Fraction Of Absorbed Photosynthetically Active Radiation Research Articles

Generalized FPAR estimation methods from various satellite sensors and validation

Fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter in ecosystem productivity and carbon balance estimation. FPAR can be estimated from various satellite images but its product might have significant differences due to the usage of various algorithms. This work proposes a generalized FPAR retrieval method for Landsat 5/ Thematic Mapper (TM), Landsat 7/Enhanced Thematic Mapper Plus (ETM+), Landsat 8/ Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer(MODIS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SPOT/VEGETATION, and HJ-1/CCD in the form of two linear models, namely, the BOA (bottom of atmosphere) model and the TOA (top of atmosphere) model, to reduce FPAR discrepancy among sensors. The BOA model estimates canopy FPAR from land surface multiband reflectance after atmospheric correction, whereas the TOA model estimates FPAR from apparent multiband reflectance at the TOA. Analysis results found that the FPAR errors from the BOA and TOA models were approximately 0.03 and 0.06, respectively, and the difference among FPAR estimated from different sensors was turned out to be less than 0.015 in theory. In addition, the FPAR difference between the two models was generally small, especially under low aerosol optical depth (AOD) and densely vegetated conditions. Ground validation using the datasets from the HiWATER and Validation of Land European Remote Sensing Instruments (VALERI) programs showed that the FPAR errors were 0.16 and 0.18 for the BOA and TOA models, respectively, which might be affected by the time interval of ground and satellite observation, spatial scale effect, and atmospheric correction errors. Moreover, this paper applied the new methods to estimate FPAR in different dates at the Heihe River basin and conduct a cross-comparison of FPAR from various sensors, and consequently obtained acceptable results.

THE IMPACTS OF TYPICAL DROUGHT EVENTS ON TERRESTRIAL VEGETATION IN CHINA

Abstract. In our study, according to the statistical results of standardized precipitation evapotranspiration index (SPEI), we chose two drought events which occurred in the North China during 2001 and in the Southwest China from 2009 to 2010. And two of the Global Land Surface Satellite (GLASS) products had been used to evaluate the impacts of drought on vegetation, including the leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR). The results show that: (1) In the development process of a drought event, the anomaly of remote sensing parameters (LAI and FAPAR) usually falls firstly and then rises as the drought changes from moderate to severe and then to moderate. This indicates that the effects of drought on vegetation remote sensing parameters are closely related to the severity of drought disaster. (2) The response of different vegetation types to the drought disaster is different. Compared with the forests, the response of grasslands to drought disaster is earlier. For example, the duration affected by drought disaster in grassland is longer 1/3 than the forests in the Southwest China. (3) Irrigation is an effective measure to mitigate the effects of drought. Irrigated croplands are less affected by drought than non-irrigated croplands and grasslands. In the North China, the decrease amplitude of irrigated croplands’ remote sensing parameters is about half of non-irrigated croplands’.

Improving Satellite Estimates of the Fraction of Absorbed Photosynthetically Active Radiation Through Data Integration: Methodology and Validation

The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input in many climate and ecological models. The accuracy of satellite FAPAR products directly influences estimates of ecosystem productivity and carbon stocks. The targeted accuracy of FAPAR products is 10% or 0.05 for many applications. However, most current FAPAR products do not meet such requirements, and further improvements are still needed. In this paper, a data fusion scheme based on the multiple resolution tree (MRT) approach is developed to integrate multiple satellite FAPAR estimates at site and regional scales. MRT was chosen because of the superior computational efficiency compared with other fusion methods. The fusion scheme removed the bias in FAPAR estimates and resulted in a 15% increase in the $R^{2}$ and 3% reduction in the root-mean-square error compared with the average of individual FAPAR estimates. The regional-scale fusion filled in the missing values, and provided spatially consistent FAPAR distributions at different resolutions. Overall, MRT can be used to efficiently and accurately generate spatially and temporally continuous FAPAR data across both site and regional scales.

Derivation of global vegetation biophysical parameters from EUMETSAT Polar System

This paper presents the algorithm developed in LSA-SAF (Satellite Application Facility for Land Surface Analysis) for the derivation of global vegetation parameters from the AVHRR (Advanced Very High Resolution Radiometer) sensor on board MetOp (Meteorological–Operational) satellites forming the EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Polar System (EPS). The suite of LSA-SAF EPS vegetation products includes the leaf area index (LAI), the fractional vegetation cover (FVC), and the fraction of absorbed photosynthetically active radiation (FAPAR). LAI, FAPAR, and FVC characterize the structure and the functioning of vegetation and are key parameters for a wide range of land–biosphere applications. The algorithm is based on a hybrid approach that blends the generalization capabilities offered by physical radiative transfer models with the accuracy and computational efficiency of machine learning methods. One major feature is the implementation of multi-output retrieval methods able to jointly and more consistently estimate all the biophysical parameters at the same time. We propose a multi-output Gaussian process regression (GPRmulti), which outperforms other considered methods over PROSAIL (coupling of PROSPECT and SAIL (Scattering by Arbitrary Inclined Leaves) radiative transfer models) EPS simulations. The global EPS products include uncertainty estimates taking into account the uncertainty captured by the retrieval method and input errors propagation. A sensitivity analysis is performed to assess several sources of uncertainties in retrievals and maximize the positive impact of modeling the noise in training simulations. The paper discusses initial validation studies and provides details about the characteristics and overall quality of the products, which can be of interest to assist the successful use of the data by a broad user’s community. The consistent generation and distribution of the EPS vegetation products will constitute a valuable tool for monitoring of earth surface dynamic processes.

Seasonality of albedo and FAPAR in a boreal forest

Satellite data are continuously used to monitor albedo and fraction of absorbed photosynthetically active radiation (FAPAR), which are key components in determining the energy balance and productivity of forests. However, due to the mismatch between spatial resolution of the satellite data and forest stand size, coarse resolution satellite products cannot capture the fine-scale variations in forest structure. Therefore, forest radiation budget models are important tools in quantifying albedo and FAPAR at stand scale. However, due to the lack of suitable input data, simulations are often restricted to summer conditions only and the seasonal patterns are not considered. We modeled the time series of albedo and FAPAR for an entire growing season for 20 forest plots in the boreal zone in Finland (61°50′N, 24°17′ E) using an exceptional ground reference data set. Canopy gap fractions and the spectra of forest floor were monitored in the plots throughout the growing season. Data on the seasonality of spectra of tree foliage were also available. The modeled albedo and FAPAR were upscaled and compared against albedo and FAPAR derived from MODIS satellite data. We showed that forest radiation budget models capable of adequately taking into account foliage clumping and its effects on multiple scattering are the most appropriate for simulating albedo of boreal coniferous forests. Our results also indicated negative albedo-productivity relations in boreal coniferous forests. In addition, we demonstrated that not only the overall level, but also the seasonal patterns of albedo and FAPAR differ between tree species. Therefore, the use of only peak growing season albedo or FAPAR values when estimating climate impacts of forest management can be misleading.

Detecting impacts of extreme events with ecological in situ monitoring networks

Abstract. Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with ≈ 100 randomly placed sites in Europe yield a ≥ 90 % chance of detecting the eight largest (typically very large) extreme events; but only a ≥ 50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere.

A Method for Consistent Estimation of Multiple Land Surface Parameters From MODIS Top-of-Atmosphere Time Series Data

Most methods for generating global land surface products from satellite data are parameter specific and do not use multiple temporal observations, which often results in spatial and temporal discontinuity and physical inconsistency among different products. This paper proposes a data assimilation (DA) scheme to simultaneously estimate five land surface parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) time series reflectance data under clear and cloudy conditions. A coupled land surface–atmosphere radiative transfer model is developed to simulate TOA reflectance, and an ensemble Kalman filter technique is used to retrieve the most influential surface parameters of the coupled model, such as leaf area index, by combining predictions from dynamic models and the MODIS TOA reflectance data whether under clear or cloudy conditions. Then, the retrieved surface parameters are input to the coupled model to calculate four other parameters: 1) land surface reflectance; 2) incident photosynthetically active radiation (PAR); 3) land surface albedo; and 4) the fraction of absorbed PAR (FAPAR). The estimated parameters are compared with those of the corresponding MODIS, the Global LAnd Surface Satellite, and the Geoland2/BioPar version 1 (GEOV1) products. Validation of the estimated parameters against ground measurements from several sites with different vegetation types demonstrates that this method can estimate temporally complete land surface parameter profiles from MODIS TOA time series reflectance data, with accuracy comparable to that of existing satellite products over the selected sites. The retrieved leaf area index profiles are smoother than the existing satellite products, and unlike the MOD09GA product, the retrieved surface reflectance values do not have the high peak values influenced by clouds. The use of the coupled land surface–atmosphere model and the DA technique ensures physical connections between the land surface parameters and makes it possible to calculate radiation-related parameters for clear and cloudy atmospheric conditions, which is an improvement for FAPAR retrieval compared with the MODIS and GEOV1 products. The retrieved FAPAR and PAR values can reveal the significant differences in them under clear and cloudy atmospheric conditions.

Simultaneous Estimation of Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Surface Albedo From Multiple-Satellite Data

Leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface broadband albedo are three routinely generated land-surface parameters from satellite observations, which have been widely used in land-surface modeling and environmental monitoring. Currently, most global land products are retrieved separately from individual satellite data. Many issues, such as data gaps, spatial and temporal inconsistencies, and insufficient accuracy under certain conditions resulting from the inadequacies of single-sensor observations, have made the incorporation of multiple sensors a reasonable solution. In this paper, an approach to simultaneous estimation of LAI, broadband albedo, and FAPAR from multiple-satellite sensors is further refined. The method, improved from that proposed in an earlier study using Moderate Resolution Imaging Spectroradiometer (MODIS) data, consists of several steps. First, a coupled dynamic and radiative-transfer model based on MODIS, SPOT/VEGETATION, and Multiangle Imaging SpectroRadiometer data was developed to retrieve LAI values and use them to construct a time-evolving dynamic model. Second, an iteration process with predefined exit criteria was developed to obtain consistent gap-filled LAI estimates. Third, a spectral albedo based on the retrieved LAI values was simulated using a radiative-transfer model and then converted to a broadband albedo using empirical methods. Snow-covered pixels identified by normalized difference snow index thresholds were adjusted to the weighted average of the underlying albedo and the maximum snow albedo. Finally, the FAPAR of green vegetation was calculated as a combination of the albedo at the top of the canopy, the soil albedo, and the transmittance of the PAR down to the background. Validation of retrieved LAI, albedo, and FAPAR values obtained from multiple-satellite data over ten study sites has demonstrated that the proposed method can produce more accurate products than presently distributed global products.

Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer(MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete Radiative Transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model and resulting in an inference of land surface parameters, such as effective Leaf Area Index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with eight years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely-used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, the MISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements ( r 2 > 0.8), as well as the lowest average R M S E (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases ( r 2 > 0.85); the R M S E is lower by 4–5%; and the bias is 2% and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground-based LAI (average r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average R M S E = 2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.

Estimating FAPAR of Rice Growth Period Using Radiation Transfer Model Coupled with the WOFOST Model for Analyzing Heavy Metal Stress

Timely assessment of crop growth conditions under heavy metal pollution is of great significance for agricultural decision-making and estimation of crop productivity. The object of this study is to assess the effects of heavy metal stress on physiological functions of rice through the spatial-temporal analysis of the fraction of absorbed photosynthetically active radiation (FAPAR). The calculation of daily FAPAR is conducted based on a coupled model consisting of the leaf-canopy radiative transfer model and World Food Study Model (WOFOST). These two models are connected by leaf area index (LAI) and a fraction of diffused incoming solar radiation (SKYL) in the rice growth period. The input parameters of the coupled model are obtained from measured data and GF-1 images. Meanwhile, in order to improve accuracy of FAPAR, the crop growth model is optimized by data assimilation. The validation result shows that the correlation between the simulated FAPAR and the measured data is strong in the rice growth period, with the correlation coefficients being above 7.5 for two areas. The discrepancy of FAPAR between two areas of different stress levels is visualized by spatial-temporal analysis. FAPAR discrepancy starts to appear in the jointing-booting period and experiences a gradual rise, reaching its maximum in the heading-flowering stage. This study suggests that the coupled model, consisting of the leaf-canopy radiative transfer model and the WOFOST model, is able to accurately simulate daily FAPAR during crop growth period and FAPAR can be used as a potential indicator to reflect the impact of heavy metal stress on crop growth.

A comparison of Dynamic Habitat Indices derived from different MODIS products as predictors of avian species richness

Understanding past and current patterns of species richness is essential for predicting how these patterns may be affected by future global change. The species energy hypothesis predicts that higher abundance and richness of animal species occur where available energy is higher and more consistently available. There is a wide range of remote sensing proxies for available energy, such as vegetation productivity, but it is not clear which best predict species richness. Our goal here was to evaluate different proxies for annual plant productivity from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) as input for the Dynamic Habitat Indices (DHIs), and to determine how well they predict the richness of breeding bird species in six functional guilds across the conterminous United States. The DHIs are measures of vegetation productivity over the course of a year and consist of three components: (1) cumulative productivity (DHI Cum), (2) minimum productivity (DHI Min), and (3) intra-annual variation of productivity (DHI Var). We hypothesized that increases in cumulative and minimum productivity and reductions in intra-annual variation will be associated with higher species richness. We calculated the DHIs from a range of MODIS 1000-m vegetation productivity data sets for 2003–2014, i.e., the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Fraction of absorbed Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). We summarized bird species richness of different guilds within ecoregions (n=85) based on abundance maps derived from the >3000 routes of the North American Breeding Bird Survey for 2006 to 2012. Generally, we found all the DHIs had high explanatory power for predicting breeding bird species richness. However, the strength of the associations between the DHIs and bird species richness depended on habitat, nest placement, and migratory behavior. We found highest correlations for habitat-based guilds, such as grassland breeding species (R2adj 0.66–0.73 for the multiple DHI regression model; R2adj 0.41–0.61 for minimum DHI) and woodland breeding species (R2adj 0.34–0.60 for the multiple DHI regression model; R2adj 0.26–0.51 for cumulative DHI). The strong relationship between the DHIs and bird species richness reinforces the importance of vegetation productivity as a determinant of species diversity patterns, and the usefulness of satellite data for applying the species energy hypothesis to predictions in service to conservation.

Simultaneous inversion of multiple land surface parameters from MODIS optical–thermal observations

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface variables usually focus on individual parameters separately even from the same satellite observations, resulting in inconsistent products. Moreover, no efforts have been made to generate global products from integrated observations from the optical to Thermal InfraRed (TIR) spectrum. Particularly, Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal, which contains both reflected and emitted radiation.In this paper, we propose a unified algorithm for simultaneously retrieving six land surface parameters – Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Emissivity (LSE), Land Surface Temperature (LST), and Upwelling Longwave radiation (LWUP) by exploiting MODIS visible-to-TIR observations. We incorporate a unified physical radiative transfer model into a data assimilation framework. The MODIS visible-to-TIR time series datasets include the daily surface reflectance product and MIR-to-TIR surface radiance, which are atmospherically corrected from the MODIS data using the Moderate Resolution Transmittance program (MODTRAN, ver. 5.0). LAI was first estimated using a data assimilation method that combines MODIS daily reflectance data and a LAI phenology model, and then the LAI was input to the unified radiative transfer model to simulate spectral surface reflectance and surface emissivity for calculating surface broadband albedo and emissivity, and FAPAR. LST was estimated from the MIR–TIR surface radiance data and the simulated emissivity, using an iterative optimization procedure. Lastly, LWUP was estimated using the LST and surface emissivity. The retrieved six parameters were extensively validated across six representative sites with different biome types, and compared with MODIS, GLASS, and GlobAlbedo land surface products. The results demonstrate that the unified inversion algorithm can retrieve temporally complete and physically consistent land surface parameters, and provides more accurate estimates of surface albedo, LST, and LWUP than existing products, with R2 values of 0.93 and 0.62, RMSE of 0.029 and 0.037, and BIAS values of 0.016 and 0.012 for the retrieved and MODIS albedo products, respectively, compared with field albedo measurements; R2 values of 0.95 and 0.93, RMSE of 2.7 and 4.2K, and BIAS values of −0.6 and −2.7K for the retrieved and MODIS LST products, respectively, compared with field LST measurements; and R2 values of 0.93 and 0.94, RMSE of 18.2 and 22.8W/m2, and BIAS values of −2.7 and −14.6W/m2 for the retrieved and MODIS LWUP products, respectively, compared with field LWUP measurements.

Important Variables of a RapidEye Time Series for Modelling Biophysical Parameters of Winter Wheat

High-resolution agricultural monitoring, e.g. the robust derivation of biophysical parameters through-out the cropping season and at subfield level, is gaining importance for agricultural management (pre-cision agriculture) and relies on high resolution remote sensing data (e.g. RapidEye or Sentinel-2). This data can then be utilized for regular mapping of biophysical parameters such as the fraction of absorbed photosynthetic active radiation (FPAR), the leaf area index (LAI) and the chlorophyll con-tent. Currently the development of methods for robust mapping of these biophysical parameters is matter of subject in research. At the same time, enormous data amounts will challenge processing capacities and a wise selection and reduction of data will improve the applicability of remote sensing in agriculture. Biophysical parameters were modelled with RapidEye data on winter wheat in Mecklenburg-West Pomerania, Germany, using Random Forest based on conditional inference trees. The study aims at the selection of the most important information out of spectral bands and indices for parameter predic-tion on winter wheat. In-situ and remote sensing observations were grouped into phenological phases in order to examine the importance of single spectral bands or indices for modelling biophysical reality in the several growing stages of winter wheat. Model accuracies for FPAR ranged between a coeffi-cient of determination of 0.19 and 0.83, showing, that the model accuracy is linked with the phenolog-ical phase. The results showed that for each biophysical parameter, different spectral variables become important for modelling and the number of important variables depends on the phenological time span. The prediction of biophysical parameters for short phenological groups, often depends only on one to three variables. The results also showed, that in the phenological phase of fruit development, the model accuracy is the lowest and the determination of the importance is more vague.

Quantifying the missing link between forest albedo and productivity in the boreal zone

Abstract. Albedo and fraction of absorbed photosynthetically active radiation (FAPAR) determine the shortwave radiation balance and productivity of forests. Currently, the physical link between forest albedo and productivity is poorly understood, yet it is crucial for designing optimal forest management strategies for mitigating climate change. We investigated the relationships between boreal forest structure, albedo and FAPAR using a radiative transfer model called Forest Reflectance and Transmittance model FRT and extensive forest inventory data sets ranging from southern boreal forests to the northern tree line in Finland and Alaska (N = 1086 plots). The forests in the study areas vary widely in structure, species composition, and human interference, from intensively managed in Finland to natural growth in Alaska. We show that FAPAR of tree canopies (FAPARCAN) and albedo are tightly linked in boreal coniferous forests, but the relationship is weaker if the forest has broadleaved admixture, or if canopies have low leaf area and the composition of forest floor varies. Furthermore, the functional shape of the relationship between albedo and FAPARCAN depends on the angular distribution of incoming solar irradiance. We also show that forest floor can contribute to over 50 % of albedo or total ecosystem FAPAR. Based on our simulations, forest albedos can vary notably across the biome. Because of larger proportions of broadleaved trees, the studied plots in Alaska had higher albedo (0.141–0.184) than those in Finland (0.136–0.171) even though the albedo of pure coniferous forests was lower in Alaska. Our results reveal that variation in solar angle will need to be accounted for when evaluating climate effects of forest management in different latitudes. Furthermore, increasing the proportion of broadleaved trees in coniferous forests is the most important means of maximizing albedo without compromising productivity: based on our findings the potential of controlling forest density (i.e., basal area) to increase albedo may be limited compared to the effect of favoring broadleaved species.

A Simple Harmonic Model for FAPAR Temporal Dynamics in the Wetlands of the Volga-Akhtuba Floodplain

The paper reports a technique used to construct a reference time series for the fraction of absorbed photosynthetically-active radiation (FAPAR) based on remotely-sensed data in the largest Russian arid wetland territory. For the arid Volga-Akhtuba wetlands, FAPAR appears to be an informative spectral index for estimating plant cover health and its seasonal and annual dynamics. Since FAPAR algorithms are developed for multiple satellite sensors, all FAPAR-based models are suggested to be universal and useful for future studies and long-term monitoring of plant cover, particularly in wetlands. The model developed in the present work for FAPAR temporal dynamics clearly reflects the field-observed seasonal and annual changes of plant cover in the Volga-Akhtuba floodplain wetlands. Various types of wetland plant communities were categorized by the specific parameters of the model seasonal vegetation curve. In addition, the values derived from the model function allow quantitative estimation of wetland plant cover health. This information is particularly important for the Volga-Akhtuba floodplain, because its hydrological regime is regulated by the Volzhskaya hydropower plant. The ecosystem is extremely fragile and sensitive to human impact, and wetland plant cover health is a key indicator of regulatory efficiency. The present study is another step towards developing a methodology focused on arid wetland vegetation monitoring and conservation of its biodiversity and natural conditions.

Do Agrometeorological Data Improve Optical Satellite-Based Estimations of the Herbaceous Yield in Sahelian Semi-Arid Ecosystems?

Quantitative estimates of forage availability at the end of the growing season in rangelands are helpful for pastoral livestock managers and for local, national and regional stakeholders in natural resource management. For this reason, remote sensing data such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) have been widely used to assess Sahelian plant productivity for about 40 years. This study combines traditional FAPAR-based assessments with agrometeorological variables computed by the geospatial water balance program, GeoWRSI, using rainfall and potential evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483 kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase in relative inter-annual variation. In particular, the additional use of agrometeorological information mitigated the saturation effects that characterize the plant indices of areas with high plant productivity. In addition, the date of the onset of the growing season derived from smoothed FAPAR seasonal dynamics showed no significant relationship (0.05 p-level) with the annual herbaceous yield across the whole studied area. The date of the onset of rainfall however, was significantly related to the herbaceous yield and its inclusion in fodder biomass models could constitute a significant improvement in forecasting risks of a mass herbaceous deficit at an early stage of the year.

Estimation of fraction of absorbed photosynthetically active radiation from multiple satellite data: Model development and validation

Abstract The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input in numerous climatological and ecological models. The targeted accuracy of FAPAR products is 10%, or 0.05, for many applications. However, most of the FAPAR products in current usage have not yet fulfilled the accuracy requirement, thus requiring further improvements. In this study, a new FAPAR estimation model is developed on the basis of the radiative transfer (RT) for a horizontally homogeneous continuous canopy. The spatially explicit parameterization of leaf-scattering and soil background reflectance is derived from a 13-year Moderate Resolution Imaging Spectroradiometer (MODIS) albedo database. The new algorithm requires the input of leaf area index (LAI), which is estimated by a hybrid geometric optical-RT model suitable for both continuous and discrete vegetation canopies in this study. The model calculated radiative surface fluxes, i.e., canopy reflectance, absorption, and transmittance, are compared with the reference data from Radiation transfer Model Intercomparison (RAMI) exercise. The evaluation results show that the model estimated FAPAR has an uncertainty of 0.08 over homogeneous and heterogeneous canopies. The FAPAR estimates from the new model are intercompared with reference satellite FAPAR products and validated with ground-based measurements at the Validation of Land European Remote Sensing Instruments (VALERI) AmeriFlux experimental sites. The validation results show that the FAPAR estimates from the new model are comparable to or slightly better in performance than the MODIS and the Multi-angle Imaging SpectroRadiometer (MISR) FAPAR products when using corresponding satellite LAI product values as the input. The FAPAR estimates are further improved when using the new LAI estimates from the hybrid model as the input. The new model adequately identifies the growing seasons and produces smooth time series curves of estimated FAPAR during a specific duration. The uncertainty is reduced to 0.1 when validating with total FAPAR measurements, and 0.08 when validating with green FAPAR measurements. The improvements are apparent in grasslands and forests with an uncertainty reduction of 0.06. The regional-scale application of the presented model generates consistent FAPAR maps at spatial resolutions of 30 m, 500 m, and 1 km from the Landsat, MODIS, and MISR data, respectively.

Consistent estimation of multiple parameters from MODIS top of atmosphere reflectance data using a coupled soil-canopy-atmosphere radiative transfer model

Abstract Traditional methods usually estimate individual land surface parameters separately from surface reflectance data, a practice which requires atmospheric correction for top of atmosphere (TOA) reflectance and may result in physical inconsistency among parameters. This paper proposes a new method for consistent estimation of multiple land surface parameters and aerosol optical depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) TOA reflectance data. Soil, vegetation canopy, and atmospheric radiative transfer models were coupled, and a global sensitivity analysis of the coupled model was performed to identify the influential parameters from satellite observations. The most influential parameters, including leaf area index (LAI) and aerosol optical depth (AOD), were simultaneously retrieved from MODIS TOA reflectance data and then provided as input to the coupled soil-canopy-atmosphere radiative transfer model to calculate land surface reflectance, incident photosynthetically active radiation (PAR), land surface albedo, and the fraction of absorbed photosynthetically active radiation (FAPAR). The retrieved land surface parameters and AOD were compared with the corresponding MODIS, Global Land Surface Satellite (GLASS), GEOV1, and Multi-angle Imaging Spectroradiometer (MISR) products and validated by ground measurements from seven sites with different vegetation types. The results demonstrate that the new inversion method can effectively produce multiple physically consistent parameters with accuracy comparable to that of existing satellite products over the select sites.

Spatial-temporal variation of fraction of absorbed photosynthetically active radiation (FPAR) in Beijing during 2010-2012.

In this study, the spatial distribution of fraction of absorbed photosynthetically active radiation (FPAR) of vegetation in Beijing during 2010-2012 was analyzed based on the MODIS-FPAR data with a resolution of 1 km and the functional classification of vegetation. The yearly dynamics of FPAR of different vegetation types were also investigated. Furthermore, the correlation between FPAR and leaf area index (LAI) was explored. The results showed that, there was a spatially descended trend from the northeast mountains to the central urban area of Beijing. The annual average FPAR of different vegetation types changed little during 2010-2012. These values varied in the ranges of 0.42-0.44, 0.38-0.39, 0.32-0.33, and 0.21-0.22 for coniferous forest, broadleaf forest, grassland, and crop, respectively. However, the range of variation was big for each vegetation type within the year. There was significant linear or logarithm relationship between FPAR and LAI of each vegetation type. Additionally, the smooth time series of MODIS-FPAR of vegetation types could better reflect seasonal variation, which was generated by Savitzky-Golay filtering in software Timesat.

Retrieval of Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from VIIRS Time-Series Data

Long-term high-quality global leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) products are urgently needed for the study of global change, climate modeling, and many other problems. As the successor of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) will continue to provide global environmental measurements. This paper aims to generate longer time series Global LAnd Surface Satellite (GLASS) LAI and FAPAR products after the era of the MODIS sensor. To ensure spatial and temporal consistencies between GLASS LAI/FAPAR values retrieved from different satellite observations, the GLASS LAI/FAPAR retrieval algorithms were adapted in this study to retrieve LAI and FAPAR values from VIIRS surface reflectance time-series data. After reprocessing of the VIIRS surface reflectance to remove remaining effects of cloud contamination and other factors, a database generated from the GLASS LAI product and the reprocessed VIIRS surface reflectance for all Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites was used to train general regression neural networks (GRNNs). The reprocessed VIIRS surface reflectance data from an entire year were entered into the trained GRNNs to estimate the one-year LAI values, which were then used to calculate FAPAR values. A cross-comparison indicates that the LAI and FAPAR values retrieved from VIIRS surface reflectance were generally consistent with the GLASS, MODIS and Geoland2/BioPar version 1 (GEOV1) LAI/FAPAR values in their spatial patterns. The LAI/FAPAR values retrieved from VIIRS surface reflectance achieved good agreement with the GLASS LAI/FAPAR values (R2 = 0.8972 and RMSE = 0.3054; and R2 = 0.9067 and RMSE = 0.0529, respectively). However, validation of the LAI and FAPAR values derived from VIIRS reflectance data is now limited by the scarcity of LAI/FAPAR ground measurements.