Optimizing the photosynthetic parameter Vcmax by assimilating MODIS-fPAR and MODIS-NDVI with a process-based ecosystem model

Hu, Shi; Mo, Xingguo; Lin, Zhifeng

doi:10.1016/j.agrformet.2014.09.002

Cited by 27 publications

(18 citation statements)

References 66 publications

(79 reference statements)

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“…including the lower (T low ) and upper (T upp ) temperatures for photosynthesis, n ef f and n l (0) are tuned to the maximum leaf assimilation expression from Penning de Vries et al (1989) (see Table 2) for each crop. These values are consistent with the wider literature (Hu et al, 2014;Sinclair et al, 2000;Olsovska et al, 2016;Xue, 2015;Makino, 2003;Ogbaga, 2014).…”

Section: Model Simulationssupporting

confidence: 92%

Developing a sequential cropping capability in the JULESvn5.2 land–surface model

Mathison¹,

Challinor²,

Deva³

et al. 2019

Preprint

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Abstract. Sequential cropping (also known as multiple or double cropping) is a common feature, particularly for tropical regions, where the crop seasons are largely dictated by the main wet season such as the Asian summer monsoon (ASM). The ASM provides the water resources for crops grown for the whole year, thereby influencing crop production outside the ASM period. Land surface models (LSMs) typically simulate a single crop per year, however, in order to understand how sequential cropping influences demand for resources, we need to simulate all of the crops grown within a year in a seamless way. In this paper we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at Avignon, a site that uses the sequential cropping system and provides over 15-years of continuous flux observations which we use to evaluate JULES with sequential cropping. In order to implement the method in future regional simulations where there may be large variations in growing conditions, we apply the same method to four locations in the North Indian states of Uttar Pradesh and Bihar to simulate the rice--wheat rotation and compare model yields to observations at these locations. JULES is able to simulate sequential cropping at Avignon and the four India locations, representing both crops within one growing season in each of the crop rotations presented. At Avignon the maxima of LAI, above ground biomass and canopy height occur at approximately the correct time for both crops. The magnitudes of biomass, especially for winter wheat, are underestimated and the leaf area index is overestimated. The JULES fluxes are a good fit to observations (r-values greater than 0.7), either using grasses to represent crops or the crop model, implying that both approaches represent the surface coverage correctly. For the India simulations, JULES successfully reproduces observed yields for the eastern locations, however yields are under estimated for the western locations. This development is a step forward in the ability of JULES to simulate crops in tropical regions, where this cropping system is already prevalent, while also providing the opportunity to assess the potential for other regions to implement it as an adaptation to climate change.

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Section: Model Simulationssupporting

confidence: 92%

Developing a sequential cropping capability in the JULESvn5.2 land–surface model

Mathison¹,

Challinor²,

Deva³

et al. 2019

Preprint

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show abstract

“…In the simulation, it is found that the spatiotemporal evolution of greenness is a crucial factor regulating the yield patterns. Greenness represented by the vegetation index is an appropriate indicator of crop productivity under environmental stresses (Hu et al, 2014). In the areas with a high vegetation index and favourable irrigation facilities, the yield losses may be caused by heat waves or pest infections in the mature stage.…”

Section: Validation With the Statistical Yield Recordsmentioning

confidence: 99%

Attributing regional trends of evapotranspiration and gross primary productivity with remote sensing: a case study in the North China Plain

Chen

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Attributing changes in evapotranspiration (ET) and gross primary productivity (GPP) is crucial for impact and adaptation assessment of the agro-ecosystems to climate change. Simulations with the VIP model revealed that annual ET and GPP slightly increased from 1981 to 2013 over the North China Plain. The tendencies of both ET and GPP were upward in the spring season, while they were weak and downward in the summer season. A complete factor analysis illustrated that the relative contributions of climatic change, CO 2 fertilization, and management to the ET (GPP) trend were 56 (−32) %, −28 (25) %, and 68 (108) %, respectively. The decline of global radiation resulted from deteriorated aerosol and air pollution was the principal cause of GPP decline in summer, while air warming intensified the water cycle and advanced the plant productivity in the spring season. Generally, agronomic improvements were the principal drivers of crop productivity enhancement.

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“…Canopy LAI is a state variable used by most studies because it directly reflects the growth of the crop. In addition, FAPAR [36,37], ET [7,38], leaf nitrogen accumulation [39], vegetation indices [21,25,31], and band reflectance [17] also demonstrate the potential as state variables for remote sensing assimilation to optimize initial parameters for crop models. The variational method attributes the model input, output, and the model's own error to the uncertainty of the initial conditions or the parameters of the model, and do not consider the state variable estimation error during the time evolution of the model parameters.…”

Section: Introductionmentioning

confidence: 98%

“…The variational method takes all available observations during the main growth season and attempts to fit the model to the observations by minimizing the cost function, thereby optimizing the initial parameters of crop models. The variational methods for remote sensing and crop model assimilation have been reported, including, shuffled complex evolution simplex algorithm (SCE-UA) [7,[17][18][19][20], four-dimensional variational data assimilation (4DVAR) [9,[21][22][23][24], particle swarm optimization (PSO) [25][26][27][28][29][30], Powell's conjugate direction method (Powell) [6,23,31,32], simplex search algorithm (SSA) [33,34], maximum likelihood solution (MLS) [35], golden section searching (GSS) [36], and annealing algorithm (AA) [21,24,37]. Canopy LAI is a state variable used by most studies because it directly reflects the growth of the crop.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts

et al. 2019

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In order to enhance the simulated accuracy of jujube yields at the field scale, this study attempted to employ SUBPLEX algorithm to assimilate remotely sensed leaf area indices (LAI) of four key growth stages into a calibrated World Food Studies (WOFOST) model, and compare the accuracy of assimilation with the usual ensemble Kalman filter (EnKF) assimilation. Statistical regression models of LAI and Landsat 8 vegetation indices at different developmental stages were established, showing a validated R2 of 0.770, 0.841, 0.779, and 0.812, and a validated RMSE of 0.061, 0.144, 0.180, and 0.170 m2 m−2 for emergence, fruit filling, white maturity, and red maturity periods. The results showed that both SUBPLEX and EnKF assimilations significantly improved yield estimation performance compared with un-assimilated simulation. The SUBPLEX (R2 = 0.78 and RMSE = 0.64 t ha−1) also showed slightly better yield prediction accuracy compared with EnKF assimilation (R2 = 0.73 and RMSE = 0.71 t ha−1), especially for high-yield and low-yield jujube orchards. SUBPLEX assimilation produced a relative bias error (RBE, %) that was more concentrated near zero, being lower than 10% in 80.1%, and lower than 20% in 96.1% for SUBPLEX, 72.4% and 96.7% for EnKF, respectively. The study provided a new assimilation scheme based on SUBPLEX algorithm to employ remotely sensed data and a crop growth model to improve the field-scale fruit crops yield estimates.

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Optimizing the photosynthetic parameter Vcmax by assimilating MODIS-fPAR and MODIS-NDVI with a process-based ecosystem model

Cited by 27 publications

References 66 publications

Developing a sequential cropping capability in the JULESvn5.2 land–surface model

Developing a sequential cropping capability in the JULESvn5.2 land–surface model

Attributing regional trends of evapotranspiration and gross primary productivity with remote sensing: a case study in the North China Plain

Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts

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