Effects of canopy photosynthesis saturation on the estimation of gross primary productivity from MODIS data in a tropical forest

Propastin, Pavel; Ibrom, Andreas; Knohl, Alexander; Erasmi, Stefan

doi:10.1016/j.rse.2012.02.005

Cited by 67 publications

(59 citation statements)

References 47 publications

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“…Finally, the use of a constant value attributed to the maximum value for light use efficiency (LUE) cannot be justified for the same biome type [18]. All sources of error mentioned above (meteorological data, FPAR product, land cover classification result and LUE value) require separate examination on an individual basis [19].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

et al. 2017

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Abstract:The latest MODIS GPP (gross primary productivity) product, MOD17A2H, has great advantages over the previous version, MOD17A2, because the resolution increased from 1000 m to 500 m. In this study, MOD17A2H GPP was assessed using the latest eddy covariance (EC) flux data (FLUXNET2015 Dataset) at eighteen sites in six ecosystems across the globe. The sensitivity of MOD17A2H GPP to the meteorology dataset and the fractional photosynthetically-active radiation (FPAR) product was explored by introducing site meteorology observations and improved Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) products. The results showed that MOD17A2H GPP underestimated flux-derived GPP at most sites. Its performance in estimating annual GPP was poor (R 2 = 0.62) and even worse over eight days (R 2 = 0.52). For the MOD17A2H algorithm, replacing the reanalysis meteorological datasets with the site meteorological measurements failed to improve the estimation accuracies. However, great improvements in estimating the site-based GPP were gained by replacing MODIS FPAR with GLASS FPAR. This indicated that in the existing MOD17A2H product, the errors were originated more from FPAR than the meteorological data. We further examined the potential error contributions from land cover classification and maximum light use efficiency (ε max ). It was found that the current land cover classification scheme exhibited frequent misclassification errors. Moreover, the ε max value assigned in MOD17A2H was much smaller than the inferred ε max value. Therefore, the qualities of FPAR and land cover classification datasets should be upgraded, and the ε max value needs to be adjusted to provide more accurate GPP estimates using MOD17A2H for global ecosystems.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

et al. 2017

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“…They rely on the physical description of the energy-matter interaction. However, every physical model strongly depends on empirical data for calibration and validation and also faces problems of saturation in regions with high biomass (see, e.g., [234] for MODIS GPP modelling). Semi-empirical approaches combine both empirical and physical modelling, e.g., by using the output from CR models to train neural networks to estimate biophysical parameters [235].…”

Section: Physical Vs Empirical Modelsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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“…MODIS GPP products have been evaluated for different ecosystems in various studies [34][35][36][37][38][39]. Uncertainties in the original products included those from biome-specific parameters, input data, and vegetation maps [40].…”

Section: The Mod_17 Modelmentioning

confidence: 99%

Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation

Yan

Tian

et al. 2016

Remote Sensing

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Abstract:This study improved simulation of forest carbon fluxes in the Changbai Mountains with a process-based model (Biome-BGC) using incorporation and data assimilation. Firstly, the original remote sensing-based MODIS MOD_17 GPP (MOD_17) model was optimized using refined input data and biome-specific parameters. The key ecophysiological parameters of the Biome-BGC model were determined through the Extended Fourier Amplitude Sensitivity Test (EFAST) sensitivity analysis. Then the optimized MOD_17 model was used to calibrate the Biome-BGC model by adjusting the sensitive ecophysiological parameters. Once the best match was found for the 10 selected forest plots for the 8-day GPP estimates from the optimized MOD_17 and from the Biome-BGC, the values of sensitive ecophysiological parameters were determined. The calibrated Biome-BGC model agreed better with the eddy covariance (EC) measurements (R 2 = 0.87, RMSE = 1.583 gC¨m´2¨d´1) than the original model did (R 2 = 0.72, RMSE = 2.419 gC¨m´2¨d´1). To provide a best estimate of the true state of the model, the Ensemble Kalman Filter (EnKF) was used to assimilate five years (of eight-day periods between 2003 and 2007) of Global LAnd Surface Satellite (GLASS) LAI products into the calibrated Biome-BGC model. The results indicated that LAI simulated through the assimilated Biome-BGC agreed well with GLASS LAI. GPP performances obtained from the assimilated Biome-BGC were further improved and verified by EC measurements at the Changbai Mountains forest flux site (R 2 = 0.92, RMSE = 1.261 gC¨m´2¨d´1).

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Effects of canopy photosynthesis saturation on the estimation of gross primary productivity from MODIS data in a tropical forest

Cited by 67 publications

References 47 publications

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation

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