Karl Schneider,et al.  Patterns and scaling properties of surface soil moisture in an agricultural landscape: An ecohydrological modeling study , 2013 .

[3]  Dan Liu,et al.  Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database , 2014 .

[4]  Rongping Li,et al.  Ten-year variability and environmental controls of ecosystem water use efficiency in a rainfed maize cropland in Northeast China , 2018, Field Crops Research.

[5]  Xiangming Xiao,et al.  Comparison of solar-induced chlorophyll fluorescence, light-use efficiency, and process-based GPP models in maize. , 2016, Ecological Applications.

[6]  Tiexi Chen,et al.  Evaluation of cropland maximum light use efficiency using eddy flux measurements in North America and Europe , 2011 .

[7]  Yanlian Zhou,et al.  Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity , 2013 .

[8]  Justus von Liebig,et al.  Chemistry in Its Application to Agriculture and Physiology , 1842, London and Edinburgh Monthly Journal of Medical Science.

[9]  S. Wofsy,et al.  Midday values of gross CO2 flux and light use efficiency during satellite overpasses can be used to directly estimate eight-day mean flux , 2005 .

[10]  Sha Zhang,et al.  A remote sensing-based two-leaf canopy conductance model: Global optimization and applications in modeling gross primary productivity and evapotranspiration of crops , 2018, Remote Sensing of Environment.

[11]  E. Small,et al.  Dynamics of evapotranspiration in semiarid grassland and shrubland ecosystems during the summer monsoon season, central New Mexico , 2004 .

[12]  Paolo De Angelis,et al.  Reconciling the optimal and empirical approaches to modelling stomatal conductance , 2011 .

[13]  Guirui Yu,et al.  A MODIS-based Photosynthetic Capacity Model to estimate gross primary production in Northern China and the Tibetan Plateau , 2014 .

[14]  Jiquan Chen,et al.  Net ecosystem methane and carbon dioxide exchanges in a Lake Erie coastal marsh and a nearby cropland , 2014, Journal of Geophysical Research: Biogeosciences.

[15]  Wenping Yuan,et al.  Estimating crop yield using a satellite-based light use efficiency model , 2016 .

[16]  Anatoly A. Gitelson,et al.  Remote estimation of gross primary productivity in crops using MODIS 250m data , 2013 .

[17]  A. Arneth,et al.  Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation , 2010 .

[18]  D. Sims,et al.  An improved approach for remotely sensing water stress impacts on forest C uptake , 2014, Global change biology.

[19]  Andrew E. Suyker,et al.  Multi-scale evaluation of light use efficiency in MODIS gross primary productivity for croplands in the Midwestern United States , 2015 .

[20]  Xuguang Tang,et al.  Differences in ecosystem water-use efficiency among the typical croplands , 2018, Agricultural Water Management.

[21]  Rasmus Fensholt,et al.  MODIS leaf area index products: from validation to algorithm improvement , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[22]  W. Cohen,et al.  Evaluation of MODIS NPP and GPP products across multiple biomes. , 2006 .

[23]  Shaoqiang Wang,et al.  Improving the light use efficiency model for simulating terrestrial vegetation gross primary production by the inclusion of diffuse radiation across ecosystems in China , 2015 .

[24]  Ningbo Cui,et al.  Estimation of soil temperature from meteorological data using different machine learning models , 2019, Geoderma.

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

[26]  Pamela L. Nagler,et al.  Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data , 2005 .

[27]  Zhengpeng Li,et al.  Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States , 2014 .

[28]  A. Viña,et al.  New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops , 2005 .

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

[30]  Josep Peñuelas,et al.  Drought impacts on terrestrial primary production underestimated by satellite monitoring , 2019, Nature Geoscience.

[31]  Yanhong Tang,et al.  Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau , 2008 .

[32]  Iryna Dronova,et al.  The impact of expanding flooded land area on the annual evaporation of rice , 2016 .

[33]  Christian Bernhofer,et al.  Land use regulates carbon budgets in eastern Germany: From NEE to NBP , 2010 .

[34]  Qiang Yu,et al.  Impacts of diffuse radiation fraction on light use efficiency and gross primary production of winter wheat in the North China Plain , 2019, Agricultural and Forest Meteorology.

[35]  Chaoyang Wu,et al.  Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize , 2010 .

[36]  Lunche Wang,et al.  Comparison of Different GPP Models in China Using MODIS Image and ChinaFLUX Data , 2014, Remote. Sens..

[37]  Lifu Zhang,et al.  An NDVI-Based Vegetation Phenology Is Improved to be More Consistent with Photosynthesis Dynamics through Applying a Light Use Efficiency Model over Boreal High-Latitude Forests , 2017, Remote. Sens..

[38]  Jonas Ardö,et al.  Evaluation of MODIS gross primary productivity for Africa using eddy covariance data , 2013 .

[39]  R. Cesar Izaurralde,et al.  Estimating crop net primary production using national inventory data and MODIS-derived parameters , 2013 .

[40]  Shilong Piao,et al.  NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China , 2006 .

[41]  Zhuguo Ma,et al.  Deriving maximal light use efficiency from coordinated flux measurements and satellite data for regional gross primary production modeling , 2010 .

[42]  N. Vuichard,et al.  The European carbon balance. Part 2: croplands , 2010 .

[43]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[44]  R. Leuning,et al.  Carbon and water fluxes over a temperate Eucalyptus forest and a tropical wet/dry savanna in Australia: measurements and comparison with MODIS remote sensing estimates , 2005 .

[45]  W. Oechel,et al.  Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants , 2015 .

[46]  Vinay Kumar Dadhwal,et al.  Evaluation of remote-sensing-based models of gross primary productivity over Indian sal forest using flux tower and MODIS satellite data , 2017 .

[47]  Jinwei Dong,et al.  A global moderate resolution dataset of gross primary production of vegetation for 2000–2016 , 2017, Scientific Data.

[48]  Zhongmin Hu,et al.  Comparison of four light use efficiency models for estimating terrestrial gross primary production , 2015 .

[49]  Prasanna H. Gowda,et al.  Parameterizing ecosystem light use efficiency and water use efficiency to estimate maize gross primary production and evapotranspiration using MODIS EVI , 2016 .

[50]  Philippe Ciais,et al.  The carbon balance of European croplands: A cross-site comparison of simulation models , 2010 .

[51]  Qianjun Zhao,et al.  A method for estimating the gross primary production of alpine meadows using MODIS and climate data in China , 2013 .

[52]  Conghe Song,et al.  Understanding moisture stress on light use efficiency across terrestrial ecosystems based on global flux and remote‐sensing data , 2015 .

[53]  Andrew E. Suyker,et al.  REMOTE ESTIMATION OF GROSS PRIMARY PRODUCTION IN MAIZE , 2011 .

[54]  Andrew E. Suyker,et al.  Uncertainty in simulating gross primary production of cropland ecosystem from satellite-based models , 2015 .

[55]  Baozhang Chen,et al.  Spatio-temporal variations in water use efficiency and its drivers in China over the last three decades , 2018, Ecological Indicators.

[56]  Q. Xin,et al.  Improving satellite-based modelling of gross primary production in deciduous broadleaf forests by accounting for seasonality in light use efficiency , 2018, International Journal of Remote Sensing.

[57]  M. Mauder,et al.  Mesoscale Eddies Affect Near-Surface Turbulent Exchange: Evidence from Lidar and Tower Measurements , 2015 .

[58]  R. Miller,et al.  Changes in Soil Microbial Community Structure in a Tallgrass Prairie Chronosequence , 2005 .

[59]  Bofu Yu,et al.  Partitioning evapotranspiration based on the concept of underlying water use efficiency , 2016 .

[60]  Dan Liu,et al.  Large Differences in Terrestrial Vegetation Production Derived from Satellite-Based Light Use Efficiency Models , 2014, Remote. Sens..

[61]  S. Verma,et al.  Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO2 flux tower data , 2011 .

[62]  T. Vesala,et al.  Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes , 2007 .

[63]  A. Viña,et al.  Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity , 2006 .

[64]  John S. Kimball,et al.  Numerical Terradynamic Simulation Group 11-2017 Improving Global Gross Primary Productivity Estimates by Computing Optimum Light Use Efficiencies Using Flux Tower Data , 2018 .

[65]  D. Baldocchi,et al.  Agricultural peatland restoration: effects of land‐use change on greenhouse gas (CO2 and CH4) fluxes in the Sacramento‐San Joaquin Delta , 2015, Global change biology.

[66]  Huifang Zhang,et al.  Seasonal fluctuations of photosynthetic parameters for light use efficiency models and the impacts on gross primary production estimation , 2017 .

[67]  Jing M. Chen,et al.  Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration , 2011 .

[68]  Minseok Kang,et al.  Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images , 2017 .

[69]  Jinwei Dong,et al.  Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought , 2015 .

[70]  Cheng-Yu Liu,et al.  Simulated water fluxes during the growing season in semiarid grassland ecosystems under severe drought conditions , 2014 .

[71]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[72]  M. Williams,et al.  Net primary production of forests: a constant fraction of gross primary production? , 1998, Tree physiology.

[73]  Shiping Chen,et al.  Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems , 2019, Remote. Sens..

[74]  Atul K. Jain,et al.  Increased atmospheric vapor pressure deficit reduces global vegetation growth , 2019, Science Advances.

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

[76]  Hans Peter Schmid,et al.  Chronic water stress reduces tree growth and the carbon sink of deciduous hardwood forests , 2014, Global change biology.

[77]  R. A. Ruiz,et al.  Light interception and radiation use efficiency in temperate quinoa (Chenopodium quinoa Willd.) cultivars , 2008 .

[78]  Christopher M. Gough,et al.  Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery , 2012 .

[79]  A. Viña,et al.  Comparison of different vegetation indices for the remote assessment of green leaf area index of crops , 2011 .

[80]  Atul K. Jain,et al.  A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis , 2012 .

[81]  M. Fischer,et al.  Vulnerability of crops and native grasses to summer drying in the U.S. Southern Great Plains , 2015 .

[82]  Philippe Ciais,et al.  Terrestrial biosphere model performance for inter‐annual variability of land‐atmosphere CO2 exchange , 2012 .

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

[84]  K. Schulz,et al.  Identification of a general light use efficiency model for gross primary production , 2010 .

[85]  W. Oechel,et al.  A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS , 2008 .

[86]  Alfredo R. Huete,et al.  MODIS seasonal and inter-annual responses of semiarid ecosystems to drought in the Southwest U.S.A , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[87]  Per Ambus,et al.  Biosphere-atmosphere exchange of reactive nitrogen and greenhouse gases at the NitroEurope core flux measurement sites: Measurement strategy and first data sets , 2009 .

[88]  K. Davis,et al.  Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data , 2010 .