Fraction Of Photosynthetically Active Radiation Research Articles

Spatiotemporal Dynamics in Vegetation GPP over the Great Khingan Mountains Using GLASS Products from 1982 to 2015

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (p < 0.05) during the period of 1982–2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas.

Estimating terrestrial gross primary productivity in water limited ecosystems across Africa using the Southampton Carbon Flux (SCARF) model

The amount of carbon uptake by vegetation is an important component to understand the functioning of ecosystem processes and their response/feedback to climate. Recently, a new diagnostic model called the Southampton Carbon Flux (SCARF) Model driven by remote sensing data was developed to predict terrestrial gross primary productivity (GPP) and successfully applied in temperate regions. The model is based on the concept of quantum yield of plants and improves on the previous diagnostic models by (i) using the fraction of photosynthetic active radiation absorbed by the photosynthetic pigment (FAPARps) and (ii) using direct quantum yield by classifying the vegetation into C3 or C4 classes. In this paper, we calibrated and applied the model to evaluate GPP across various ecosystems in Africa. The performance of the model was evaluated using data from seven eddy covariance flux tower sites. Overall, the modelled GPP values showed good correlation (R>0.59, p<0.0001) with estimated flux tower GPP at most sites (except at a tropical rainforest site, R=0.38, p=0.02) in terms of their seasonality and absolute values. Mean daily GPP across the investigated period varied significantly across sites depending on the vegetation types from a minimum of 0.44gCm−2day−1 at the semi-arid and sub-humid savanna grassland sites to a maximum of 9.86gCm−2day−1 at the woodland and tropical rain forest sites. Generally, strong correlation is observed in savanna woodlands and grasslands where vegetation follows a prescribed seasonal cycle as determined by changes in canopy chlorophyll content and leaf area index. Finally, the mean annual GPP value for Africa predicted by the model was 35.25PgCyr−1. The good performance of the SCARF model in water-limited ecosystems across Africa extends its potential for global application.

Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data

Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP) and annual net primary production (NPP) are contained in MODerate Resolution Imaging Spectroradiometer (MODIS) products (MOD17), which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS) LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types.

Predicting canopy biophysical properties and sensitivity of plant carbon uptake to water limitations with a coupled eco-hydrological framework

Variations in soil water availability and atmospheric water demand impact seasonal canopy dynamics are often represented by the fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI) under a coupled eco-hydrologic framework. Changes in FPAR and LAI lead to changes in evapotranspiration (ET) and gross primary productivity (GPP), coupling the water and carbon cycles. In this study, a predictive Dynamic Canopy Biophysical Properties (DCBP) model is adapted to predict daily FPAR and LAI forced by observed and modeled meteorological and root-zone soil moisture conditions, respectively. Vegetation green-up and die-off responses to temperature (T), vapor pressure deficit (VPD), soil water potential (ψsoil), and photoperiod (Pht) are modeled through a modified form of the growing season index (GSI). The DCBP model parameterizations of seasonality (T and Pht) and intraseasonal water stress (VPD and ψsoil) are calibrated separately for distinct plant functional types (PFTs) and soil types using a Bayesian estimator. To investigate the impact of dynamic phenology on modeled GPP and hydrologic processes, phenology predicted by the DCBP model is input to the Duke Coupled Hydrology Model with Prognostic Vegetation (DCHM-PV), and hydrologic conditions are input to the DCBP model to specify water availability constraints. The coupled model framework was evaluated against AmeriFlux tower data and remotely sensed FPAR and LAI products. Sensitivity analysis of the predicted daily FPAR, LAI, and GPP to the diurnal cycle of root zone water indicates that mid-day soil water availability is the primary control on seasonality across different PFTs and soil textures in the DCBP model. Further, calibrated parameters describing plant-water relations change significantly depending on whether the inference period used in the data assimilation includes persistent meteorological drought, thus effectively resulting in distinct plant water use strategies in the DCHM-PV. The dynamics of water stress recovery are examined by mapping seasonal phenology into the phase space of soil water stress and carbon uptake. In the Southeast U.S., simulated annual differences in GPP can be as high as 350 g C/m2/year with ET increases up to 125 mm/year during wet years. These values represent first order estimates of the dynamics of plant-water use feedbacks on the water and carbon budgets, and highlight the need to incorporate vegetation-specific phenology responses to water availability in order to accurately estimate the terrestrial carbon storage component of the global carbon budget at local and regional scales.

Monitoring the impact of feral horses on vegetation condition using remotely sensed fPAR: A case study in Australia's alpine parks

Throughout the world, feral horses ( Equus caballus ) are causing environmental degradation and a decline in ecological integrity. Evidence from scientific monitoring is needed to inform the public debate and help land managers make informed decisions. We used field observations of vegetation condition at a network of sites in the Australian Alps where horses were present or absent. The data were combined with the remotely -sensed fraction of photosynthetic active radiation (fPAR) and topographic condition. Vegetation condition was assessed in the field by rangers using a modified version of the Landscape Function Analysis (LFA) index. We found significant differences in the LFA index between sites where horses were present or absent. Sites with presence of horses have 10 per cent lower fPAR than sites with absence of horses. The results also indicated a significant correlation between LFA and fPAR. Our analysis supports the hypothesis that feral horses have a negative impact on the condition of Australian alpine vegetation. This study provides a useful and relatively cost-effective method for monitoring the impact of feral horses on native vegetation, and can be used to support decision making and management interventions.

Relationship between MRPV Model Parameters from MISRL2 Land Surface Product and Land Covers: A Case Study within Mainland Spain

In this study, we showed that the multi-angle satellite remote sensing product, MISR L2 Land Surface (MIL2ASLS), which has a scale of 1.1 km, could be suitable for improving land-cover studies. Using seven images from this product, captured by the multi-angle imaging spectroradiometer sensor (MISR), we explored the values reached by the three parameters (ρ0, Θ, and k) of the Rahman–Pinty–Verstraete model, which was modified by Martonchick (MRPV). Thereafter, we compared the values and behaviors shown in seven Co-ordination of Information on the Environment (CORINE) land cover categories, in the red and near infrared (NIR) bands, over the seven MISR orbits captured in 2006 for Mainland Spain. Furthermore, we used Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) ancillary data and the illumination angles from the same pixels, which made up the images. These ancillary data were also provided by the MISR products. An inferential statistic test was performed to evaluate the relationship between each parameter–band combination, and the land cover in every MISR orbit used. The results suggested that the ρ0 parameters of this product seemed to be the most related to photosynthetic activity, and it should be comparable with the widely-used NDVI. On the other hand, the k and Θ parameter values were not related, or at least not entirely related, to the phenology of land coverage. These seemed to be more influenced by the anisotropy behavior of the studied land cover pixels. Additionally, we observed, by constructing analysis of variance, how the mean of each MRPV parameter–band differed statistically (p &lt; 0.01) by land covers and orbits. This study suggested that the MISR MRPV model parameter data product has great potential to be used to improve land cover applications.

Incorporating diffuse radiation into a light use efficiency and evapotranspiration model: An 11-year study in a high latitude deciduous forest

The fraction of diffuse photosynthetic active radiation (PAR) reaching the land surface is one of the biophysical factors regulating carbon and water exchange between terrestrial ecosystems and the atmosphere. This is especially relevant for high latitude ecosystems, where cloudy days are prevalent. Without considering impacts of diffuse PAR, traditional ‘top-down’ models of ecosystem gross primary productivity (GPP) and evapotranspiration (ET), which use satellite remote sensing observations, are biased towards clear sky conditions. This study incorporated a cloudiness index (CI), an index for the fraction of diffuse PAR, into a joint ‘top-down’ model that uses the same set of biophysical constraints to simulate GPP and ET for a high latitude temperate deciduous forest. To quantify the diffuse PAR effects, CI along with other environmental variables derived from an eleven-year eddy covariance data set were used to statistically explore the independent and joint effects of diffuse PAR on GPP, ET, incident light use efficiency (LUE), evaporative fraction (EF) and ecosystem water use efficiency (WUE). The independent and joint effects of CI were compared from global sensitivity analysis of the ‘top-down’ models. Results indicate that for independent effects, CI increased GPP, LUE, ET, EF and WUE. Analysis of joint effects shows that CI mainly interacted with the radiation intercepted in the canopy (PAR, net radiation and leaf area index) to influence GPP, ET and WUE. Moreover, Ta and vapor pressure saturation deficit played a major role for the joint influence of CI on LUE and EF. In the growing season from May to October, variation in CI accounts for 11.9%, 3.0% and 7.8% of the total variation of GPP, ET and transpiration, respectively. As the influence of CI on GPP is larger than that on ET, this leads to an increase in WUE with CI. Joint GPP and ET model results showed that when including CI, the root mean square errors (RMSE) of daily GPP decreased from 1.64 to 1.45gCm−2d−1 (11.7% reduction) and ET from 15.79 to 14.50 Wm−2 (8.2% reduction). Due to the interaction of diffuse PAR with plant canopies, the largest model improvements using CI for GPP and ET occurred during the growing season and for the transpiration component, as suggested by comparisons to sap flow measurements. Furthermore, our study suggests a potential biophysical mechanism, not considered in other studies: under high diffuse PAR conditions, due to the increased longwave emission from clouds, canopy temperature gets higher and enhances GPP and transpiration in this temperature-limited high latitude ecosystem.

Estimation of net primary productivity of forests by modified CASA models and remotely sensed data

ABSTRACTTo increase the accuracy of predicting net primary productivity (NPP), in this study, Carnegie–Ames–Stanford Approach (CASA) model was modified by developing new methods to estimate absorbed photosynthetically active radiation or fraction of photosynthetically active radiation (FPAR) and water stress coefficient (WSC). In the modified model, FPAR was derived based on its non-linear relationship with leaf area index. Moreover, WSC was estimated using leaf water potential from soil moisture instead of a traditional evapotranspiration-based method. This study was conducted in Baiyun District area of Guangzhou, China, using Gaofen-1 (GF-1), Landsat 7, and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. The predictions from the original and three modified CASA models and MODIS NPP product MOD17A3 were compared with field observations. The results showed that all the CASA-based models led to similar spatial distributions of forest aboveground NPP estimates. Overall, the estimates increased with elevation because the valley bottoms were dominated by developed or urbanized areas whereas the hillslopes and hilltops were largely vegetated. Based on root mean square error (RMSE) and relative RMSE between the observed and predicted values, the CASA model that integrated the modifications of both FPAR and WSC increased the estimation accuracy of NPP by 8.1% over the original one. The increase in accuracy was mainly contributed by the modification of FPAR. This suggested that the modification of FPAR provided greater potential than that of WSC for improving the predictions of CASA model. Compared to the CASA models, MOD17A3 had lower accuracy of aboveground NPP estimates. This study also showed that the fine spatial resolution GF-1 image provided a new source of data used to estimate NPP of forest ecosystems.

Improving the prediction of African savanna vegetation variables using time series of MODIS products

African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2=0.79, relative Root Mean Square Error, rRMSE=1.9%) and tree cover (R2=0.78, rRMSE=0.3%). EVI provided the best model for shrub density (R2=0.82) and shrub cover (R2=0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2=0.76), shrubs (R2=0.83), and grass (R2=0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees’ and shrubs’ variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.

Estimation of FPAR and FPAR profile for maize canopies using airborne LiDAR

FPAR (fraction of photosynthetically active radiation) and FPAR profile (vertical FPAR distribution) are important parameters for characterizing the vegetation growth status and studying global climate change. Few studies have been carried out to estimate FPAR and FPAR profile using waveform LiDAR data. This research explored the potential of airborne small-footprint full-waveform LiDAR in the estimation of FPAR and FPAR profile of the maize canopy in Huailai County of Hebei Province, China. First, the maize growing area was identified by a simple decision tree model. Second, raw waveform data were processed to extract LiDAR-derived energy ratio and energy ratio profile. Third, FPAR and FPAR profile were estimated from LiDAR-derived metrics. Finally, we analyzed the FPAR and FPAR profile estimation results and assessed the model validity using the leave-one-out cross-validation (LOOCV) method. The comparative analyses found that the LiDAR-derived energy ratio profile and field-measured FPAR profile had the same trend and similar change rate for all maize layers. The accuracy assessments indicated that the FPAR and FPAR profile were estimated well by the LiDAR waveform data, with the high R2 (0.90 for the whole canopy, and 0.95, 0.90, 0.93, 0.92, and 0.97 for layers 1–5) and low RMSEs (0.042 for the whole canopy, and 0.033, 0.035, 0.039, 0.043, and 0.044 for layers 1–5). The spatial distribution map of FPAR was produced to describe the maize growth status of the whole study area, and the map showed that the FPAR distributed relatively uniformly. This study suggested that airborne small-footprint full-waveform LiDAR was useful in accurately measuring FPAR and FPAR profile of the maize canopy and in effectively mapping the maize FPAR spatial distribution.

Remotely Sensed Estimation of Net Primary Productivity (NPP) and Its Spatial and Temporal Variations in the Greater Khingan Mountain Region, China

We improved the CASA model based on differences in the types of land use, the values of the maximum light use efficiency, and the calculation methods of solar radiation. Then, the parameters of the model were examined and recombined into 16 cases. We estimated the net primary productivity (NPP) using the NDVI3g dataset, meteorological data, and vegetation classification data from the Greater Khingan Mountain region, China. We assessed the accuracy and temporal-spatial distribution characteristics of NPP in the Greater Khingan Mountain region from 1982 to 2013. Based on a comparison of the results of the 16 cases, we found that different values of maximum light use efficiency affect the estimation more than differences in the fraction of photosynthetically active radiation (FPAR). However, the FPARmax and the constant Tε2 values did not show marked effects. Different schemes were used to assess different model combinations. Models using a combination of parameters established by scholars from China and the United States produced different results and had large errors. These ideas are meaningful references for the estimation of NPP in other regions. The results reveal that the annual average NPP in the Greater Khingan Mountain region was 760 g C/m2·a in 1982–2013 and that the inter-annual fluctuations were not dramatic. The NPP estimation results of the 16 cases exhibit an increasing trend. In terms of the spatial distribution of the changes, the model indicated that the values in 75% of this area seldom or never increased. Prominent growth occurred in the areas of Taipingling, Genhe, and the Oroqen Autonomous Banner. Notably, NPP decreased in the southeastern region of the Greater Khingan Mountains, the Hulunbuir Pasture Land, and Holingol.

Validation of collection of 6 MODIS/Terra and MODIS/Aqua gross primary production in an alpine meadow of the Northern Tibetan Plateau

ABSTRACTModerate Resolution Imaging Spectroradiometer (MODIS) continuously monitors gross primary production (GPP), which is an extremely important component of carbon cycling, at the global scale. Uncertainties about MODIS GPP limit our ability to accurately quantify GPP at the regional scales. The Collection 6 MODIS/Terra and MODIS/Aqua GPP products (i.e. MOD17A2H and MYD17A2H) were compared with the estimated GPP (GPPEC) by eddy covariance measurements in an alpine meadow in the Northern Tibetan Plateau during three consecutive growing seasons of 2005–2007. The Collection 6 MODIS/Terra and MODIS/Aqua fractional photosynthetically active radiation (FPAR) products (i.e. MOD15A2H and MYD15A2H) were also validated. The MOD17A2H and MYD17A2H products tended to overestimate GPPEC by 2.17% and 7.35% in 2005–2007, respectively, although these differences were not significant. The MOD15A2H and MYD15A2H products also tended to overestimate ground-based FPAR (FPARG) by 20.31% and 24.73% in 2005–2007, respectively. The overestimation of FPAR resulted in about 17.51–23.97% overestimation of GPPEC. The default maximum light-use efficiency (εmax) of 0.86 g C MJ−1 only underestimated the ground-based εmax (0.88 g C MJ−1) by 2.27%, which in turn resulted in about 2.13–2.72% underestimation of GPPEC. The meteorology data errors only caused about 0.48–1.06% underestimation of GPPEC. Therefore, although MODIS Collection 6 GPP had a very high accuracy, the input parameters had relative greater errors in the alpine meadow of the Northern Tibetan Plateau. The differences between MODIS GPP and GPPEC mainly resulted from FPAR, followed by εmax and meteorological data.

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

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 (R2 = 0.62) and even worse over eight days (R2 = 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 (εmax). It was found that the current land cover classification scheme exhibited frequent misclassification errors. Moreover, the εmax value assigned in MOD17A2H was much smaller than the inferred εmax value. Therefore, the qualities of FPAR and land cover classification datasets should be upgraded, and the εmax value needs to be adjusted to provide more accurate GPP estimates using MOD17A2H for global ecosystems.

Prototyping of LAI and FPAR Retrievals from MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are key variables in many global models of climate, hydrology, biogeochemistry, and ecology. These parameters are being operationally produced from Terra and Aqua MODIS bidirectional reflectance factor (BRF) data. The MODIS science team has developed, and plans to release, a new version of the BRF product using the multi-angle implementation of atmospheric correction (MAIAC) algorithm from Terra and Aqua MODIS observations. This paper presents analyses of LAI and FPAR retrievals generated with the MODIS LAI/FPAR operational algorithm using Terra MAIAC BRF data. Direct application of the operational algorithm to MAIAC BRF resulted in an underestimation of the MODIS Collection 6 (C6) LAI standard product by up to 10%. The difference was attributed to the disagreement between MAIAC and MODIS BRFs over the vegetation by −2% to +8% in the red spectral band, suggesting different accuracies in the BRF products. The operational LAI/FPAR algorithm was adjusted for uncertainties in the MAIAC BRF data. Its performance evaluated on a limited set of MAIAC BRF data from North and South America suggests an increase in spatial coverage of the best quality, high-precision LAI retrievals of up to 10%. Overall MAIAC LAI and FPAR are consistent with the standard C6 MODIS LAI/FPAR. The increase in spatial coverage of the best quality LAI retrievals resulted in a better agreement of MAIAC LAI with field data compared to the C6 LAI product, with the RMSE decreasing from 0.80 LAI units (C6) down to 0.67 (MAIAC) and the R2 increasing from 0.69 to 0.80. The slope (intercept) of the satellite-derived vs. field-measured LAI regression line has changed from 0.89 (0.39) to 0.97 (0.25).

MODIS vegetation products as proxies of photosynthetic potential along a gradient of meteorologically and biologically driven ecosystem productivity

Abstract. A direct relationship between gross ecosystem productivity (GEP) estimated by the eddy covariance (EC) method and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) has been observed in many temperate and tropical ecosystems. However, in Australian evergreen forests, and particularly sclerophyll and temperate woodlands, MODIS VIs do not capture seasonality of GEP. In this study, we re-evaluate the connection between satellite and flux tower data at four contrasting Australian ecosystems, through comparisons of GEP and four measures of photosynthetic potential, derived via parameterization of the light response curve: ecosystem light use efficiency (LUE), photosynthetic capacity (Pc), GEP at saturation (GEPsat), and quantum yield (α), with MODIS vegetation satellite products, including VIs, gross primary productivity (GPPMOD), leaf area index (LAIMOD), and fraction of photosynthetic active radiation (fPARMOD). We found that satellite-derived biophysical products constitute a measurement of ecosystem structure (e.g. leaf area index – quantity of leaves) and function (e.g. leaf level photosynthetic assimilation capacity – quality of leaves), rather than GEP. Our results show that in primarily meteorological-driven (e.g. photosynthetic active radiation, air temperature, and/or precipitation) and relatively aseasonal ecosystems (e.g. evergreen wet sclerophyll forests), there were no statistically significant relationships between GEP and satellite-derived measures of greenness. In contrast, for phenology-driven ecosystems (e.g. tropical savannas), changes in the vegetation status drove GEP, and tower-based measurements of photosynthetic activity were best represented by VIs. We observed the highest correlations between MODIS products and GEP in locations where key meteorological variables and vegetation phenology were synchronous (e.g. semi-arid Acacia woodlands) and low correlation at locations where they were asynchronous (e.g. Mediterranean ecosystems). However, we found a statistical significant relationship between the seasonal measures of photosynthetic potential (Pc and LUE) and VIs, where each ecosystem aligns along a continuum; we emphasize here that knowledge of the conditions in which flux tower measurements and VIs or other remote sensing products converge greatly advances our understanding of the mechanisms driving the carbon cycle (phenology and climate drivers) and provides an ecological basis for interpretation of satellite-derived measures of greenness.

Is row orientation a determinant factor for radiation interception in row vineyards?

Background and Aims This study analysed the effect of row direction on photosynthetically active radiation (PAR) absorption (APAR) and the fraction of PAR absorbed (fPAR) on both a daily and hourly temporal scale in vertical shoot positioned trellised vineyards. Methods and Results The analysis included experimental data and model simulations. The data of fPAR and APAR were acquired in commercial plots with a range of canopy development and with row directions near to the north–south, northeast–southwest and east–west orientations. Simulated values of fPAR and APAR were calculated for north–south, northeast–southwest, northwest–southeast and east–west row directions and for a range of geographical latitudes, from 30 to 50°N to represent the wide range of latitudes where grapes are grown round the world. In agreement with previous research, the results confirm the strong daily variation of fPAR and APAR, and the instantaneous values of these parameters are not representative of the daily period in row vineyards. The field data and model results suggest the advantage of the north–south and northeast–southwest row orientations to maximise PAR absorption for latitudes between 30 and 50°N. And the exploratory analysis based on model simulation indicates that PAR absorption is lower for the east–west row direction in all latitudes analysed. Conclusions The interrelations between radiation regimen and physiological aspects related to climatic limitations in canopy conductance result in a relative advantage of the north–south and northeast–southwest row directions against other orientations in arid and semi-arid environments. Significance of the Study The determination of the potential PAR absorption for a given vineyard training system enables a good first step towards understanding the effect of canopy management on vineyard productivity. In addition, these results are of interest for plot design and simulation of vineyard development.

Remote sensing and ground-based measurements of evapotranspiration in an extreme cold Patagonian desert

Accurate estimates of seasonal evapotranspiration (ET) at different temporal and spatial scales are essential for understanding the biological and environmental determinants of ecosystem water balance in arid regions and the patterns of water utilization by the vegetation. For this purpose, remote sensing ET estimates of a Patagonian desert in Southern Argentina were verified with field measurements of soil evaporation and plant transpiration using an open top chamber. Root distribution and seasonal variation in soil volumetric water content were also analysed. There was a high correlation between remote sensing and field measurements of ecosystem water fluxes. A substantial amount of the annual ET occurred in spring and early summer (73.4 mm) using winter rain stored in the soil profile and resulting in water content depletion of the upper soil layers. A smaller amount of annual ET was derived from few rainfall events occurring during the mid or late summer (41.4 mm). According to remote sensing, the 92.9% of the mean annual precipitation returns to the atmosphere by transpiration or evaporation from the bare soil and by canopy interception. Only 7.1% infiltrates to soil layers deeper than 200 cm contributing to the water table recharge. Fourier time series analysis, cross-correlation methods and multiple linear regression models were used to analyse 11 years of remote sensing data to assess determinants of water fluxes. A linear model predicts well the variables that drive complex ecosystem processes such as ET. Leaf area index and air temperature were not linearly correlated to ET because of the multiple interaction among variables resulting in time lags with ET variations and thus these two variables were not included in the linear model. Soil water content, the fraction of photosynthetic active radiation and precipitation explained 86% of the ET monthly variations. The high volumetric water content and the small seasonal variations at 200-cm depth were probably the result of little water uptake from deeper soil horizons by roots with low hydraulic conductivity. Copyright © 2016 John Wiley & Sons, Ltd.

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

An exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250m spatial resolution while the other products were 1000m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250m vs. 1000m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008–2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000m resolution NDVI showed somewhat poorer performance than the 250m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.

Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements

As the latest version of Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products, Collection 6 (C6) has been distributed since August 2015. This collection is evaluated in this two-part series with the goal of assessing product accuracy, uncertainty and consistency with the previous version. In this first paper, we compare C6 (MOD15A2H) with Collection 5 (C5) to check for consistency and discuss the scale effects associated with changing spatial resolution between the two collections and benefits from improvements to algorithm inputs. Compared with C5, C6 benefits from two improved inputs: (1) L2G–lite surface reflectance at 500 m resolution in place of reflectance at 1 km resolution; and (2) new multi-year land-cover product at 500 m resolution in place of the 1 km static land-cover product. Global and seasonal comparison between C5 and C6 indicates good continuity and consistency for all biome types. Moreover, inter-annual LAI anomalies at the regional scale from C5 and C6 agree well. The proportion of main radiative transfer algorithm retrievals in C6 increased slightly in most biome types, notably including a 17% improvement in evergreen broadleaf forests. With same biome input, the mean RMSE of LAI and FPAR between C5 and C6 at global scale are 0.29 and 0.091, respectively, but biome type disagreement worsens the consistency (LAI: 0.39, FPAR: 0.102). By quantifying the impact of input changes, we find that the improvements of both land-cover and reflectance products improve LAI/FPAR products. Moreover, we find that spatial scale effects due to a resolution change from 1 km to 500 m do not cause any significant differences.

Estimation of rice biophysical parameters using multitemporal RADARSAT-2 images

Compared with optical sensors, synthetic aperture radar (SAR) has the capability of acquiring images in all-weather conditions. Thus, SAR images are suitable for using in rice growth regions that are characterized by frequent cloud cover and rain. The objective of this paper was to evaluate the probability of rice biophysical parameters estimation using multitemporal RADARSAT-2 images, and to develop the estimation models. Three RADARSTA-2 images were acquired during the rice critical growth stages in 2014 near Meishan, Sichuan province, Southwest China. Leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), height, biomass and canopy water content (WC) were observed at 30 experimental plots over 5 periods. The relationship between RADARSAT-2 backscattering coefficients (σ0) or their ratios and rice biophysical parameters were analysed. These biophysical parameters were significantly and consistently correlated with the VV and VH σ0 ratio (σ0VV/ σ0VH) throughout all growth stages. The regression model were developed between biophysical parameters and σ0VV/ σ0VH. The results suggest that the RADARSAT-2 data has great potential capability for the rice biophysical parameters estimation and the timely rice growth monitoring.