fAPAR Products Research Articles

The severity of dry and hot climate extremes and their related impacts on vegetation in Madagascar

The severity of dry and hot climate extremes and their related impacts on vegetation in Madagascar

Integration of Sentinel-3 OLCI Land Products and MERRA2 Meteorology Data into Light Use Efficiency and Vegetation Index-Driven Models for Modeling Gross Primary Production

Accurately and reliably estimating total terrestrial gross primary production (GPP) on a large scale is of great significance for monitoring the carbon cycle process. The Sentinel-3 satellite provides the OLCI FAPAR and OTCI products, which possess a higher spatial and temporal resolution than MODIS products. However, few studies have focused on using LUE models and VI-driven models based on the Sentinel-3 satellites to estimate GPP on a large scale. The purpose of this study is to evaluate the performance of Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data in estimating GPP at site and regional scale. Firstly, we integrated OLCI FAPAR and meteorology reanalysis data into the MODIS GPP algorithm and eddy covariance light use efficiency (EC-LUE) model (GPPMODIS-GPP and GPPEC-LUE, respectively). Then, we combined OTCI and meteorology reanalysis data with the greenness and radiation (GR) model and vegetation index (VI) model (GPPGR and GPPVI, respectively). Lastly, GPPMODIS-GPP, GPPEC-LUE, GPPGR, and GPPVI were evaluated against the eddy covariance flux data (GPPEC) at the site scale and MODIS GPP products (GPPMOD17) at the regional scale. The results showed that, at the site scale, GPPMODIS-GPP and GPPEC-LUE agreed well with GPPEC for the US-Ton site, with R2 = 0.73 and 0.74, respectively. The performance of GPPGR and GPPVI varied across different biome types. Strong correlations were obtained across deciduous broadleaf forests, mixed forests, grasslands, and croplands. At the same time, there are overestimations and underestimations in croplands, evergreen needleleaf forests and deciduous broadleaf forests. At the regional scale, the annual mean and maximum daily GPPMODIS-GPP and GPPEC-LUE agreed well with GPPMOD17 in 2017 and 2018, with R2 > 0.75. Overall, the above findings demonstrate the feasibility of using Sentinel-3 OLCI FAPAR and OTCI products combined with meteorology reanalysis data through LUE and VI-driven models to estimate GPP, and fill in the gaps for the large-scale evaluation of GPP via Sentinel-3 satellites.

Accuracy assessment on the number of flux terms needed to estimate in situ fAPAR

The fraction of Absorbed Photosynthetically Active Radiation (fAPAR) is a crucial variable for assessing global carbon balances and currently, there is an urgent need for reference data to validate satellite-derived fAPAR products. However, it is well-known that fAPAR ground measurements are associated with considerable uncertainties. Generally, fAPAR measurements can be carried out with two-, three- and four-flux approaches, depending on the number of flux terms measured. Currently, not much is known about the number of flux terms needed to satisfactorily reduce systematic errors. This study investigates the accuracy of different fAPAR estimates based on permanent, 10-min PAR measurements using Wireless Sensor Networks (WSNs) at three forest sites, located in Central Europe (mixed-coniferous forest), North America (boreal-deciduous forest) and Central America (tropical dry forest). All fAPAR estimates reflect the seasonal course of fAPAR. The highest average biases of different fAPAR estimates account to 0.02 at the temperate, 0.08 at the boreal and -0.05 at the tropical site, respectively, thereby generally fulfilling the uncertainty threshold of a maximum of 10 % or 0.05 fAPAR units set by the Global Climate Observing System (GCOS, 2016). During high wind speed conditions at the boreal site, the bias of the two-flux fAPAR estimate exceeded the 0.05-uncertainty threshold. Three-flux fAPAR estimates were not found to be advantageous, especially at the tropical site. Our findings are beneficial for the development of sampling protocols that are needed to validate global satellite-derived fAPAR products.

Validation of Sentinel-2 fAPAR products using ground observations across three forest ecosystems

Accurate estimates of Essential Climate Variables (ECV) such as the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are essential for assessing global carbon balances. ESA's Sentinel-2 (S2) mission with its decametric resolution enables to derive fAPAR information at single forest stand. Validation studies on previously existing satellite-derived fAPAR products have found considerable discrepancies, especially in forest ecosystems that exceed relative discrepancies of 10% (i.e. 0.05 for absolute values) set as target accuracy by the Global Climate Observing System (GCOS).This study presents the validation of S2 fAPAR products using direct radiation measurements of 2017 at three different forests, located in Central Europe (mixed-coniferous forest in temperate mid-latitude), North America (boreal-deciduous forest) and Central America (tropical dry forest, TDF). We measured incoming and transmitted PAR every 10 min synchronously using Wireless Sensor Networks (WSN) and calculated a two-flux fAPAR estimate. We validated the S2 fAPAR product with different instantaneous ground fAPAR estimates (e.g. instantaneous fAPAR at 10:00 and 14:00 local solar time) and daily average fAPAR. We considered uncertainties of ground data, i.e. bias related to the presence of colored autumn leaves and influences of solar zenith angle.Overall, we found high discrepancies between the S2 fAPAR product and ground measurements, indicating that the S2 fAPAR product systematically underestimated (negative values for bias in percent) the ground observations. The highest agreement was observed at the boreal-deciduous forest stand with a bias of −13% (R2 = 0.67). The Central American and European sites reported deviations of −20% (R2 = 0.68) and −25% (R2 = 0.26), respectively. At all sites, we found evidence that particularly the influence of colored leaves during the senescence periods lead to bias of the ground data. Further, the choice of temporal fAPAR estimate, i.e. daily average fAPAR or a certain instantaneous fAPAR estimate, lead to partly different results in the correlation analysis with the S2 fAPAR product. However, considering sources of uncertainties of ground data, we emphasize that only the boreal-deciduous site in Canada fulfilled the accuracy requirements set by the GCOS.In contrast to absolute values, we found strong agreement on phenological changes at all three sites. Specifically, the influence of species composition on seasonal variability of fAPAR across the European mixed-coniferous site was well-represented in the S2 fAPAR product. As for the representation of spatial variability, we found highest agreement at the boreal-deciduous forest stand (BIAS = −22%, R2 = 0.93), whereas spatial variability was least represented at the TDF site (BIAS = 125%, R2 = 0.97). We conclude that the S2 fAPAR product has strong capabilities for assessing temporal variability of fAPAR, but due to low accuracy of absolute values currently limited options to feed global production efficiency models and assess global carbon balances.

Global White-Sky and Black-Sky FAPAR Retrieval Using the Energy Balance Residual Method: Algorithm and Validation

The fraction of absorbed photosynthetically active radiation by vegetation (FAPAR) is a key variable in describing the light absorption ability of the vegetation canopy. Most global FAPAR products, such as MCD15A2H and GEOV1, correspond to FAPAR under black-sky conditions at the satellite overpass time only. In this paper, we aim to produce both the global white-sky and black-sky FAPAR products based on the moderate resolution imaging spectroradiometer (MODIS) visible (VIS) albedo, leaf area index (LAI), and clumping index (CI) products. Firstly, a non-linear spectral mixture model (NSM) was designed to retrieve the soil visible (VIS) albedo. The global soil VIS albedo and its dynamics were successfully mapped at a resolution of 500 m using the MCD43A3 VIS albedo product and the MCD15A2H LAI product. Secondly, a method based on the energy balance residual (EBR) principle was presented to retrieve the white-sky and black-sky FAPAR using the MODIS broadband VIS albedo (white-sky and black-sky) product (MCD43A3), the LAI product (MCD15A2H) and CI products. Finally, the two EBR FAPAR products were compared with the MCD15A2H and Geoland2/BioPar version 1 (GEOV1) black-sky FAPAR products. A comparison of the results indicates that these FAPAR products show similar spatial and seasonal patterns. Direct validation using FAPAR observations from the Validation of Land European Remote sensing Instrument (VALERI) project demonstrates that the EBR black-sky FAPAR product was more accurate and had a lower bias (R2 = 0.917, RMSE = 0.088, and bias = −2.8 %) than MCD15A2H (R2 = 0.901, RMSE = 0.096, and bias = 7.6 % ) and GEOV1 (R2 = 0.868, RMSE = 0.105, and bias = 6.1%).

Assessing the variability and uncertainty of two-flux FAPAR measurements in a conifer-dominated forest

The estimation of the Fraction of Absorbed Active Radiation (FAPAR) and its uncertainties is key for understanding global carbon balances. This work investigates the variability and associated uncertainties of in-situ two-flux FAPAR observations attributed to changes in phenological and meteorological conditions. Specifically, we assessed influences of illumination conditions with solar zenith angle and the ratio of diffuse-to-total incident radiation, wind speed, leaf color and snow coverage on the variability of two-flux FAPAR. We assumed FAPAR acquired under diffuse light conditions to be closest to “true” FAPAR as it is not influenced by the solar zenith angle. To reveal the uncertainty of the two-flux FAPAR measurements, we investigated the difference (Δtwo-flux) between FAPAR acquired under diffuse light conditions and two-flux FAPAR acquired during a certain environmental condition (e.g. large solar zenith angle). A positive (negative) value obtained from this difference was interpreted as an indication for an underestimation (overestimation) of “true” FAPAR by the two-flux FAPAR estimate, as found in previously investigations with canopy radiative transfer models (RTM).Therefore, permanent PAR measurements were carried out 2015–2017 in a sub-alpine, spruce-dominated forest in Southern Germany using a Wireless Sensor Network (WSN). FAPAR observations exhibited considerable seasonal variability (0.89 to 0.99 ± 0.03) despite the dominance of evergreen spruces. The in-situ observations confirm significant overestimation of FAPAR by up to 0.06 under solar zenith angles above 60° and by up to 0.05 during the presence of colored autumn leaves, similarly to the results obtained from previous studies with canopy RTMs. Additionally, our results indicate an effect of wind speed which we consider crucial at sites where high wind speeds occur more frequently. Overall, this study shows the potential of permanent WSN monitoring activities to ensure multi-year FAPAR observations with associated uncertainty information that are demanded to validate satellite-derived FAPAR products in forest ecosystems.

Combining hectometric and decametric satellite observations to provide near real time decametric FAPAR product

A wide range of ecological, agricultural, hydrological and meteorological applications at local to regional scales requires decametric biophysical data. However, before the launch of SENTINEL-2A, only few decametric products are produced and most of them remain limited by the small number of available observations, mostly due to a moderate revisit frequency combined with cloud occurrence. Conversely, kilometric and hectometric biophysical products are now widely available with almost complete and continuous coverage, but the associated spatial resolution limits the application over heterogeneous landscapes. The objective of this study is to combine unfrequent decametric spatial resolution products with frequent hectometric spatial resolution products to improve the temporal frequency and completeness of decametric observations. The study focuses on the fraction of photosynthetically active radiation absorbed by the green vegetation (FAPAR) because of its important role in canopy models and small dependency to scaling issues.An algorithm is developed to provide near real time estimates of FAPAR called DHF (for Decametric Hectometric Fusion) at a decametric resolution and dekadal time step. It is assumed that the FAPAR time course is described by a second-degree polynomial function over a limited 60-days temporal window for each decametric pixel. To reduce the dimensionality of the problem, landcover classes are considered instead of each individual pixel. For each class, the coefficients of the polynomial function are adjusted using the temporal course of the available decametric FAPAR products, under the constraint of providing a good match with the time course of the hectometric dekadal FAPAR products. The point spread function associated to the hectometric FAPAR products and the possible biases between the decametric and hectometric FAPAR products are explicitly accounted for.The algorithm was evaluated over a time series of decametric Landsat-8 FAPAR images (30m) and hectometric (330m) dekadal GEOV3 FAPAR derived from PROBA-V images acquired in 2014 over a site in the South-West of France.Results show that the estimated DHF FAPAR products capture well the expected seasonal variation and spatial distribution while improving the temporal frequency and spatial and temporal completeness of the original Landsat-8 products. A leave one out exercise shows that the DHF values are in very good agreement with the Landsat-8 FAPAR (RMSE=0.05–0.14) that were not used when computing the DHF. This demonstrates the robustness of the algorithm and interest under cloudy regions. Additional comparison with ground measurements collected over 14 sunflower fields along the growth season confirms the good performances of the DHF FAPAR products (RMSE=0.11).

Predictive power of remote sensing versus temperature‐derived variables in modelling phenology of herbivorous insects

AbstractApplication of remote sensing datasets in modelling phenology of heterotrophic animals has received little attention. In this work, we compare the predictive power of remote sensing versus temperature‐derived variables in modelling peak flight periods of herbivorous insects, as exemplified by nocturnal moths. Moth phenology observations consisted of weekly observations of five focal moth species (Orthosia gothica,Ectropis crepuscularia,Cabera exanthemata,Dysstroma citrataandOperophtera brumata) gathered in a national moth monitoring scheme in Finland. These species were common and widespread and had peak flight periods in different seasons. Temperature‐derived data were represented by weekly accumulating growing degree days (GDD) calculated from gridded temperature observations. Remote sensing data were obtained from three sources: (1) snow melt‐off date from theMODISdaily snow maps, (2) greening date using theNDWIfromMODISdata and (3) dates of start, maximum and end of growing season based on theJRC FAPARproducts. Peak phenology observations of moths were related to different explanatory variables by using linear mixed effect models (LMM), with 70% of the data randomly selected for model calibration. Predictive power of models was tested using the remaining 30% of the data. Remote sensing data (snow melt‐off and vegetation greening date) showed the highest predictive power in two moth species flying in the early and late spring, whereas in the three other species none of the variables showed reasonable predictive power. Flight period of the spring species coincides with natural events such as snow melt or vegetation greening that can easily be observed using remote sensing techniques. We demonstrate the applicability of our methodology by predictive spatial maps of peak flight phenology covering the entire Finland for two of the focal species. The methods are applicable in situations that require spatial predictions of animal activity, such as the management of populations of insect pest species.

Validation of PROBA-V GEOV1 and MODIS C5 & C6 fAPAR Products in a Deciduous Beech Forest Site in Italy

The availability of new fAPAR satellite products requires simultaneous efforts in validation to provide users with a better comprehension of product performance and evaluation of uncertainties. This study aimed to validate three fAPAR satellite products, GEOV1, MODIS C5, and MODIS C6, against ground references to determine to what extent the GCOS requirements on accuracy (maximum 10% or 5%) can be met in a deciduous beech forest site in a gently and variably sloped mountain site. Three ground reference fAPAR, differing for temporal (continuous or campaign mode) and spatial sampling (single points or Elementary Sampling Units—ESUs), were collected using different devices: (1) Apogee (defined as benchmark in this study); (2) PASTIS; and (3) Digital cameras for collecting hemispherical photographs (DHP). A bottom-up approach for the upscaling process was used in the present study. Radiometric values of decametric images (Landsat-8) were extracted over the ESUs and used to develop empirical transfer functions for upscaling the ground measurements. The resulting high-resolution ground-based maps were aggregated to the spatial resolution of the satellite product to be validated considering the equivalent point spread function of the satellite sensors, and a correlation analysis was performed to accomplish the accuracy assessment. PASTIS sensors showed good performance as fAPARPASTIS appropriately followed the seasonal trends depicted by fAPARAPOGEE (benchmark) (R2 = 0.84; RMSE = 0.01). Despite small dissimilarities, mainly attributed to different sampling schemes and errors in DHP classification process, the agreement between fAPARPASTIS and fAPARDHP was noticeable considering all the differences between both approaches. The temporal courses of the three satellite products were found to be consistent with both Apogee and PASTIS, except at the end of the summer season when ground data were more affected by senescent leaves, with both MODIS C5 and C6 displaying larger short-term variability due to their shorter temporal composite period. MODIS C5 and C6 retrievals were obtained with the backup algorithm in most cases. The three green fAPAR satellite products under study showed good agreement with ground-based maps of canopy fAPAR at 10 h, with RMSE values lower than 0.06, very low systematic differences, and more than 85% of the pixels within GCOS requirements. Among them, GEOV1 fAPAR showed up to 98% of the points lying within the GCOS requirements, and slightly lower values (mean bias = −0.02) as compared with the ground canopy fAPAR, which is expected to be only slightly higher than green fAPAR in the peak season.

LAI, FAPAR and FCOVER ground-truth map creation from FASat-C satellite imagery and in-situ measurements in Chimbarongo, Chile, for satellite products validation

<p align="justify">In remote sensing, validation exercises are essential to ensure the quality of the products originated from satellite Earth observations. To assess the measurement uncertainty derived from satellite products, several ground field data from different ecosystems must be available for use. In the same order of importance, it is necessary to define data sampling and up-scaling methodologies to allow a suitable comparison between the ground data and the pixel size of the product. This paper shows the applied methodology used in the FP7 ImagineS project (Implementing Multi-scale Agricultural Indicators Exploiting Sentinels) to validate 10-days global LAI, FAPAR and vegetation cover products at 1km spatial resolution using in-situ data. These global products are derived from PROBA-V observations in the Copernicus Global Land Service. In particular, this case study shows the results of the field-campaign carried out in January of 2015 in the agricultural area of Chimbarongo, Chile. The methodology to scale the ground data and to create ground-based maps using FASat-C Chilean satellite imagery with a 5,8 m spatial resolution using multivariate least squares regression is shown. Finally, the same methodology was used with a 30 m spatial resolution Landsat-8 image to analyze the effect of the field-data input on the ground-truth maps used to validate the results. Our results show the reliability on the presented methodology and the consistency of the method with regard to the input data. Better results and lower RMSE errors were obtained using FASat-C data. The comparison with satellite products at 1 km shows a good agreement with Copernicus Global Land products derived from PROBA-V observations, and systematic negative bias for the MODIS products.</p>

A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements

The leaf area index (LAI) and the fraction of photosynthetically active radiation absorbed by green vegetation (FAPAR) are essential climatic variables in surface process models. FCOVER is also important to separate vegetation and soil for energy balance processes. Currently, several LAI, FAPAR and FCOVER satellite products are derived moderate to coarse spatial resolution. The launch of Sentinel-2 in 2015 will provide data at decametric resolution with a high revisit frequency to allow quantifying the canopy functioning at the local to regional scales. The aim of this study is thus to evaluate the performances of a neural network based algorithm to derive LAI, FAPAR and FCOVER products at decametric spatial resolution and high temporal sampling. The algorithm is generic, i.e., it is applied without any knowledge of the landcover. A time series of high spatial resolution SPOT4_HRVIR (16 scenes) and Landsat 8 (18 scenes) images acquired in 2013 over the France southwestern site were used to generate the LAI, FAPAR and FCOVER products. For each sensor and each biophysical variable, a neural network was first trained over PROSPECT+SAIL radiative transfer model simulations of top of canopy reflectance data for green, red, near-infra red and short wave infra-red bands. Our results show a good spatial and temporal consistency between the variables derived from both sensors: almost half the pixels show an absolute difference between SPOT and LANDSAT estimates of lower that 0.5 unit for LAI, and 0.05 unit for FAPAR and FCOVER. Finally, downward-looking digital hemispherical cameras were completed over the main land cover types to validate the accuracy of the products. Results show that the derived products are strongly correlated with the field measurements (R2 > 0.79), corresponding to a RMSE = 0.49 for LAI, RMSE = 0.10 (RMSE = 0.12) for black-sky (white sky) FAPAR and RMSE = 0.15 for FCOVER. It is concluded that the proposed generic algorithm provides a good basis to monitor the seasonal variation of the vegetation biophysical variables for important crops at decametric resolution.

Remote sensing of ecosystem light use efficiency with MODIS-based PRI

Abstract. Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the effectiveness with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependence on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well.

LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products

The main objective of this paper is the validation of CYCLOPES version 3.1 LAI and fAPAR products. It is achieved by the comparison with MODIS collection 4 and 4.1 products and ECOCLIMAP LAI climatology over the BELMANIP representative set of sites, and with ground measurements over a limited set of sites. Great attention is paid to the consistency of the comparison: for the spatial dimension, product PSF appears to be the main aspect governing the spatial resolution at which the comparison has to be achieved. For CYCLOPES, a minimal size of the sites should be 3 km × 3 km 2, while the optimal one is 10 km × 10 km 2; regarding the temporal sampling interval and resolution, the problem is much easier to solve when assuming a relatively smooth time course of vegetation characteristics (8–16 days). Great care was also paid to the departure of products from the nominal definition, particularly for LAI where different scales of clumping have to be considered. Results showed that CYCLOPES and MODIS products have generally consistent seasonality, CYCLOPES being however characterized by a smoother temporal evolution as expected. Differences are mainly concentrated on the magnitude of products values, CYCLOPES achieving better performances both for LAI (RMSE = 0.73) and fAPAR (RMSE = 0.10) over the limited number of sites where ground measurements were available. This study also sets a framework to the validation exercise that could be used to evaluate other products or future versions of the same products and contribute to associate quantitative uncertainties as required by the user community.

Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements

On global and regional scales, earth observation (EO)-based estimates of leaf area index (LAI) provide valuable input to climate and hydrologic modelling, while fraction of absorbed photosynthetically active radiation (fAPAR) is a key variable in the assessment of vegetation productivity and yield estimates. Validation of moderate resolution imaging spectroradiometer (MODIS) LAI and fAPAR products is an important prerequisite to using these variables for global modelling or for local water resource modelling and net primary production (NPP) assessment, as in semi-arid West Africa and Senegal. In situ measurements of LAI and fAPAR from three sites in semi-arid Senegal were carried out in 2001 and 2002 for comparison with remotely sensed MODIS data. The seasonal dynamics of both in situ LAI and fAPAR were captured well by MODIS LAI and fAPAR. MODIS LAI is overestimated by approximately 2–15% and the overall level of fAPAR is overestimated by 8–20%. Both MODIS LAI and fAPAR are characterised by a moderate offset, which is slightly higher than can be explained by model and input data uncertainty. In situ fAPAR and normalised differential vegetation index (NDVI) for three different vegetation types showed a strong linear relationship, suggesting that covariance between fAPAR and NDVI is insensitive to variations in leaf angle distribution (LAD) and vegetative heterogeneity. A strong linear relation also exists between MODIS fAPAR and NDVI but with different regression coefficients than the in situ relation because of MODIS' tendency to overestimate fAPAR. The fAPAR/NDVI relations found here, however, do not apply on a global scale but are only valid for similar sun-sensor view geometry and soil colour.