Mapping daily leaf area index at 30 m resolution over a meadow steppe area by fusing Landsat,Sentinel-2A and MODIS data

ABSTRACT

The leaf area index (LAI) is a key vegetation canopy structure parameter and is closely associated with vegetation photosynthesis, transpiration, and energy balance. Developing a landscape-scale LAI dataset with a high temporal resolution (daily) is essential for capturing rapidly changing vegetation structure at field scales and supporting regional biophysical modeling efforts. In this study, two daily 30 m LAI time series from 2014 to 2016 over a meadow steppe site in northern China were generated using a spatial and temporal adaptive re?ectance fusion model (STARFM) combined with an LAI retrieval radiative transfer model (PROSAIL). Gap-filled Landsat 7, Landsat 8 and Sentinel-2A surface reflectance (SR) images were used to generate fine-resolution LAI maps with the PROSAIL look-up table method. Two daily 500 m moderate-resolution imaging spectroradiometer (MODIS) LAI product-the existing MCD15A3H LAI product and one was generated from the MCD43A4 SR product and the PROSAIL model, were used to provide temporally continuous LAI variations. The STARFM model was then used to fuse the fine-resolution LAI maps with the two 500 m LAI products separately to generate two daily 30 m LAI time series. Both results were assessed for three types of pasture (mowed pasture, grazing pasture, and fenced pasture) using ground measurements from 2014–2015. The results showed that the PROSAIL-generated LAI maps all exhibited a high accuracy, and the root mean squared errors (RMSEs) for the Landsat 7 LAI and Landsat 8 LAI compared to the ground-measured LAI were 0.33 and 0.28 respectively. The Landsat LAI maps also showed good agreement and similar spatial patterns with the Sentinel-2A LAI with mean differences between ± 0.5. The MCD43A4_PROSPECT LAI product exhibited similar seasonal variability to the ground measurements and to the Landsat and Sentinel-2A LAIs, and these data are also smoother and contain fewer noisy points than the gap-filled MCD15A3H LAI product. Compared to the ground measurements, the daily 30 m LAI time series fused from the fine-resolution LAI maps and PROSPECT generated MODIS LAI product demonstrated better performance with an RMSE of 0.44 and a mean absolute error (MAE) of 0.34, which is an improvement from the LAI time series fused from the fine-resolution LAI maps and the existing MCD15A3H LAI product (RMSE of 0.56 and MAE of 0.42). The latter dataset also exhibited abnormal temporal fluctuations, which may have been caused by the interpolation method. The results also demonstrated the very good performance of the STARFM model in grazing and mowed pasture with homogeneous surfaces compared to fenced pasture with smaller patch sizes. The Sentinel-2A data offers increased landscape vegetation observation frequency and provides temporal information about canopy changes that occur between Landsat overpass dates. The scheme developed in this study can be used as a reference for regional vegetation dynamic studies and can be applied to larger areas to improve grassland modeling efforts.     

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km² area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements.     

呼伦贝尔草甸草原MODIS/LAI产品验证

以内蒙古呼伦贝尔草甸草原为研究区域,利用2013年6期地面实测数据,结合HJ-1A/B CCD高分辨率影像,经过辐射校正与模型建立,对研究区域草原生长季的MODIS/LAI产品进行验证。结果表明:在时间上,MODIS/LAI产品能够较好地反映草原的长势与物候变化。在空间上,由于MODIS/LAI产品输入数据的不确定性,MODIS/LAI产品与地面情况存在一定偏差(ΔLAI=0.59m2/m2),在呼伦贝尔草甸草原草场整个生长季都存在高估现象,平均相对误差为40%。在生长初期和末期,较大的地表异质性使MODIS/LAI产品高估现象较严重;生长中期高估现象减小,相对误差在30%以内。研究结果对了解该地区的MODIS/LAI产品精度与使用该地区MODIS/LAI产品具有重要指导意义。     

Data fusion for high spatial resolution LAI estimation

Leaf Area Index (LAI) is a critical variable for forest management. It is difficult to obtain accurate LAI estimations of high spatial resolution over large areas. Local estimations can be obtained from in situ field measurements. Extrapolation of local measurements is prone to error. Remote sensing LAI estimation products, such as the one provided by MODIS are of very low resolution and subject to criticism in recent validation works. Forest management requires increasingly high resolution estimations of LAI. We propose a data fusion process for high spatial resolution estimation of the LAI over a large area, combining several heterogeneous information sources: field sampled data, elevation data and remote sensing data. The process makes use of spatial interpolation techniques. We follow a hybrid validation approach that combines the conventional prediction error measures with a spatial validation based on image segmentation. We obtain encouraging results of this information fusion process on data from a forest area in the north of Portugal.     

Real-time retrieval of Leaf Area Index from MODIS time series data

Real-time/near real-time inversion of land surface biogeophysical variables from satellite observations is required to monitor rapid land surface changes, and provide the necessary input for numerical weather forecasting models and decision support systems. This paper develops a new inversion method for the real-time estimation of the Leaf Area Index (LAI) of land surfaces from MODIS time series reflectance data (MOD09A1). It consists of a series of procedures, including time series data smoothing, data quality control and real-time estimation of LAI. After the historical LAI time series is smoothed by a multi-step Savitzky-Golay filter to determine the upper LAI envelope, a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model is used to derive the LAI climatology. Based on the climatology from the SARIMA model to evolve LAI in time, a dynamic model is then constructed and used to provide the short-range forecast of LAI. Predictions from this model are used with Ensemble Kalman Filter (EnKF) techniques to recursively update biophysical variables as new observations arrive. The validation results produced using MODIS surface reflectance data and field-measured LAI data at eight BELMANIP sites show that the real-time inversion method is able to efficiently produce a relatively smooth LAI product. In addition, the accuracy is significantly improved over the MODIS LAI product.     

Applicability of leaf area index products for boreal regions of Sweden

Leaf area index (LAI) of boreal ecosystems was estimated with optical instruments at the Laxemar and the Forsmark investigation areas in Sweden. The aim was to study relationships between LAI and normalized difference vegetation index (NDVI), and to evaluate the applicability of the moderate resolution imaging spectroradiometer (MODIS) LAI product for small boreal regions. Relationships between optically-estimated LAI and NDVI were significant for different forest types in Laxemar and for Forsmark, effective LAI was correlated to the NDVI for all sites. NDVI-estimated LAI was used for evaluating accuracy of the MODIS LAI product and the comparison showed no correlation in Forsmark, whereas they were correlated in Laxemar. MODIS LAI was, on average, 2.28 higher than NDVI-based LAI, and it showed larger scatter. Scale issues were the main explanation for the high MODIS LAI, since heterogeneous landscapes with open areas were seen as forest in the large pixels of the MODIS LAI product.     

Comparisons of land cover and LAI estimates derived from ETM+ and MODIS for four sites in North America: a quality assessment of 2000/2001 provisional MODIS products

The MODIS land science team produces a number of standard products, including land cover and leaf area index (LAI). Critical to the success of MODIS and other sensor products is an independent evaluation of product quality. In that context, we describe a study using field data and Landsat ETM+ to map land cover and LAI at four 49-km² sites in North America containing agricultural cropland (AGRO), prairie grassland (KONZ), boreal needleleaf forest, and temperate mixed forest. The purpose was to: (1) develop accurate maps of land cover, based on the MODIS IGBP (International Geosphere-Biosphere Programme) land cover classification scheme; (2) derive continuous surfaces of LAI that capture the mean and variability of the LAI field measurements; and (3) conduct initial MODIS validation exercises to assess the quality of early (i.e., provisional) MODIS products. ETM+ land cover maps varied in overall accuracy from 81% to 95%. The boreal forest was the most spatially complex, had the greatest number of classes, and the lowest accuracy. The intensive agricultural cropland had the simplest spatial structure, the least number of classes, and the highest overall accuracy. At each site, mapped LAI patterns generally followed patterns of land cover across the site. Predicted versus observed LAI indicated a high degree of correspondence between field-based measures and ETM+ predictions of LAI. Direct comparisons of ETM+ land cover maps with Collection 3 MODIS cover maps revealed several important distinctions and similarities. One obvious difference was associated with image/map resolution. ETM+ captured much of the spatial complexity of land cover at the sites. In contrast, the relatively coarse resolution of MODIS did not allow for that level of spatial detail. Over the extent of all sites, the greatest difference was an overprediction by MODIS of evergreen needleleaf forest cover at the boreal forest site, which consisted largely of open shrubland, woody savanna, and savanna. At the agricultural, temperate mixed forest, and prairie grassland sites, ETM+ and MODIS cover estimates were similar. Collection 3 MODIS-based LAI estimates were considerably higher (up to 4 m² m⁻²) than those based on ETM+ LAI at each site. There are numerous probable reasons for this, the most important being the algorithms' sensitivity to MODIS reflectance calibration, its use of a prelaunch AVHRR-based land cover map, and its apparent reliance on mainly red and near-IR reflectance. Samples of Collection 4 LAI products were examined and found to consist of significantly improved LAI predictions for KONZ, and to some extent for AGRO, but not for the other two sites. In this study, we demonstrate that MODIS reflectance data are highly correlated with LAI across three study sites, with relationships increasing in strength from 500 to 1000 m spatial resolution, when shortwave-infrared bands are included.     

Quasi-plus sampling edge correction for spatial point patterns

A widely applicable edge correction method for estimating summary statistics of a spatial point pattern is proposed. We reconstruct point patterns in a larger region containing the sampling window by matching sampled and simulated kth nearest neighbour distance distributions of the given pattern and then apply plus sampling. Simulation studies show that this approach, called quasi-plus sampling, gives estimates with smaller root mean squared errors than estimates obtained by using other popular edge corrections. We apply the proposed approach to real data and yield an estimate of a summary statistic that is more plausible than that obtained by a popular edge correction.     

Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America

Leaf Area Index (LAI) is an important biophysical variable for characterizing the land surface vegetation. Global LAI product has been routinely produced from the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellite platforms. However, the MODIS standard LAI product is not continuous both spatially and temporally. To fill the gaps and improve the quality, we have developed a data filtering algorithm. This filter, called the temporal spatial filter (TSF), integrates both spatial and temporal characteristics for different plant functional types. The spatial gaps are first filled with the multi-year averages of the same day. If the values are missing over all years, the pixel is filled with a new estimate using the vegetation continuous field-ecosystem curve fitting method. The TSF integrates both the multi-seasonal average trend (background) and the seasonal observation. We implement this algorithm using the MODIS Collection 4 LAI product over North America. Comparison of the TSF results with the Savitzky-Golay filter indicates that the TSF performs much better in restoring the spatial and temporal distribution of seasonal LAI trends. The new LAI product has been validated by comparing with field measurements and the derived LAI maps from ETM+ data at a broadleaf forest site and an agricultural site. The validation results indicate that the new LAI product agrees better with both the field measurements and LAI values obtained from the ETM+ than does the MODIS LAI standard product, which usually shows higher LAI values.     

Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation,analysis and validation

The evaluation of a new global monthly leaf area index (LAI) data set for the period July 1981 to December 2006 derived from AVHRR Normalized Difference Vegetation Index (NDVI) data is described. The physically based algorithm is detailed in the first of the two part series. Here, the implementation, production and evaluation of the data set are described. The data set is evaluated both by direct comparisons to ground data and indirectly through inter-comparisons with similar data sets. This indirect validation showed satisfactory agreement with existing LAI products, importantly MODIS, at a range of spatial scales, and significant correlations with key climate variables in areas where temperature and precipitation limit plant growth. The data set successfully reproduced well-documented spatio-temporal trends and inter-annual variations in vegetation activity in the northern latitudes and semi-arid tropics. Comparison with plot scale field measurements over homogeneous vegetation patches indicated a 7% underestimation when all major vegetation types are taken into account. The error in mean values obtained from distributions of AVHRR LAI and high-resolution field LAI maps for different biomes is within 0.5 LAI for six out of the ten selected sites. These validation exercises though limited by the amount of field data, and thus less than comprehensive, indicated satisfactory agreement between the LAI product and field measurements. Overall, the inter-comparison with short-term LAI data sets, evaluation of long term trends with known variations in climate variables, and validation with field measurements together build confidence in the utility of this new 26 year LAI record for long term vegetation monitoring and modeling studies.     

Water content estimation in vegetation with MODIS reflectance data and model inversion methods

Statistical and radiative-transfer physically based studies have previously demonstrated the relationship between leaf water content and leaf-level reflectance in the near-infrared spectral region. The successful scaling up of such methods to the canopy level requires modeling the effect of canopy structure and viewing geometry on reflectance bands and optical indices used for estimation of water content, such as normalized difference water index (NDWI), simple ratio water index (SRWI) and plant water index (PWI). This study conducts a radiative transfer simulation, linking leaf and canopy models, to study the effects of leaf structure, dry matter content, leaf area index (LAI), and the viewing geometry, on the estimation of leaf equivalent water thickness from canopy-level reflectance. The applicability of radiative transfer model inversion methods to MODIS is studied, investigating its spectral capability for water content estimation. A modeling study is conducted, simulating leaf and canopy MODIS-equivalent synthetic spectra with random input variables to test different inversion assumptions. A field sampling campaign to assess the investigated simulation methods was undertaken for analysis of leaf water content from leaf samples in 10 study sites of chaparral vegetation in California, USA, between March and September 2000. MODIS reflectance data were processed from the same period for equivalent water thickness estimation by model inversion linking the PROSPECT leaf model and SAILH canopy reflectance model. MODIS reflectance data, viewing geometry values, and LAI were used as inputs in the model inversion for estimation of leaf equivalent water thickness, dry matter, and leaf structure. Results showed good correlation between the time series of MODIS-estimated equivalent water thickness and ground measured leaf fuel moisture (LFM) content (r²=0.7), demonstrating that these inversion methods could potentially be used for global monitoring of leaf water content in vegetation.     

Estimation of absorbed PAR across Scandinavia from satellite measurements. Part II: Modeling and evaluating the fractional absorption

The aim of this study is to generate a quality-controlled sub-kilometer dataset of the fraction of absorbed photosynthetically active radiation (FAPAR) across Scandinavia from satellite. FAPAR is required for estimating the amount of PAR absorbed (APAR) by vegetation, which in turn allows for estimation of carbon uptake. In this study, FAPAR was modeled from normalized difference vegetation index (NDVI) which was obtained from the MODIS VI product (MOD13Q1) at 250 m spatial resolution. Modeled FAPAR was evaluated against in-situ measurements of fractional interception of PAR (FIPAR) and FAPAR at nine plots in six forested sites across Sweden and Denmark from 2001 to 2005. High resolution remote sensing data were used to investigate the representativeness of the measurement areas. Furthermore, FAPAR from the MODIS LAI/FPAR product at 1 km spatial resolution (MOD15A2) was investigated and compared the measured and modeled FAPAR. There was good agreement between modeled and measured FAPAR (6.9% average RMSE of the means). A linear relationship between daily values of NDVI and FAPAR was found (R² = 0.82), and it is concluded that seasonally adjusted NDVI can be used for accurate FAPAR estimations over forested areas in Scandinavia. However, it was found that the error was correlated with average FAPAR and that it is important to take the understory vegetation into account when measuring FAPAR in open canopies. The observed difference between FIPAR and FAPAR was 2.3 and 1.4 percentage units for coniferous and deciduous stands, respectively. MODIS FAPAR performed well although a few unrealistic values were present, highlighting the necessity to filter out low quality values using the quality-control datasets.     

Evaluation of the MODIS Albedo product over a heterogeneous agricultural area

In this article, the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF)/Albedo product (MCD43) is evaluated over a heterogeneous agricultural area in the framework of the Earth Observation: Optical Data Calibration and Information Extraction (EODIX) project campaign, which was developed in Barrax (Spain) in June 2011. In this method, two models, the RossThick-LiSparse-Reciprocal (RTLSR) (which corresponds to the MODIS BRDF algorithm) and the RossThick-Maignan-LiSparse-Reciprocal (RTLSR-HS), were tested over airborne data by processing high-resolution images acquired with the Airborne Hyperspectral Scanner (AHS) sensor. During the campaign, airborne images were retrieved with different view zenith angles along the principal and orthogonal planes. Comparing the results of applying the models to the airborne data with ground measurements, we obtained a root mean square error (RMSE) of 0.018 with both RTLSR and RTLSR-HS models. The evaluation of the MODIS BRDF/Albedo product (MCD43) was performed by comparing satellite images with AHS estimations. The results reported an RMSE of 0.04 with both models. Additionally, taking advantage of a homogeneous barley pixel, we compared in situ albedo data to satellite albedo data. In this case, the MODIS albedo estimation was (0.210 ± 0.003), while the in situ measurement was (0.204 ± 0.003). This result shows good agreement in regard to a homogeneous pixel.     

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 km², while the optimal one is 10 km × 10 km²; 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.     

A multisensor fusion approach to improve LAI time series

High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. It is applicable to any optical sensor and satellite product. In this study, the potential of this technique was demonstrated for leaf area index (LAI) product based on MODIS and VEGETATION reflectance data. The FUSION product showed an overall good agreement with the original MODIS LAI product but exhibited a reduction of 90% of the missing LAI values with an improved monitoring of vegetation dynamics, temporal smoothness, and better agreement with ground measurements.     

Estimation of atmospheric particulate matter based on MODIS haze optimized transformation

This research is an attempt to simulate the relationship between haze optimized transformation (HOT) and aerosol optical thickness (AOT), and explore the influence of typical ground covers on this relationship using the 6S atmospheric radiative transfer model for the Chinese city of Nanjing. The HOT data were derived from moderate resolution imaging spectroradiometer (MODIS) satellite images recorded in the winter and spring seasons of December 2007–May 2009. They were analysed in conjunction with ground observed atmospheric particulate matter (PM) data so as to establish their quantitative relationship. Such a relationship may open a new avenue for remotely estimating atmospheric PM based on HOT. The results obtained indicate that HOT is related positively to AOT. This relationship is most accurately depicted by a second-order polynomial equation. Although built-up areas, waterbodies, and vegetation have differing HOT values, all of them bear a close and consistent correlation with AOT. HOT of built-up areas, waterbodies, and vegetative surfaces derived from MODIS images is also positively correlated with PM₁₀ (PM with diameter <10 μm), which was measured near the surface. The second-order polynomial equation has a coefficient of determination (R²) value of 0.375 (built-up), 0.344 (water), and 0.362 (vegetation) and a root mean squared error (RMSE) of 0.0258, 0.0264, and 0.0261, respectively. The closeness in R² value and RMSE for different ground covers suggests that correlation is marginally affected by the ground cover. It is thus concluded that HOT can be used as a reliable alternative for estimating PM₁₀ from MODIS data.     

Prototyping of Land-SAF leaf area index algorithm with VEGETATION and MODIS data over Europe

The Satellite Application Facility on Land Surface Analysis (Land-SAF) aims to provide land surface variables for the meteorological and environmental science communities from EUMETSAT satellites. This study assesses the performance of a simplified (i.e. random distribution of vegetation is assumed) version of the Land-SAF algorithm for the estimation of Leaf Area Index (LAI) when prototyped with VEGETATION (processed in CYCLOPES program) and MODIS reflectances. The prototype estimates of LAI are evaluated both by comparison with validated CYCLOPES and MODIS LAI products derived from the same sensors and directly through comparison with ground-based estimates. Emphasis is given on evaluating the impact of the algorithm and input data on LAI retrieval discrepancies. Analysis is achieved over Europe for the 2000-2003 period. The results demonstrate the capacity of the Land-SAF algorithm to retrieve consistent LAI estimates from multiple optical sensors even when their reflectances present systematic differences. High spatial and temporal consistencies between Land-SAF prototype estimates and existing LAI products are found. The differences between Land-SAF and CYCLOPES LAI are lower than their uncertainties (RMSE (relative RMSE) within 0.4 (30%)). Land-SAF prototype estimates and MODIS LAI show larger discrepancies mainly due to differences in the vegetation structure representation and algorithm assumptions (RMSE ranging from 0.2 (30%) up to 0.8 (40%)). Land-SAF prototype provides higher LAI values than MODIS for herbaceous canopies (i.e. shrubs, grasses and crops) and lower values for woody biomes (i.e. savannas and forests). Direct validation indicates that LAI estimates from prototyping of the Land-SAF algorithm with CYCLOPES and MODIS reflectances achieve similar performances (differences with ground measurements are lower than 0.5 LAI units in 60% and 50% of the cases, respectively) as CYCLOPES and MODIS LAI products. Results from this prototyping exercise appear useful for improved retrieval of LAI and constitute a step forward for refinement, validation and consolidation of the Land-SAF algorithm.     

黑河及汉江流域MODIS叶面积指数产品质量评价

叶面积指数（LAI）是MODIS地面队伍生产的一系列标准产品之一，对其进行独立的质量评价有助于用户了解数据的适用性。本文用近同时相的Landsat影像及野外实测LAI数据获得了黑河及汉江两个研究区高分辨率的Landsat LAl分布图。基于此，对MODIS LAI数据进行了质量评价，评价指标包括统计特征和空间特征。分析结果表明，就统计特征而言，MODIS LAI数据值一般低于Landsat的LAI值，在植被覆盖较好的汉江区低估约10％，在植被覆盖稀疏的黑河区，LAI值低估达58％；就空间特征而言，两个研究区的结果都表明MODIS LAI数据无法很好地体现植被空间分布信息，在黑河区存在大量低槽被覆盖像元被归类为非植被覆盖区的情况。     

Bayesian maximum entropy data fusion of field-observed leaf area index (LAI) and Landsat Enhanced Thematic Mapper Plus-derived LAI

Accurate high-resolution leaf area index (LAI) reference maps are necessary for the validation of coarser-resolution satellite-derived LAI products. In this article, we propose an efficient method based on the Bayesian Maximum Entropy (BME) paradigm to combine field observations and Landsat Enhanced Thematic Mapper Plus (ETM+)-derived LAI surfaces in order to produce more accurate LAI reference maps. This method takes into account the uncertainties associated with field observations and with the regression relationship between ETM+-derived LAI and field measurements to perform a non-linear prediction of LAI, the variable of interest. In order to demonstrate the difference by soft data and hard data, we estimate the LAI reference maps by three BME interpolation methods, BME1, BME2, and BME3. BME1 and BME2 perform maximum estimation and mean estimation, respectively, by taking the ETM+-derived LAI as interval soft data and the field LAI measurements as hard data. BME3 is utilized when ETM+-derived LAI surfaces are processed as uniform probability soft data and field measurements are processed as Gaussian probability soft data. Three study sites are selected from the BigFoot project (NASA's Earth Observing System validation programme) (http://www.fsl.orst.edu/larse/bigfoot/index.html). In regard to the mean and standard deviation of LAI surfaces, standard deviation predicted by BME methods has lower values than that derived by ETM+. The mean value of the BME-predicted LAI, which takes into account the uncertainties of field measurements, is lower than that of ETM+-derived LAI at each study site. A comparison with field measurements shows that BME1, BME2, and BME3 have root mean square errors (RMSE) of 0.455, 0.485, and 0.517 and average biases of??0.017,??0.010, and??0.304, respectively. The RMSEs and biases of the predicted LAI surfaces are less when compared to the ETM+-derived LAI, which has the average RMSE and bias of 0.642 and??0.080. When the field measurements are processed as soft data, the predicted LAI by BME3 has more bias than those of the predictions by BME1 and BME2, but has less RMSE than that of the ETM+-derived LAI by 0.125. In summary, BME is capable of incorporating the spatial autocorrelation and the uncertainties in the field LAI measurements into the LAI surface estimation to produce a more accurate LAI surface with less RMSE in validation. The maximum estimation has relatively better accuracy than the mean estimation. The results indicate that the BME is a promising method for fusing point-scale and area-scale data.