Land-surface temperature dynamics in the Upper Mekong Basin derived from MODIS time series

Land surface temperature (LST) is an important indicator for climate variability and can be sensed remotely by satellites with a high temporal resolution on a broad spatial scale. In this research, Moderate Resolution Imaging Spectroradiometer (MODIS) LST is used to derive a 13 year time series on the Upper Mekong Basin (UMB), belonging to the People’s Republic of China and the Republic of the Union of Myanmar, to analyse the spatial pattern and temporal development of LST. The data set shows the regular annual curve of surface temperatures with maximum values in summer and minimum values in winter. Average temperatures in the southern parts of the basin are higher than in the northern part. Spatial gradients between maximum and minimum LST as well as gradients between daytime and night-time LST are much lower in the southern parts than in the northern parts, which are characterized by a strong topography. The pixel-wise variability of monthly means was found to be in the range of ±4°C for most pixels in the daytime scenes, whereas the night-time scenes show a lower variability with most pixels in the range of ±1°C. The variability of LST in the northern areas clearly exceeds that in the southern areas. Some inter-annual variations occur, mainly during summer: in some years a two-peak distribution is found, which is explained by the generally low number of observations in the respective months. A primary challenge of optical satellite data in the UMB is cloud contamination in the summer months, where peak rainfall occurs. In the Mekong Highlands for instance, the average number of available daytime observations of MODIS LST in July is one observation per month only. It can be assumed that climate statistics calculated from such data is biased. In this context, two gap-filling algorithms were applied to two test areas for the year 2002 and results are discussed in the article. Another issue with MODIS LST data are day-to-day differences in the acquisition time. A temporal homogenization was applied to selected LST data, converting them to one fixed acquisition time. The converted data were compared to the original data set. No significant influence could be found.     

Suitability of SAR imagery for automatic flood mapping in the Lower Mekong Basin

Flood detection and inundation mapping are amongst the most important applications for remote-sensing data. Space-borne radar systems, synthetic aperture radar (SAR) in particular, and its application for waterbody mapping have recently been subject to research in many publications. Although very good results have been achieved with such data, in some cases automatic waterbody classification based on SAR data is not feasible. Factors influencing the applicability are, e.g., local environmental conditions, roughening of water surfaces due to wind, or the satellite observation geometry. In this study, a measure for the usability of SAR imagery for flood mapping was investigated. Additionally, a method for permanent waterbody mapping was introduced. The study is based on Envisat ASAR wide swath mode (150 m spatial resolution) data of the Mekong River Basin. For the usability measure, the concept of ‘high-contrast tiles’ was established, which allows an a priori estimation of the expected accuracy of a waterbody classifier. The SAR-based permanent waterbody map was used for the validation of the approach. It was found that, for the test site, the new SAR usability measure allows the identification of unsuitable scenes with a certainty of more than 90%. The method is expected to be very useful for near-real-time flood mapping applications where human interaction is neither desired nor feasible when large regions and large data volumes are considered.     

Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000-2001 snow year

Snow cover represents an important water resource for the Upper Rio Grande River Basin of Colorado and New Mexico. Accuracy assessment of MODIS snow products was accomplished using Geographic Information System (GIS) techniques. Daily snow cover maps produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data were compared with operational snow cover maps produced by the National Operational Hydrologic Remote Sensing Center (NOHRSC) and against in situ Snowpack Telemetry (SNOTEL) measurements for the 2000-2001 snow season. Over the snow season, agreement between the MODIS and NOHRSC snow maps was high with an overall agreement of 86%. However, MODIS snow maps typically indicate a higher proportion of the basin as being snow-covered than do the NOHRSC snow maps. In particular, large tracts of evergreen forest on the western slopes of the San de Cristo Range, which comprise a large portion of the eastern margin of the basin, are more consistently mapped as snow-covered in the MODIS snow products than in the NOHRSC snow products. NOHRSC snow maps, however, typically indicate a greater proportion of the central portion of the basin, predominately in cultivated areas, as snow. Comparisons of both snow maps with in situ SNOTEL measurements over the snow season show good overall agreement with overall accuracies of 94% and 76% for MODIS and NOHRSC, respectively. A lengthened comparison of MODIS against SNOTEL sites, which increases the number of comparisons of snow-free conditions, indicates a slightly lower overall classification accuracy of 88%. Errors in mapping extra snow and missing snow by MODIS are comparable, with MODIS missing snow in approximately 12% of the cases and mapping too much snow in 15% of the cases. The majority of the days when MODIS fails to map snow occurs at snow depths of less than 4 cm.     

Toward improved daily snow cover mapping with advanced combination of MODIS and AMSR-E measurements

Taking three snow seasons from November 1 to March 31 of year 2002 to 2005 in northern Xinjiang, China as an example, this study develops a new daily snow cover product (500 m) through combining MODIS daily snow cover data and AMSR-E daily snow water equivalent (SWE) data. By taking advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data, the new daily snow cover product greatly complements the deficiency of MODIS product when cloud cover is present especially for snow cover product on a daily basis and effectively improves daily snow detection accuracy. In our example, the daily snow agreement of the new product with the in situ measurements at 20 stations is 75.4%, which is much higher than the 33.7% of the MODIS daily product in all weather conditions, even a little higher than the 71% of the MODIS 8-day product (cloud cover of ~ 5%). Our results also indicate that i) AMSR-E daily SWE imagery generally agrees with MOD10A1 data in detecting snow cover, with overall agreement of 93.4% and snow agreement of 96.6% in the study area; ii) AMSR-E daily SWE imagery underestimates the snow covered area (SCA) due to its coarse spatial resolution; iii) The new snow cover product can better and effectively capture daily SCA dynamics during the snow seasons, which plays a significant role in reduction, mitigation, and prevention of snow-caused disasters in pastoral areas.     

Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta

Because of the importance of rice for the global food security and because of the role of inundated paddy fields in greenhouse gases emissions, monitoring the rice production world-wide has become a challenging issue for the coming years. Local rice mapping methods have been developed previously in many studies by using the temporal change of the backscatter from C-band synthetic aperture radar (SAR) co-polarized data. The studies indicated in particular the need of a high observation frequency. In the past, the operational use of these methods has been limited by the small coverage and the poor acquisition frequency of the available data (ERS-1/2, Radarsat-1). In this paper, the method is adapted for the first time to map rice at large scale, by using wide-swath images of the Advanced SAR (ASAR) instrument onboard ENVISAT. To increase the observation frequency, data from different satellite tracks are combined. The detection of rice fields is achieved by exploiting the high backscatter increase at the beginning of the growing cycle, which allows the production of rice maps early in the season (in the first 50 days). The method is tested in the Mekong delta in Vietnam. The mapping results are compared to existing rice maps in the An Giang province, with a good agreement (higher than 81%). The rice planted areas are retrieved from the maps and successfully validated with the official statistics available at each province (R² = 0.92). These results show that the method is useful for large scale early mapping of rice areas, using current and future C band wide-swath SAR data.     

Continental-scale net radiation and evapotranspiration estimated using MODIS satellite observations

Evapotranspiration (ET) is a major pathway for water loss from many ecosystems, and its seasonal variation affects soil moisture and net ecosystem CO₂ exchange. We developed an algorithm to estimate ET using a semi-empirical Priestley-Taylor (PT) approach, which can be applied at a range of spatial scales. We estimated regional net radiation (R_net) at monthly time scales using MODerate resolution Imaging Spectroradiometer (MODIS) albedo and land surface temperature. Good agreement was found between satellite-based estimates of monthly R_net and field-measured R_net, with a RMSE of less than 30 W m^− 2. An adjustable PT coefficient was parameterized as a function of leaf area index and soil moisture based on observations from 27 AmeriFlux eddy covariance sites. The biome specific optimization using tower-based observations performed well, with a RMSE of 17 W m^− 2 and a correlation of 0.90 for predicted monthly latent heat. We implemented the approach within the hydrology module of the CASA biogeochemical model, and used it to estimate ET at a 1 km spatial resolution for the conterminous United States (CONUS). The RMSE of modeled ET was reduced to 21.1 mm mon^− 1, compared to 27.1 mm mon^− 1 in the original CASA model. The monthly ET rates averaged over the Mississippi River basin were similar to those derived using GRACE satellite measurements and river discharge data. ET varied substantially over the CONUS, with annual mean values of 110 ± 76 mm yr^− 1 in deserts, 391 ± 176 mm yr^− 1 in savannas and grasslands, and 840 ± 234 mm yr^− 1 in broadleaf forests. The PT coefficient was the main driver for the spatial variation of ET in arid areas, whereas R_net controlled ET when mean annual precipitation was higher than approximately 400 mm yr^− 1.     

Regional mapping of gross light-use efficiency using MODIS spectral indices

Direct estimation of photosynthetic light-use efficiency (LUE) from space would be of significant benefit to LUE-based models which use inputs from remote sensing to estimate terrestrial productivity. The Photochemical Reflectance Index (PRI) has shown promise in tracking LUE at the leaf- to small canopy levels, but its use at regional to global scales still remains a challenge. In this study, we used different formulations of PRI calculated from the MODIS ocean band centered at 531 nm and a set of alternative reference bands at 488, 551, and 678 nm to explore the relationship between PRI and LUE where LUE was measured at eight eddy covariance flux towers located in the boreal forest of Saskatchewan, Canada. The magnitude and variability of LUE was significantly lower at the times when useful MODIS ocean band images were available (i.e. around midday under clear-sky conditions) relative to the rest of the growing season. PRI₆₇₈ (reference band at 678 nm) showed the strongest relationship (r² = 0.70) with LUE_90a (i.e. 90-minute mean LUE calculated using Absorbed Photosynthetically Active Radiation, APAR), but only when all sites were combined. Overall, the relationships between the MODIS PRIs and LUE_90a were always stronger for observations closer to the backscatter direction and there were no significant differences in the strength of the correlations whether LUE was calculated based on incident PAR or on APAR. Predictions of ecosystem photosynthesis at the time of the MODIS overpasses were significantly improved by multiplying either PAR or APAR by MODIS PRI (r² improved from 0.09 to 0.44 and 0.54 depending on the PRI formulation).We used our PRI-LUE model to create a regional LUE_90a map for the three cover types covering 47,500 km² around the flux sites. The MODIS PRI-derived LUE_90a map appeared to capture more realistic spatial heterogeneity of LUE across the landscape compared to a daily LUE map derived using the look-up table in the MODIS GPP (MOD17) algorithm. While our LUE map is only a snapshot of minimum regional LUE_90a values, with appropriate gap-filling methods it could be used to improve regional-scale monitoring of GPP. Moreover, the strong relationship between midday and daily LUE on clear days (r² = 0.93) indicates that instantaneous MODIS observations of LUE_90a could be used to estimate daily LUE. Finally, pixel shadow fraction from the 5-Scale geometric-optical model was closely related to both MODIS PRI and tower-derived LUE suggesting that differences in stand leaf area and in diffuse illumination among flux sites play a role in the relationship we observed between LUE and MODIS PRI.     

Characterization of the tropical diurnal fire cycle using VIRS and MODIS observations

Seven years of data from the Tropical Rainfall Measuring Mission (TRMM) Visible and Infrared Scanner (VIRS) were used to characterize the average diurnal fire cycle in 15 regions of the tropics and sub-tropics. Bias errors in the resulting diurnal cycles were either avoided or removed through a combination of judicious region selection and the application of corrections to compensate for cloud obscuration and time-dependent “blind spots” in the fire-detection capability of the VIRS sensor. Supplementary data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board NASA's Terra satellite aided this process. In all regions, the local time of peak burning fell between 13:00 and 18:30, with fire activity peaking distinctly earlier for the heavily forested regions. The time period of the central 50% of total daily fire activity varied from a minimum of 1.3 h in North Central Africa to a maximum of 5.5 h in Eastern Australia. In general, shorter periods of burning were associated with greater tree cover. Using the diurnal cycles obtained for each region, an analysis of the drift in the local overpass times of the NOAA-7 through NOAA-14 afternoon satellites was performed. Results show that very large, spurious trends are likely to occur in a long-term Advanced Very High Resolution Radiometer (AVHRR) fire record due to differences in diurnal sampling over time.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Retrieving middle-infrared reflectance for burned area mapping in tropical environments using MODIS

The ephemeral character of the radiative signal together with the presence of aerosols imposes severe limitations on the use of classical approaches, e.g. based on red and near-infrared, to discriminate between burned and unburned surfaces in tropical environments. Surface reflectance in the middle-infrared (MIR) has been used to circumvent these difficulties because the signal is virtually unaffected by the presence of aerosols associated to biomass burning. Retrieval of the MIR reflected component from the total signal is, however, a difficult problem because of the presence of a diversity of radiance sources, namely the surface reflected solar irradiance and the surface emitted radiance that may reach comparable magnitude during daytime. The method proposed by Kaufman and Remer (1994) to retrieve surface MIR reflectance presents the advantage of not requiring auxiliary datasets (e.g. atmospheric profiles) nor major computational means (e.g. for solving radiative transfer models). Nevertheless, the method was specifically designed to retrieve MIR reflectance over dense dark forests in the middle latitudes and, as shown in the present study, severe problems may arise when applying it beyond the range of validity, namely for burned area mapping in tropical environments. The present study consists of an assessment of the performance of the method for a wide range of atmospheric, geometric and surface conditions and of the usefulness of extracted surface reflectances for burned area discrimination. Results show that, in the case of tropical environments, there is a significant decrease in performance of the method for high values of land surface temperature, especially when associated with low sun elevation angles. Burned area discrimination is virtually impaired in such conditions, which are often present when using data from instruments on-board polar orbiters, namely MODIS in Aqua and Terra, to map burned surfaces over the Amazon forest and “cerrado” savanna regions.     

Landsat-based land cover mapping in the lower Yuna River watershed in the Dominican Republic

To aid in the environmental planning and management of Los Haitises National Park, a neotropical park in the Dominican Republic, a land cover change analysis was performed on the lower Yuna River watershed, within which a portion of the park exists and which contains a diversity of agricultural practices. Separate image classifications were performed on a 1973 Landsat MSS image and a 1985 Landsat TM image with resulting overall classification accuracies of 77.3 per cent and 81.3 per cent, respectively. In both classifications, spectral similarities between the various growth stages of rice, mangrove, orchard, and permanent grassland made separation and delineation of these classes difficult. The implications of land cover trends within the watershed for ecologic and economic management issues which affect both the watershed and the park were discussed.     

Winter wheat mapping using temporal signatures of MODIS vegetation index data

Because most land-cover types have distinct seasonal changes and corresponding reflectance characteristics in remotely sensed images, the signatures in time-series data are useful for discriminating different land covers. Although temporal signatures have been used to classify different land-cover types, they have not been fully exploited to classify specific crops, and the influence of low resolution should be evaluated. The aims of this study were to seek an effective method to classify specific crops using the temporal signatures in coarse time-series data and to examine the applicability of the data for crop classification as well. A winter wheat-producing region in China was selected for this case study. Moderate-Resolution Imaging Spectroradiometer (MODIS) 8-day composite land surface reflectance product (MOD09Q1) data with a 250 m spatial resolution were used to calculate the vegetation index data, which was applied to detect the properties of live green plants. The noise in the time series was filtered to minimize the classification uncertainties. The curve shape in the time-series vegetation index profile was used as the major metric to classify winter wheat, and other phenological metrics extracted from the data were used conjunctly as auxiliary functions to improve the separability. The metrics for winter wheat classification were quantified in the large fields with relatively pure pixels. Winter wheat was successfully extracted from the MODIS vegetation index data, and the MODIS-derived result was validated with a fine-resolution (19.5 m) thematic map derived from images collected by the charge-coupled device sensor on board the China–Brazil Earth Resources Satellite (CBERS). It showed that the MODIS-derived result had inevitable low-resolution bias, and the errors of commission and omission were 32.3 and 33.8%, respectively. The overall classification effect of the MODIS-derived result relied upon the distribution of pixel purity in the study area.     

基于MODIS数据的黑河流域土壤热惯量反演研究

热惯量法在土壤水分反演中有着广泛的应用。以MODIS数据为基础,选用真实热惯量模型,反演得到了黑河流域的土壤热惯量,为进一步研究流域土壤水分提供可靠的方法和数据。利用地面实测数据对模型参数及反演结果进行了验证,并分析了地表昼夜最大温差、地表反照率及土壤热惯量的季节性变化规律,同时对比了真实热惯量模型与表观热惯量模型反演结果与土壤水分的相关性。结果表明：地表温差、地表反照率及土壤热惯量都具有明显的季节性变化特征;真实热惯量模型相对于表观热惯量模型更有利于土壤水分的反演,且具有广泛的适用性。     

MODIS地表温度产品的验证研究——以黑河流域为例

分析了影响MODIS地表温度产品精度的主要因素,并对这些因素综合作用下的MODIS地表温度产品的精度验证方法进行了回顾和比较。针对MODIS地表温度产品在干旱半干旱地区误差偏大的状况,以黑河流域为例,对MODIS地表温度产品进行了验证。用于验证的地面观测数据包括自动气象站红外辐射温度计数据和长波辐射数据。这里结合具体的...     

Drought impact on vegetation productivity in the Lower Mekong Basin

The Lower Mekong Basin (LMB) has a typical monsoon climate, with high temperatures and an uneven distribution of precipitation throughout the year. This climate, combined with the geographic position of the LMB, has led to an increase in the frequency of extreme weather events over last decade. However, few previous studies have used remote-sensing data to investigate the impact of such weather events, particularly severe droughts, on biological productivity in the LMB. To address this, we assessed the impact of drought on vegetation productivity in the LMB during 2000–2011 using MOD17 products. Several drought events were identified during this period. Of these, the most severe occurred during 2005 and 2010, although the 2005 drought was both more extensive and more intense. Net primary productivity (NPP) exhibited considerable variation during 2000–2011: the droughts in 2005 and 2010 reduced NPP by 14.7% and 8.4%, respectively. The impact of drought on NPP in 2005 was much greater than that in 2010, likely owing to the longer duration and larger deficit of precipitation in 2005 (which lasted from winter 2004 to spring 2005). Our results demonstrate that severe drought had a greater impact on NPP than mild drought, especially for forests, woodlands, and shrublands. Comparatively, little variation in NPP was found for croplands, even under drought conditions, which were attributed to the wide use of irrigation and the exploitation of water sources during drought periods. Moreover, multi-season croplands in Vietnam experienced only a small reduction in gross primary productivity (GPP) in 2005 compared to one-season croplands in Cambodia, which can be related to the shorter growing periods of the former impacted by droughts.     

Derivation of melt pond coverage on Arctic sea ice using MODIS observations

The formation of meltponds on the surface of sea ice during summer is one of the main factors affecting variability in surface albedo over the ice cover. However, observations of the spatial extent of ponding are rare. To address this, a MODIS surface reflectance product is used to derive the daily melt pond cover over sea ice in the Beaufort/Chukchi Sea region through the summer of 2004. For this region, the estimated pond cover increased rapidly during the first 20 days of melt from 10% to 40%. Fluctuations in pond cover occurred through summer, followed by a more gradual decrease through late August to 10%. The rapid initial increase in pond cover occurred later as latitude increased and melt progressed northward.

A surface campaign at Barrow in June 2004 provided pond and ice spectral reflectance needed by the MODIS algorithm to deduce pond coverage. Although individual pond and ice reflectance varies within the comparatively small region of measurement, the mean values used within the algorithm ensured that relevant values (i.e. concurrent with satellite observations) were being applied.

Aerosonde unpiloted aerial vehicles (UAVs) were deployed in June 2004 from Barrow, Alaska, to photograph the sea ice so melt pond cover could be estimated. Although the agreement between derived pond cover from UAV photos and estimates from MODIS varies, the mean estimates and distribution of pond coverages are similar, suggesting that the MODIS technique is useful for estimating pond coverage throughout the region. It is recommended that this technique be applied to the entire Arctic through the melt season.     

Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (T_a) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (T_max), mean (T_mean), and minimum air temperature (T_min) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily T_a and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best T_mean and T_max models were satisfactory (coefficient of determination (R²) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the T_min models (R² = 0.80–0.81 and RSE = 2.83–3.00 K). In this article T_min models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best T_min models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved T_min models yield R² = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R² and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the T_min models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.     

Quantitative mapping of global land degradation using Earth observations

Land degradation is a global issue on par with climate change and loss of biodiversity, but its extent and severity are only roughly known and there is little detail on the immediate processes – let alone the drivers. Earth-observation methods enable monitoring of land degradation in a consistent, physical way and on a global scale by making use of vegetation productivity and/or loss as proxies. Most recent studies indicate a general greening trend, but improved data sets and analysis also show a combination of greening and browning trends. Statistically based linear trends average out these effects. Improved understanding may be expected from data-driven and process-modelling approaches: new models, model integration, enhanced statistical analysis and modern sensor imagery at medium spatial resolution should substantially improve the assessment of global land degradation.     

Detection of burned areas from mega-fires using daily and historical MODIS surface reflectance

The detection and mapping of burned areas from wildland fires is one of the most important approaches for evaluating the impacts of fire events. In this study, a novel burned area detection algorithm for rapid response applications using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data was developed. Spectra from bands 5 and 6, the composite indices of the Normalized Burn Ratio, and the Normalized Difference Vegetation Index were employed as indicators to discover burned pixels. Historical statistical data were used to provide pre-fire baseline information. Differences in the current (post-fire) and historical (pre-fire) data were input into a support vector machine classifier, and the fire-affected pixels were detected and mapped by the support vector machine classification process. Compared with the existing MODIS level 3 monthly burned area product MCD45, the new algorithm is able to generate burned area maps on a daily basis when new data become available, which is more applicable to rapid response scenarios when major fire incidents occur. The algorithm was tested in three mega-fire cases that occurred in the continental USA. The experimental results were validated against the fire perimeter database generated by the Geospatial Multi-Agency Coordination Group and were compared with the MCD45 product. The validation results indicated that the algorithm was effective in detecting burned areas caused by mega-fires.     

基于MODIS数据的玛纳斯河山区雪盖年内变化特征研究

基于2000年～2010年的MODIS/Terra积雪8d合成数据(MOD10A2)与DEM数据,通过计算和分析不同高程带、不同坡向和不同坡度的积雪覆盖率,研究了新疆玛纳斯河山区雪盖的年内变化特征。结果表明:①研究区平均积雪覆盖率最高为一月中旬的67.8%,最低为七月中旬的11.9%,年内变化总体上呈V字型,积雪分布与气温关系密切;②可将研究区雪盖年内分布情况归纳为1600m以下、1600m～3800m和3800m以上共三个高程带,各高程带内雪盖分布的年内变化较为相似,不同高程带则差异明显。从年内波动情况来看,低海拔地区年内波动幅度最大,随着海拔上升,波动幅度逐渐减小;③3800m以下各坡向和坡度地区积雪覆盖率均表现为一月最高,七月最低,四月和十月介于二者之间,而3800m以上地区积雪覆盖率全年最高值则出现在四月和十月;④各坡度和坡向区域雪盖的年内变化与所在高程带的总体情况基本相似,说明坡度和坡向对雪盖分布的影响是在高程影响的基础上产生的。