The Fraction of Photosynthetically Active Radiation (FPAR) is an important index for detecting the vegetation water, energy and carbon balance and is a key parameter in the ecosystem productivity model, crop yield model, and other models [1
]. FPAR is most often defined as the proportion of available photosynthetically active radiation absorbed by the green vegetation canopy in the specific spectrum of 400–700 nm.
Several current operational sensors in remote sensing satellites can provide regional or global FPAR data in a time series, such as Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), Multi-angle Imaging Spectroradiometer (MISR), Global Land Products for Carbon Model Assimilation (GLOBCARBON), Land Surface Analysis Satellite Applications Facility (LANDSAF), Joint Research Centre FAPAR (JRC-FAPAR) and System Observation Probatoire beyond Tarre (SPOT) VEGETATION [6
]. The global remote sensing FPAR product is an important data source because of the inherent technology advantage that can provide continuous temporal and spatial covering data. They have long been used for modeling ecosystem process or estimating agro-ecological parameters [8
]. Connolly et al.
] found MODIS FPAR is a useful data source for the characterization of light use efficiency parameter ɛ at flux tower sites. Nightingale et al.
studied result [11
] showed that MODIS FPAR product can also provide realistic time-series data with a gap-filled and smoothed method for carbon modeling in regions where persistent cloud cover is an issue. Seixas et al.
] emphasized the importance of FPAR product in data-driven ecosystem productivity estimation methods, and found MERIS underestimated FPAR but displayed greater spatial homogeneity than MODIS, as well as compared the MODIS and MERIS FPAR products application in net primary production (NPP) estimates, identifying a seasonal discrepancy. Haverd et al.
] used MODIS and AVHRR FPAR to estimate regional NPP—regional differences up to 15% were identified—and found that seasonal discrepancy in FPAR is a key contributor to seasonal discrepancy in NPP. The existing products distinguish in terms of the time compositing period, retrieval algorithm, spatial resolution or remapping technique. Data users and producers are needed and are eager to understand these FPAR products discrepancies and consistencies [6
]. It is very necessary for validating and assessing the product accuracy to establish confidence in FPAR data sets and to provide a basis for their applications [14
Overall, several methods have been used to validate and assess FPAR products accuracies and their consistencies, and these include direct comparisons with in situ
measured data, comparisons with FPAR data estimated from high-resolution images and inter-comparisons among the global products. Weiss et al.
] found that CYCLOPES FAPAR showed consistent seasonality with MODIS FPAR and performed better than MODIS FPAR. McCallum et al.
] compared four global FPAR products and found that MODIS and CYCLOPES datasets record generally similar but substantially more than JRC and GLOBCARBON datasets. FAPAR products derived from the JRC-TIP (Two-stream Inversion Package) using MODIS and MISR surfaces as inputs exhibited much less variability [17
]. Martinze et al.
] inter-compared and assessed the quality of the MODIS, MERIS and SEVIRI (Spinning Enhanced Visible and InfraRed Imager) FPAR products over the Iberian Peninsula. Pickett-Heaps et al.
] evaluated the consistency of six FPAR products (MODIS, MERIS, SeaWIFS (Sea-Viewing Wide Field-of-View Sensor), MODIS-TIP, SPOT-VEGETATION, and AVHRR (Advanced Very High Resolution Radiometer)) across the Australian continent using multi-year datasets and found that the consistency of FPAR products is sensitive to the biome classification but not the fractional vegetation cover. Serbin et al.
] found MODIS collection 4 (C4) FPAR product overestimated and collection 5 (C5) data were more closely aligned with ground measured FPAR across a boreal forest wildfire chronosequence.
Using high-resolution images and ground-measured data, several investigations indicated MODIS collection 3 (C3) and C4 FPAR products tend to overestimate several land cover classes [20
]. Hummrich et al.
] found there was a significant bias between MODIS C3 FPAR products and ground measured FPAR for woodland in Africa, and land cover misclassification was a possible cause. Steinberg et al.
] indicated that the MODIS C4 FPAR overestimated when compared to Landsat-7 ETM retrieval FPAR in the boreal forests of Alaska. Overall, the MODIS C5 FPAR product has undergone only minimal validation [15
]. Steinberg and Geotz [15
] found the majority of MODIS C4 FPAR was actually derived from empirically based algorithm and C5 showed better data quality but still had limitations in temperate forest areas in the eastern United States. Chen et al.
] found that the difference between MODIS C5 FPAR and TM retrieved FPAR is smaller than the expected level. Fritsch et al.
] validated the MODIS C5 FPAR product with multi-temporal RapidEye images obtained from arid agricultural landscape and found that MODIS C5 FPAR results in overestimation of approximately 6%–15%. The C5 products that are currently available show better data quality than C3 and C4 products, but it still persistent limitations in forest areas [15
]. Gobron et al.
] analyzed the uncertainty of MERIS FPAR products with FPAR field estimates derived from ground measured leaf area index (LAI) and found that the expected accuracy of the MERIS FPAR product was within ± 0.1. Weiss et al.
] found that the CYCLOPES FAPAR product performed well (RMSE = 0.1) for a limited number of ground measurements in Europe and the Americas. The Geoland2 Core Mapping Service BioPAR provided newly the GEOV1 FPAR product [26
], which is based on the CYCLOPES FPAR product and has been combined with the MODIS C5 FPAR product [26
]. The spatial and temporal consistencies of GEOV1 product were preliminarily evaluated via an inter-comparison with other global products [27
Until now, as the authors’ understanding, most of the FPAR validation works were carried out in America, Europe and Africa [14
]. The quality of FPAR products in Asia, particularly in China, still requires validation. Moreover, FPAR product validations have focused primarily on grass and forest areas, but the FPAR data for cropland in Asia has scarcely been validated. The reliability of FPAR products for different biome classes and accuracy variations of FPAR products throughout the growing season in temperate zones also need to be urgently validated [15
The spatial resolution of the moderate-resolution biophysical parameter product is low; there are many mixed pixels, which has much effect on the pixels’ value [30
]. Quantitatively evaluating the mixed pixel effect on FPAR product is very important for global moderate resolution product assessment and algorithm development. Several studies have proposed that the mixed land cover may introduce errors to the global product estimation. Martinez et al.
] found that the MODIS and SEVIRI values rarely exceed FPAR = 0.6, whereas the MERIS FPAR values are concentrated below 0.4. This phenomenon may be partially explained by the lower spatial resolution of these products, the mixed pixel often reduces the real FPAR. The specific biome class within a pixel may be a main source of estimation error because of the different fractional cover and canopy structure [31
]. Nevertheless, MODIS land cover misclassifications cannot fully explain the overestimation or underestimation of MODIS FPAR [23
]. Fang et al.
] evaluated the biome mixture effect based on MODIS standard land cover. The mixture of biome classes in pixel should also impact FPAR accuracy differentially at different vegetation growth stages [34
]. Therefore, the global FPAR product for mixed pixels, such as MODIS, MERIS and GEOV1, must be analyzed based on high resolution land cover or vegetation type data at different vegetation growth stages.
It is worth mentioning that the most notable feature of plains worldwide is the high populations and dense residential distribution. The land area in Chinese plains is also mainly covered by cultivated land and residential area, and the residential areas are sporadically scattered. For moderate-resolution global products in Chinese plains, most pixels are a mixture of cultivated land and residential areas. The sporadically scattered residential areas will likely significantly impact the accuracy of the MODIS, MERIS and GEOV1 FPAR products for their low spatial resolutions in Chinese plains.
Therefore, this investigation will assess and inter-compare the MODIS, MERIS and GEOV1 FPAR products with similar temporal and spatial resolutions over northern China based on the ground measured maize and winter wheat FPAR, considering that these global products cover a large geographical region of China, and will evaluate detailed the residential area in mixed pixel effect on global FPAR product. This study may provide a good foundation for the scientific use of these FPAR products over northern China.
Compared with the ground-measured FPAR, the MODIS, MERIS and GEOV1 global FPAR products all yielded different estimation results for maize and winter wheat. The studied results showed that the MODIS FPAR data are relatively close to the ground-measured data for small FPAR values and obviously higher than the ground-measured data for large FPAR values, these results are similar to those obtained from other studies [19
]. Comparatively, the GEOV1 product can estimate the FPAR relatively well and reliably, particularly for high FPAR values. Camacho et al.
] also found that the regression R2
of GEOV1 FPAR products with ground measured data were larger than MODIS FPAR product. The overall uncertainties (RMSE) of the three products ranged from 0.0806–0.1146 which are close to the required target accuracy (0.05 for FPAR). The mixture of land covers or biomes have a significant effect on biophysical parameters product accuracy, such as on LAI [33
]. This study investigated earlier the residential area in the mixed pixel effect on FPAR accuracy, and found they had a different effect on different crops and different growing stages. This study also showed that residential area in mixed pixels affect greatly the FPAR accuracy especially for residential area percentages above 50%.
It is very important to collect reliable ground measured FPAR data for product validation and even for its utilization in modeling communities [46
]. At present, a few instruments have been widely used, such as the Decagon AccuPAR ceptometer [15
], tracing architecture and radiation of canopies (TRAC) [20
], LI-191SA line quantum sensors or LI-190SZ (LI-COR Inc., Lincoln, NE, USA) [47
], TM Cava devices line quantum sensor [49
] and SKYE PAR quantum sensors (SKP 215 and SKR 110) [21
]. Serbin et al.
] found MODIS C5 FPAR product follows well the temporal evolution of LAI-191SA FPAR data. In this study, the LI-191SA was used for the FPAR measurement.
Moreover, only two parts of the canopy photosynthetically active radiation—the above- and below-PAR—were usually recorded at each spot during the ground measurement, and their ratios were usually calculated as FPAR. However, the soil background or the understory vegetation also absorbed the photosynthetic active radiation, and they should be excluded in the FPAR measurement. Therefore, in this study, the line quantum sensor of LI-191SA was used to measure four fractions above and below the canopies: the incident and reflected PAR above the canopy as well as the incident and reflected PAR below the canopy, and to compute the FPAR data. Meanwhile, three or nine measurements were taken at each sampling site, to reduce the random measurement error of FPAR. They were then averaged to the final FPAR for comparison with global FPAR products. The sampling sites or pixels were determined before carrying out the field measurements, to ensure the ground measurements can represent effectively the actual FPAR value in the exact pixel for comparison. In addition, the study areas for maize and winter wheat are both located in the plains, the topography influence can be ignored. These measurement strategies and conditions can reduce the measurement errors as much as possible.
The studied results showed that residential area in mixed pixel markedly affects the global FPAR product accuracy, although the land cover misclassification introduces small errors to the FPAR products. Meanwhile, the effect of residential area percentage in mixed pixels on FPAR values differ for different crops, and this was not necessarily in accordance with the FPAR product accuracy. Wu et al.
] found that the cultivated land area errors of MODIS land cover data in the Jilin and Shandong provinces are 34% and 15.7%. In Changchun city, Dezhou and Liaocheng city, 91% and 81% of all pixels are mixed pixels that contain cultivated land area. The mixed pixels that only contained cultivated land and residential areas accounted for approximately 71% and 56% of all pixels, respectively. This study revealed that the residential area in mixed pixel have an obvious effect on real crop FPAR estimation, and provided a quantitative function to transform the FPAR value in mixed pixel to pure crop FPAR value in the pixel at each key crop growth stages. It can help to effectively use the FPAR product to improve the light use efficiency estimation accuracy and also improve the simulation accuracy of the relationship between crop canopy and photosynthetically active radiation, and to further improve the accuracy of agronomic production (yield modeling and prediction) or the ecosystem model simulation.
The inter-comparison and quality assessment among these three FPAR products is essential to understand differences and ensure their reliability. Furthermore, they were studied separately based on different biome classes and at different growing stages, and residential area in mixed pixel was also detailed investigated. These can provide a good reference for using several products to avoid having missing FPAR pixel values because of cloudy conditions.
It should be noted that the ground measurements in this study were conducted mostly at the crop high growth stages, and this may limit the evaluations of FPAR products’ accuracy at other growing stages. Therefore, it is necessary to get complete ground measured FPAR data for all the growth stages in future study. Moreover, this study quantitatively investigated earlier the effect of mixed pixels only in plain areas on global FPAR product accuracy, where the pixel mixtures are simple. The land cover mixtures in other areas are more complex. Most pixels of global FPAR products consist of several land cover or biome classes due to the low spatial resolution, which will complicate the quantitative evaluation of mixed pixel effect on FPAR product. The global FPAR product assessment with complex land cover mixtures still needs further investigation.