A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km² study area in Arctic Alaska. The study area is centered at 69 °N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10°, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.     

Effect of remote sensing spatial resolution on interpreting tower-based flux observations

Validation comparisons between satellite-based surface energy balance models and tower-based flux measurements over heterogeneous landscapes can be strongly influenced by the spatial resolution of the remote sensing inputs. In this paper, a two-source energy balance model developed to use thermal and visible /near-infrared remotely sensed data is applied to Landsat imagery collected during the 2004 Soil Moisture Experiment (SMEX04) conducted in southern Arizona. Using a two dimensional flux-footprint algorithm, modeled surface fluxes are compared to tower measurements at three locations in the SMEX04 study area: two upland sites, and one riparian site. The effect of pixel resolution on evaluating the performance of the land surface model and interpreting spatial variations of land surface fluxes over these heterogeneous areas is evaluated. Three Landsat scenes were examined, one representing the dry season and the other two representing the relatively wet monsoon season. The model was run at three resolution scales: namely the Landsat visible/near-infrared band resolution (30 m), the Landsat 5 thermal band resolution (120 m), and 960 m, which is nominally the MODIS thermal resolution at near-nadir. Comparisons between modeled and measured fluxes at the three tower sites showed good agreement at the 30 m and 120 m resolutions — pixel scales at which the source area influencing the tower measurement (∼ 100 m) is reasonably resolved. At 960 m, the agreement is relatively poor, especially for the latent heat flux, due to sub-pixel heterogeneity in land surface conditions at scales exceeding the tower footprint. Therefore in this particular landscape, thermal data at 1-km resolution are not useful in assessing the intrinsic accuracy of the land-surface model in comparison with tower fluxes. Furthermore, important spatial patterns in the landscape are lost at this resolution. Currently, there are no definite plans supporting high resolution thermal data with regular global coverage below ∼ 700 m after Landsat 5 and ASTER fail. This will be a serious problem for the application and validation of thermal-based land-surface models over heterogeneous landscapes.     

Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model

Landsat imagery with a 30 m spatial resolution is well suited for characterizing landscape-level forest structure and dynamics. While Landsat images have advantageous spatial and spectral characteristics for describing vegetation properties, the Landsat sensor's revisit rate, or the temporal resolution of the data, is 16 days. When considering that cloud cover may impact any given acquisition, this lengthy revisit rate often results in a dearth of imagery for a desired time interval (e.g., month, growing season, or year) especially for areas at higher latitudes with shorter growing seasons. In contrast, MODIS (MODerate-resolution Imaging Spectroradiometer) has a high temporal resolution, covering the Earth up to multiple times per day, and depending on the spectral characteristics of interest, MODIS data have spatial resolutions of 250 m, 500 m, and 1000 m. By combining Landsat and MODIS data, we are able to capitalize on the spatial detail of Landsat and the temporal regularity of MODIS acquisitions. In this research, we apply and demonstrate a data fusion approach (Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM) at a mainly coniferous study area in central British Columbia, Canada. Reflectance data for selected MODIS channels, all of which were resampled to 500 m, and Landsat (at 30 m) were combined to produce 18 synthetic Landsat images encompassing the 2001 growing season (May to October). We compared, on a channel-by-channel basis, the surface reflectance values (stratified by broad land cover types) of four real Landsat images with the corresponding closest date of synthetic Landsat imagery, and found no significant difference between real (observed) and synthetic (predicted) reflectance values (mean difference in reflectance: mixed forest x? = 0.086, σ = 0.088, broadleaf x? = 0.019, σ = 0.079, coniferous x? = 0.039, σ = 0.093). Similarly, a pixel based analysis shows that predicted and observed reflectance values for the four Landsat dates were closely related (mean r² = 0.76 for the NIR band; r² = 0.54 for the red band; p < 0.01). Investigating the trend in NDVI values in synthetic Landsat values over a growing season revealed that phenological patterns were well captured; however, when seasonal differences lead to a change in land cover (i.e., disturbance, snow cover), the algorithm used to generate the synthetic Landsat images was, as expected, less effective at predicting reflectance.     

Evaluation of LST downscaling algorithms on seasonal thermal data in humid subtropical regions of India

Thermal image downscaling algorithms use a unique relationship between land surface temperature (LST) and vegetation indices (e.g. normalized difference vegetation index (NDVI)). The LST–NDVI correlation and regression parameters vary in different seasons depending on land-use practices. Such relationships are dynamic in humid subtropical regions due to inter-seasonal changes in biophysical parameters. The present study evaluates three downscaling algorithms, namely disaggregation of radiometric surface temperature (DisTrad), sharpening thermal imagery (TsHARP), and local model using seasonal (25 February 2010, 14 April 2010, and 26 October 2011) thermal images. The aggregated Landsat LST of 960 m resolution is downscaled to 480, 360, 240, and 120 m using DisTrad, TsHARP, and the local model and validated with aggregated Landsat LSTs of a similar resolution. The results illustrate that the seasonal variability of the LST–NDVI relationship affects the accuracy of the downscaling model. For example, the accuracy of all algorithms is higher for the growing seasons (February and October) unlike the harvesting season (April). The root mean square error of the downscaled LST increases from 480 to 120 m spatial resolution in all seasons. The models are least suitable in water body and dry-river bed sand areas. However, the downscaling accuracy is higher for NDVI > 0.3. The present study is useful to understand the applicability of the downscaling models in seasonally varied landscapes and different NDVI ranges.     

Disaggregation of LST over India: comparative analysis of different vegetation indices

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.     

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

The temporal frequency of the thermal data provided by current spaceborne high-resolution imagery systems is inadequate for agricultural applications. As an alternative to the lack of high-resolution observations, kilometric thermal data can be disaggregated using a green (photosynthetically active) vegetation index e.g. NDVI (Normalized Difference Vegetation Index) collected at high resolution. Nevertheless, this approach is only valid in the conditions where vegetation temperature is approximately uniform. To extend the validity domain of the classical approach, a new methodology is developed by representing the temperature difference between photosynthetically and non-photosynthetically active vegetation. In practice, both photosynthetically and non-photosynthetically active vegetation fractions are derived from a time series of Formosat-2 shortwave data, and then included in the disaggregation procedure. The approach is tested over a 16 km by 10 km irrigated cropping area in Mexico during a whole agricultural season. Kilometric MODIS (MODerate resolution Imaging Spectroradiometer) surface temperature is disaggregated at 100 m resolution, and disaggregated temperature is subsequently compared against concurrent ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data. Statistical results indicate that the new methodology is more robust than the classical one, and is always more accurate when fractional non-photosynthetically active vegetation cover is larger than 0.10. The mean correlation coefficient and slope between disaggregated and ASTER temperature is increased from 0.75 to 0.81 and from 0.60 to 0.77, respectively. The approach is also tested using the MODIS data re-sampled at 2 km resolution. Aggregation reduces errors in MODIS data and consequently increases the disaggregation accuracy.     

A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.     

Effects of remote sensing pixel resolution on modeled energy flux variability of croplands in Iowa

With increased availability of satellite data products used in mapping surface energy balance and evapotranspiration (ET), routine ET monitoring at large scales is becoming more feasible. Daily satellite coverage is available, but an essential model input, surface temperature, is at 1 km or greater pixel resolution. At such coarse spatial resolutions, the capability to monitor the impact of land cover change and disturbances on ET or to evaluate ET from different crop covers is severely hampered. The effect of sensor resolution on model output for an agricultural region in central Iowa is examined using Landsat data collected during the Soil Moisture Atmosphere Coupling Experiment (SMACEX). This study was conducted in concert with the Soil Moisture Experiment 2002 (SMEX02). Two images collected during a rapid growth period in soybean and corn crops are used with a two-source (soil+vegetation) energy balance model, which explicitly evaluates soil and vegetation contributions to the radiative temperature and to the net turbulent exchange/surface energy balance. The pixel resolution of the remote sensing inputs are varied from 60 m to 120, 240, and 960 m. Model output at high resolution are first validated with tower and aircraft-based flux measurements to assure reliability of model computations. Histograms of the flux distributions and resulting statistics at the different pixel resolutions are compared and contrasted. Results indicate that when the input resolution is on the order of 1000 m, variation in fluxes, particularly ET, between corn and soybean fields is not feasible. However, results also suggest that thermal sharpening techniques for estimating surface temperature at higher resolutions (∼250 m) using the visible/near infrared waveband resolutions could provide enough spatial detail for discriminating ET from individual corn and soybean fields. Additional support for this nominal resolution requirement is deduced from a geostatistical analysis of the vegetation index and surface temperature images.     

An active-fire based burned area mapping algorithm for the MODIS sensor

We present an automated method for mapping burned areas using 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) imagery coupled with 1-km MODIS active fire observations. The algorithm applies dynamic thresholds to composite imagery generated from a burn-sensitive vegetation index and a measure of temporal texture. Cumulative active fire maps are used to guide the selection of burned and unburned training samples. An accuracy assessment for three geographically diverse regions (central Siberia, the western United States, and southern Africa) was performed using high resolution burned area maps derived from Landsat imagery. Mapped burned areas were accurate to within approximately 10% in all regions except the high-tree-cover sub-region of southern Africa, where the MODIS burn maps underestimated the area burned by 41%. We estimate the minimum detectable burn size for reliable detection by our algorithm to be on the order of 120 ha.     

Estimating sub-pixel temperatures using the triangle algorithm

In water-deficient areas, water resource management requires evapotranspiration at high spatiotemporal resolution – an impossible situation given the trade-off between spatial and temporal resolutions in space-borne systems. Some researchers have suggested sharpening the Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature product with a resolution from 1 km to 250 m and a functional relationship between surface temperature (T _r) and normalized difference vegetation index (NDVI). Evapotranspiration at 250 m resolution can be obtained once every few days using this technique. Based on the interpretation of the triangular T _r–NDVI space and assuming uniform soil moisture conditions in a coarse pixel, this paper suggests an alternative algorithm – the triangle algorithm – for sharpening. The triangle algorithm was tested using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data from an arid zone. Sharpened surface temperatures and reference temperatures were compared at 60 m and 240 m resolutions. Root mean square errors with the triangle algorithm are smaller than those with a functional relationship between T _r and NDVI. This paper will also discuss the impact of soil moisture variations in the coarse pixel on the triangle algorithm. Finally we should mention that the triangle algorithm only applies to regions with non-stressed vegetation canopies.     

Sub-pixel water temperature estimation from thermal-infrared imagery using vectorized lake features

Water skin temperature derived from thermal infrared satellite data are used in a wide variety of studies. Many of these studies would benefit from frequent, high spatial resolution (100 m pixels) thermal imagery but currently, at any given location, such data are only available every few weeks from spaceborne sensors such as ASTER. Lower spatial resolution (1 km pixels) thermal imagery is available multiple times per day at any given location, from several sensors such as MODIS on board both the AQUA and TERRA satellite platforms. In order to fully exploit lower spatial resolution imagery, a sub-pixel unmixing technique has been developed and tested at Quesnel Lake, British Columbia, Canada. This approach produces accurate, frequent high spatial resolution water skin temperature maps by exploiting a priori knowledge of water boundaries derived from vectorized water features. The pixel water-fraction maps are then input to a gradient descent algorithm to solve the mixed pixel ground leaving radiance equation for sub-pixel water temperature. Ground-leaving radiance is estimated from standard temperature and emissivity data products for pure pixels and a simple regression technique to estimate atmospheric effects. In this test case, MODIS 1 km thermal imagery was used along with 1:50,000 water features to create a high-resolution (100 m) water skin temperature map. This map is compared to a concurrent ASTER temperature image and found to be within 1 °C of the ASTER skin temperature 99% of the time. This is a considerable improvement over the 2.55 °C difference between the original MODIS product and ASTER image due to land temperature contamination. The algorithm is simple, effective, and unlocks a largely untapped resource for limnological and hydrological studies.     

High-resolution urban thermal sharpener (HUTS)

A high resolution urban thermal sharpener (HUTS) was developed that increases the resolution of thermal infrared (TIR) data to that of visible and near infrared (VNIR) data by fitting the relationship between radiometric surface temperature, normalized difference vegetation index (NDVI) and surface albedo (α). HUTS was applied to TIR data aggregated to 90 m to represent a satellite acquired dataset and validated against the measured 10 m data from an aircraft over San Juan, Puerto Rico. HUTS sharpening reduced the root mean square error of surface temperature at the high resolution by 17% compared to no sharpening and outperformed other sharpening methods. HUTS is proposed as a useful tool to study urban meteorology and climatology at the microscale using ASTER satellite data.     

Application of MODIS derived parameters for regional crop yield assessment

NOAA AVHRR has been used extensively for monitoring vegetation condition and changes across the United States. Integration of crop growth models with MODIS imagery at 250 m resolution from the Terra Satellite potentially offers an opportunity for operational assessment of the crop condition and yield at both field and regional scales. The primary objective of this research was to evaluate the quality of the MODIS 250 m resolution data for retrieval of crop biophysical parameters that could be integrated in crop yield simulation models. A secondary objective was evaluating the potential use of MODIS 250 m resolution data for crop classification. A field study (24 fields) was conducted during the 2000 crop season in McLean County, Illinois, in the U.S. Midwest to evaluate the applicability of the MODIS 8-day, 250 m resolution composite imagery (version 4) for operational assessment of crop condition and yields. Ground-based canopy and leaf reflectance and leaf area index (LAI) measurements were used to calibrate a radiative transfer model to create a look up table (LUT) that was used to simulate LAI. The seasonal trend of MODIS derived LAI was used to find crop model parameters by adjusting the LAI simulated from the climate-based crop yield model. Other intermediate products such as crop phenological events were adjusted from the LAI seasonal profile. Corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) yield simulations were conducted on a 1.6 × 1.6 km² spatial resolution grid and the results integrated to the county level. The results were within 10% of county yields reported by the USDA National Agricultural Statistics Service (NASS).     

Mapping regional land cover with MODIS data for biological conservation: Examples from the Greater Yellowstone Ecosystem, USA and Pará State, Brazil

The paper investigated the application of MODIS data for mapping regional land cover at moderate resolutions (250 and 500 m), for regional conservation purposes. Land cover maps were generated for two major conservation areas (Greater Yellowstone Ecosystem—GYE, USA and the Pará State, Brazil) using MODIS data and decision tree classifications. The MODIS land cover products were evaluated using existing Landsat TM land cover maps as reference data. The Landsat TM land cover maps were processed to their fractional composition at the MODIS resolution (250 and 500 m). In GYE, the MODIS land cover was very successful at mapping extensive cover types (e.g. coniferous forest and grasslands) and far less successful at mapping smaller habitats (e.g. wetlands, deciduous tree cover) that typically occur in patches that are smaller than the MODIS pixels, but are reported to be very important to biodiversity conservation. The MODIS classification for Pará State was successful at producing a regional forest/non-forest product which is useful for monitoring the extreme human impacts such as deforestation. The ability of MODIS data to map secondary forest remains to be tested, since regrowth typically harbors reduced levels of biodiversity. The two case studies showed the value of using multi-date 250 m data with only two spectral bands, as well as single day 500 m data with seven spectral bands, thus illustrating the versatile use of MODIS data in two contrasting environments. MODIS data provide new options for regional land cover mapping that are less labor-intensive than Landsat and have higher resolution than previous 1 km AVHRR or the current 1 km global land cover product. The usefulness of the MODIS data in addressing biodiversity conservation questions will ultimately depend upon the patch sizes of important habitats and the land cover transformations that threaten them.     

Forest structure and aboveground biomass in the southwestern United States from MODIS and MISR

Red band bidirectional reflectance factor data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) acquired over the southwestern United States were interpreted through a simple geometric-optical (GO) canopy reflectance model to provide maps of fractional crown cover (dimensionless), mean canopy height (m), and aboveground woody biomass (Mg ha^− 1) on a 250 m grid. Model adjustment was performed after dynamic injection of a background contribution predicted via the kernel weights of a bidirectional reflectance distribution function (BRDF) model. Accuracy was assessed with respect to similar maps obtained with data from the NASA Multiangle Imaging Spectroradiometer (MISR) and to contemporaneous US Forest Service (USFS) maps based partly on Forest Inventory and Analysis (FIA) data. MODIS and MISR retrievals of forest fractional cover and mean height both showed compatibility with the USFS maps, with MODIS mean absolute errors (MAE) of 0.09 and 8.4 m respectively, compared with MISR MAE of 0.10 and 2.2 m, respectively. The respective MAE for aboveground woody biomass was ~ 10 Mg ha^− 1, the same as that from MISR, although the MODIS retrievals showed a much weaker correlation, noting that these statistics do not represent evaluation with respect to ground survey data. Good height retrieval accuracies with respect to averages from high resolution discrete return lidar data and matches between mean crown aspect ratio and mean crown radius maps and known vegetation type distributions both support the contention that the GO model results are not spurious when adjusted against MISR bidirectional reflectance factor data. These results highlight an alternative to empirical methods for the exploitation of moderate resolution remote sensing data in the mapping of woody plant canopies and assessment of woody biomass loss and recovery from disturbance in the southwestern United States and in parts of the world where similar environmental conditions prevail.     

Land Surface Temperature Data Fusion of Landsat ETM and MODISby Combining TsHARP and STITFM Model

Land Surface Temperature (LST) is an important parameter that describes energy balance of substance and energy exchange between the surface and the atmosphere,and LST has widely used in the fields of urban heat island effect,soil moisture and surface radiative flux.Currently,no satellite sensor can deliver thermal infrared data at both high temporal resolution and spatial resolution,which strongly limits the wide application of thermal infrared data.Based on the MODIS land surface temperature product and Landsat ETM+image,a temporal and spatial fusion method is proposed by combining the TsHARP (Thermal sHARPening) model with the STITFM (Spatio\|Temporal Integrated Temperature Fusion Model) algorithm,defined as CTsSTITFM model in this study.The TsHARP method is used to downscale the 1 km MODIS land surface temperature image to LST data at spatial resolution of 250 m.Then the accuracy is verified by the retrieval LST from Landsat ETM+ image at the same time.Land surface temperature image at 30 m spatial scale is predicted by fusing Landsat ETM+ and downscaling MODIS data using STITFM model.The fusion LST image is validated by the estimated LST from Landsat ETM+ data for the same predicted.The results show that the proposed method has a better precision comparing to the STITFM algorithm.Under the default parameter setting,the predicted LST values using CTsSTITFM fusion method have a root mean square error (RMSE) less than 1.33 K.By adjusting the window size of CTsSTITFM fusion method,the fusion results in the selected areas show some regularity with the increasing of the window.In general,a reasonable window size set may slightly improve the effects of LST fusion.The CTsSTITFM fusion method can solve the problem of mixed pixels caused by coarse\|scale MODIS surface temperature images to some degree.     

Subpixel monitoring of the seasonal snow cover with MODIS at 250 m spatial resolution in the Southern Alps of New Zealand: Methodology and accuracy assessment

This study describes a comprehensive method to produce routinely regional maps of seasonal snow cover in the Southern Alps of New Zealand (upper Waitaki basin) on a subpixel basis, and with the MODerate Resolution Imaging Spectroradiometer (MODIS). The method uses an image fusion algorithm to produce snow maps at an improved 250 m spatial resolution in addition to the 500 m resolution snow maps. An iterative approach is used to correct imagery for both atmospheric and topographic effects using daily observations of atmospheric parameters. The computation of ground spectral reflectance enabled the use of image-independent end-members in a constrained linear unmixing technique to achieve a robust estimation of subpixel snow fractions. The accuracy of the snow maps and performance of the algorithm were assessed carefully using eight pairs of synchronic MODIS/ASTER images. ‘Pixel-based’ metrics showed that subpixel snow fractions were retrieved with a Mean Absolute Error of 6.8% at 250 m spatial resolution and 5.1% after aggregation at 500 m spatial resolution. In addition, a ‘feature-based’ metric showed that 90% of the snowlines were depicted generally within 300 m and 200 m of their correct position for the 500-m and 250-m spatial resolution snow maps, respectively. A dataset of 679 maps of subpixel snow fraction was produced for the period from February 2000 to May 2007. These repeated observations of the seasonal snow cover will benefit the ongoing effort to model snowmelt runoff in the region and to improve the estimation and management of water resources.     

Generation of high-resolution East Antarctic landfast sea-ice maps from cloud-free MODIS satellite composite imagery

A method to generate high spatio-temporal resolution maps of landfast sea ice from cloud-free MODIS composite imagery is presented. Visible (summertime) and thermal infrared (wintertime) cloud-free 20-day MODIS composite images are used as the basis for these maps, augmented by AMSR-E ASI sea-ice concentration composite images (when MODIS composite image quality is insufficient). The success of this technique is dependent upon efficient cloud removal during the compositing process. Example wintertime maximum (~ 374,000 km²) and summertime minimum (~ 112,000 km²) fast-ice maps for the entire East Antarctic coast are presented. The summertime minimum map provides the first high-resolution indication of multi-year fast-ice extent, which may be used to help assess changes in Antarctic sea-ice volume. The 2σ errors in fast-ice extent are estimated to be ± 2.98% when ≥ 90% of the fast-ice pixels in a 20-day period are classified using the MODIS composite, or ± 8.76 otherwise (when augmenting AMSR-E or the previous/next MODIS composite image is used to classify > 10% of the fast ice). Imperfect composite image quality, caused by persistent cloud, inaccurate cloud masking or a highly dynamic fast-ice edge, was the biggest impediment to automating the fast-ice detection procedure.     

A sequential model for disaggregating near-surface soil moisture observations using multi-resolution thermal sensors

A sequential model is developed to disaggregate microwave-derived soil moisture from 40 km to 4 km resolution using MODIS (Moderate Imaging Spectroradiometer) data and subsequently from 4 km to 500 m resolution using ASTER (Advanced Scanning Thermal Emission and Reflection Radiometer) data. The 1 km resolution airborne data collected during the three-week National Airborne Field Experiment 2006 (NAFE'06) are used to simulate the 40 km pixels, and a thermal-based disaggregation algorithm is applied using 1 km resolution MODIS and 100 m resolution ASTER data. The downscaled soil moisture data are subsequently evaluated using a combination of airborne and in situ soil moisture measurements. A key step in the procedure is to identify an optimal downscaling resolution in terms of disaggregation accuracy and sub-pixel soil moisture variability. Very consistent optimal downscaling resolutions are obtained for MODIS aboard Terra, MODIS aboard Aqua and ASTER, which are 4 to 5 times the thermal sensor resolution. The root mean square error between the 500 m resolution sequentially disaggregated and ground-measured soil moisture is 0.062 vol./vol. with a bias of − 0.045 vol./vol. and values ranging from 0.08 to 0.40 vol./vol.