Estimating the area of stubble burning from the number of active fires detected by satellite

To estimate the area affected by stubble burning in southern Australia, use was made of observations from the MODIS (MODerate resolution Imaging Spectroradiometer) on the Terra and Aqua satellites. The burnt area (BA) was calculated from the number of active fires, known as fire hot spots (FHS) using parameters estimated from a survey of farms in the agricultural area of south-western Australia. The study also served as a ground validation of the capability and limitations of the MODIS sensor and associated algorithm for detection of small agricultural fires.During the period from 1 March to 23 May 2005, 3240 unique FHS associated with stubble burning were detected. The majority of these FHS occurred in the afternoon in the last 3 weeks of April. To estimate the total area of stubble burning associated with these FHS, a survey was sent to 2066 farmers. This survey determined for each farm, the number of fields in which stubble was burnt, average size of field burnt (A), crop types burnt, dates and time of day of burning. Responses were received from 273 farms, 38% of whom reported over 500 stubble burns. The 3240 FHS were intersected with the polygons of the farm boundaries to determine the proportion (P) of stubble burns detected using MODIS. Only 13% (± 3%) of the stubble burns recorded in the farm survey were detected. Average field size burnt was 75 ha (± 6 ha). Total BA was calculated as: BA = A ^? 3240/P, which gave an estimated area of 1.87 million ha. This area was 27% of the total cropped area in south-western Australia. This level of stubble burning was similar to that determined by a 1990s survey in the State of Victoria.Neither cloud cover nor field size was significantly correlated with the low number of stubble fires that were detected. Therefore it was concluded that many stubble burns went undetected because of the lack of coincidence between the time of the MODIS overpass and when stubble burns were initiated. Also the use of additional sensors such as the Advanced Very High Resolution Radiometer (AVHRR) on NOAA satellites with afternoon overpasses would improve the fraction of stubble burns detected.Across the whole of Australia where winter cropping occurs, there was a high correlation (r² = 0.96) between FHS and total cropped area in each State. This provided the basis for extrapolating the field results from south-western Australia, to estimate the total area of stubble burning in southern Australia for 2005.     

Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset

In monsoon Asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. We evaluated the quality of satellite remote sensing of paddy phenology: (1) through continuous in situ observations of a paddy field in Japan for 1.5 years, we investigated phenological signals in the reflectance spectrum of the paddy field; (2) we tested daily satellite data taken by Terra/Aqua MODIS (MOD09 and L1B products) with regard to the agreement with the in situ data and the influence of cloud contamination. As a result, the in situ spectral characteristics evidently indicated some phenological changes in the rice paddy field, such as irrigation start, padding, heading, harvest and ploughing. The Enhanced Vegetation Index (EVI) was the best vegetation index in terms of agreement with the in situ data. More than 65% of MODIS observations were contaminated with clouds in this region. However, the combined use of Terra and Aqua decreased the rate of cloud contamination of the daily data to 43%. In conclusion, the most robust dataset for monitoring rice paddy phenology in monsoon Asia would be daily EVI derived from a combination of Terra/MODIS and Aqua/MODIS.     

Analysis of leaf area index products from combination of MODIS Terra and Aqua data

A prototype product suite, containing the Terra 8-day, Aqua 8-day, Terra-Aqua combined 8- and 4-day products, was generated as part of testing for the next version (Collection 5) of the MODerate resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI) products. These products were analyzed for consistency between Terra and Aqua retrievals over the following data subsets in North America: single 8-day composite over the whole continent and annual time series over three selected MODIS tiles (1200 × 1200 km). The potential for combining retrievals from the two sensors to derive improved products by reducing the impact of environmental conditions and temporal compositing period was also explored. The results suggest no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectances products. The differences over smaller regions, however, can be large due to the random nature of residual atmospheric effects. High quality retrievals from the radiative transfer based algorithm can be expected in 90-95% of the pixels with mostly herbaceous cover and about 50-75% of the pixels with woody vegetation during the growing season. The quality of retrievals during the growing season is mostly restricted by aerosol contamination of the MODIS data. The Terra-Aqua combined 8-day product helps to minimize this effect and increases the number of high quality retrievals by 10-20% over woody vegetation. The combined 8-day product does not improve the number of high quality retrievals during the winter period because the extent of snow contamination of Terra and Aqua observations is similar. Likewise, cloud contamination in the single-sensor and combined products is also similar. The LAI magnitudes, seasonal profiles and retrieval quality in the combined 4-day product are comparable to those in the single-sensor 8-day products. Thus, the combined 4-day product doubles the temporal resolution of the seasonal cycle, which facilitates phenology monitoring in application studies during vegetation transition periods. Both Terra and Aqua LAI products show anomalous seasonality in boreal needle leaf forests, due to limitations of the radiative transfer algorithm to model seasonal variations of MODIS surface reflectance data with respect to solar zenith angle. Finally, this study suggests that further improvement of the MODIS LAI products is mainly restricted by the accuracy of the MODIS observations.     

Fire radiative energy for quantitative study of biomass burning: derivation from the BIRD experimental satellite and comparison to MODIS fire products

A major focus in global change research is to quantify the amount of gaseous and particulate pollutants emitted from terrestrial vegetation fires. Determination of the emitted radiant energy released during biomass combustion episodes (the so-called fire radiative energy or FRE) has been suggested as a new tool for determining variations in biomass combustion rates and the rate of production of atmospheric pollutants. We review the physical principals behind the remote determination of FRE and present an alternative method for its derivation via analysis of ‘fire pixel’ radiances in the middle infrared spectral region. We compare our method to the existing FRE retrieval approach used in the EOS Moderate Resolution Imaging Spectro-radiometer (MODIS) fire products, and to retrievals of FRE based on derived fire temperature and area made via the so-called Bi-spectral method. We test each FRE retrieval method using both simulated data and imagery from a new experimental space mission, the Bi-spectral InfraRed Detection (BIRD) small satellite, which has sensors specifically designed for the study of active fires. We analyse near simultaneous MODIS and BIRD data of the fires that burned around Sydney, Australia in January 2002. Despite the markedly different pixel size and spectral coverage of these sensors, where the spatial extent of the fire pixel groups detected by MODIS and BIRD are similar, the derived values of FRE for these fires agree to within ±15 %. However, in certain fires, the lower spatial resolution of MODIS appears to prevent many of the less intensely radiating fire pixels being detected as such, meaning MODIS underestimates FRE for these fires by up to 46% in comparison to BIRD. Though the FRE release of each of these low intensity fire pixels is relatively low, their comparatively large number makes their overall FRE significant. Thus, total FRE release of the Sydney fires on 5 January 2002 is estimated to be 6.5×10⁹ J s⁻¹ via BIRD but 4.0×10⁹ J s⁻¹ via MODIS. The ability of BIRD to resolve individual fire fronts further allows the first accurate calculation of ‘radiative’ fireline intensity from spaceborne measurements, providing values of 15-75 kJ s⁻¹ m⁻¹ for fire fronts that are up to 9 km in length. Finally, we analyse the effectiveness of the satellite-based FRE retrieval methods in estimating the FRE from the active flaming and smouldering components only (FRE_Active, believed to be proportional to the rate of biomass combustion), despite the sensor receiving additional radiance from the ‘cooling ground’. The MIR radiance method appears particularly strong in this regard, allowing FRE_Active to be estimated to within ±30% in the range 100-100,000 J s⁻¹ m⁻². These results provide further confidence in the ability of spaceborne missions to derive physically meaningful values of FRE that could be used to support biomass burning emissions inventories. Future comparisons between FRE derived via MODIS and those from higher spatial resolution BIRD or airborne imagery may allow the MODIS-derived FRE values to be ‘calibrated’ for any systematic underestimation. We therefore expect FRE to become an important tool for enhancing global studies of terrestrial vegetation fires with infrared remote sensing, particularly as the majority of large fires are now imaged four times per day via the MODIS instruments on the Terra and Aqua spacecraft.     

Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data

In this study we implemented a comprehensive analysis to validate the MODIS and GOES satellite active fire detection products (MOD14 and WFABBA, respectively) and characterize their major sources of omission and commission errors which have important implications for a large community of fire data users. Our analyses were primarily based on the use of 30 m resolution ASTER and ETM+ imagery as our validation data. We found that at the 50% true positive detection probability mark, WFABBA requires four times more active fire area than is necessary for MOD14 to achieve the same probability of detection, despite the 16× factor separating the nominal spatial resolutions of the two products. Approximately 75% and 95% of all fires sampled were omitted by the MOD14 and WFABBA instantaneous products, respectively; whereas an omission error of 38% was obtained for WFABBA when considering the 30-minute interval of the GOES data. Commission errors for MOD14 and WFABBA were found to be similar and highly dependent on the vegetation conditions of the areas imaged, with the larger commission errors (approximately 35%) estimated over regions of active deforestation. Nonetheless, the vast majority (> 80%) of the commission errors were indeed associated with recent burning activity where scars could be visually confirmed in the higher resolution data. Differences in thermal dynamics of vegetated and non-vegetated areas were found to produce a reduction of approximately 50% in the commission errors estimated towards the hours of maximum fire activity (i.e., early-afternoon hours) which coincided with the MODIS/Aqua overpass. Lastly, we demonstrate the potential use of temporal metrics applied to the mid-infrared bands of MODIS and GOES data to reduce the commission errors found with the validation analyses.     

An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data

Accurate high-resolution soil moisture data are needed for a range of agricultural and hydrologic activities. To improve the spatial resolution of ∼ 40 km resolution passive microwave-derived soil moisture, a methodology based on 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) red, near-infrared and thermal-infrared data has been implemented at 4 km resolution. The three components of that method are (i) fractional vegetation cover, (ii) soil evaporative efficiency (defined as the ratio of actual to potential evaporation) and (iii) a downscaling relationship. In this paper, 36 different disaggregation algorithms are built from 3 fractional vegetation cover formulations, 3 soil evaporative efficiency models, and 4 downscaling relationships. All algorithms differ with regard to the representation of the nonlinear relationship between microwave-derived soil moisture and optical-derived soil evaporative efficiency. Airborne L-band data collected over an Australian agricultural area are used to both generate ∼ 40 km resolution microwave pixels and verify disaggregation results at 4 km resolution. Among the 36 disaggregation algorithms, one is identified as being more robust (insensitive to soil, vegetation and atmospheric variables) than the others with a mean slope between MODIS-disaggregated and L-band derived soil moisture of 0.94. The robustness of that algorithm is notably assessed by comparing the disaggregation results obtained using composited (averaged) Terra and Aqua MODIS data, and using data from Terra and Aqua separately. The error on disaggregated soil moisture is systematically reduced by compositing daily Terra and Aqua data with an error of 0.012 vol./vol.     

Addressing the spatiotemporal sampling design of MODIS to provide estimates of the fire radiative energy emitted from Africa

Satellite-based estimates of the fire radiative power (FRP) and energy (FRE) emitted from open biomass burning are affected by the spatiotemporal resolution of polar-orbiting and geostationary sensors. Here the impacts of the MODIS sampling design on estimates of FRE are characterized by superimposing the timing and extents of the Terra and Aqua granules onto the SEVIRI active fire product. Results for different land-cover types across Africa indicate that the FRE measured by SEVIRI during eight days is linearly related to the sum of FRP measured by SEVIRI within the MODIS granules. These relationships are least variable during the height of the fire season when diurnal cycles of FRP measured by SEVIRI are most consistent. Relationships between FRE and the sum of FRP developed using the SEVIRI active fire product are directly applied to the sum of FRP retrieved from the MODIS Terra and Aqua Climate Modeling Grid (CMG) fire products. Estimates of FRE from MODIS herein agree within 5% of those obtained from previously published methods, but remain a factor of 0.72 times those obtained by adjusting SEVIRI measurements of FRE to account for low spatial resolution detection biases. An examination of the MODIS scan geometry suggests that the latter underestimation is attributed to the coupling between a MODIS imaging artefact referred to as the “bow-tie” effect and the typical calculation used to retrieve the sum of FRP from the MODIS CMG fire products. Depending on the availability of MODIS scan angle information, we offer rigorous and simplified calculations to account for the bow-tie effect. Applying the simplified adjustment to the MODIS CMG fire products yields national estimates of monthly FRE that are 1.44 times greater than originally predicted.     

Using multiple endmember spectral mixture analysis to retrieve subpixel fire properties from MODIS

The Moderate-Resolution Imaging Spectroradiometer (MODIS) sensors on NASA's Terra and Aqua satellites image most of the Earth multiple times each day, providing useful data on fires that cannot be practically acquired using other means. Unfortunately, current fire products from MODIS and other sensors leave large uncertainties in measurements of fire sizes and temperatures, which strongly influence how fires spread, the amount and chemistry of their gas and aerosol emissions, and their impacts on ecosystems. In this study, we use multiple endmember spectral mixture analysis (MESMA) to retrieve subpixel fire sizes and temperatures from MODIS, possibly overcoming some limitations of existing methods for characterizing fire intensities such as estimating the fire radiative power (FRP). MESMA is evaluated using data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to assess the performance of FRP and MESMA retrievals of fire properties from a simultaneously acquired MODIS image, for a complex of fires in Ukraine from August 21, 2002. The MESMA retrievals of fire size described in this paper show a slightly stronger correlation than FRP does to fire pixel counts from the coincident ASTER image. Prior to this work, few studies, if any, had used MESMA for retrieving fire properties from a broad-band sensor like MODIS, or compared MESMA to higher-resolution fire data or other measures of fire properties like FRP. In the future, MESMA retrievals could be useful for fire spread modeling and forecasting, reducing hazards that fires pose to property and health, and enhancing scientific understanding of fires and their effects on ecosystems and atmospheric composition.     

An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States

Traditional fire detection algorithms mainly rely on hot spot detection using thermal infrared (TIR) channels with fixed or contextual thresholds. Three solar reflectance channels (0.65 μm, 0.86 μm, and 2.1 μm) were recently adopted into the MODIS version 4 contextual algorithm to improve the active fire detection. In the southeastern United States, where most fires are small and relatively cool, the MODIS version 4 contextual algorithm can be adjusted and improved for more accurate regional fire detection. Based on the MODIS version 4 contextual algorithm and a smoke detection algorithm, an improved algorithm using four TIR channels and seven solar reflectance channels is described. This approach is presented with fire events in the southeastern United States. The study reveals that the T₂₂ of most small, cool fires undetected by the MODIS version 4 contextual algorithm is lower than 310 K. The improved algorithm is more sensitive to small, cool fires in the southeast especially for fires detected at large scan angles.     

Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data

Vegetation phenology is the chronology of periodic phases of development. It constitutes an efficient bio-indicator of impacts of climate changes and a key parameter for understanding and modelling vegetation-climate interactions and their implications on carbon cycling. Numerous studies were devoted to the remote sensing of vegetation phenology. Most of these were carried out using data acquired by AVHRR instrument onboard NOAA meteorological satellites. Since 1999, multispectral images were acquired over the whole earth surface every one to two days by MODIS instrument onboard Terra and Aqua platforms. In comparison with AVHRR, MODIS constitutes a significant technical improvement in terms of spatial resolution, spectral resolution, geolocation accuracy, atmospheric corrections scheme and cloud screening and sensor calibration. In this study, 250 m daily MODIS data were used to derive precise vegetation phenological dates over deciduous forest stands. Phenological markers derived from MODIS time-series and provided by MODIS Global Land Cover Dynamics product (MOD12Q2) were compared to field measurements carried out over the main deciduous forest stands across France and over five years. We show that the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands. At plot level, the prediction uncertainty is 8.5 days and the bias is 3.5 days. MODIS Global Land Cover Dynamics MOD12Q2 provides estimates of onset of green-up dates which deviate substantially from in situ observations and do not perform better than the null model. RMSE values are 20.5 days (bias -17 days) using the onset of greenness increase and 36.5 days (bias 34.5 days) using the onset of greenness maximum. An improvement of prediction quality is obtained if we consider the average of MOD12Q2 onset of greenness increase and maximum as marker of onset of green-up date. RMSE decreases to 16.5 days and bias to 7.5 days.     

Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology

Monitoring the extent and pattern of snow cover in the dry, high altitude, Trans Himalayan region (THR) is significant to understand the local and regional impact of ongoing climate change and variability. The freely available Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover images, with 500 m spatial and daily temporal resolution, can provide a basis for regional snow cover mapping, monitoring and hydrological modelling. However, high cloud obscuration remains the main limitation. In this study, we propose a five successive step approach — combining data from the Terra and Aqua satellites; adjacent temporal deduction; spatial filtering based on orthogonal neighbouring pixels; spatial filtering based on a zonal snowline approach; and temporal filtering based on zonal snow cycle — to remove cloud obscuration from MODIS daily snow products. This study also examines the spatial and temporal variability of snow cover in the THR of Nepal in the last decade. Since no ground stations measuring snow data are available in the region, the performance of the proposed methodology is evaluated by comparing the original MODIS snow cover data with least cloud cover against cloud-generated MODIS snow cover data, filled by clouds of another densely cloud-covered product. The analysis indicates that the proposed five-step method is efficient in cloud reduction (with average accuracy of > 91%). The results show very high interannual and intra-seasonal variability of average snow cover, maximum snow extent and snow cover duration over the last decade. The peak snow period has been delayed by about 6.7 days per year and the main agropastoral production areas of the region were found to experience a significant decline in snow cover duration during the last decade.     

Evaluation of the MODIS (MOD10A1) daily snow albedo product over the Greenland ice sheet

This study evaluates the performance of the beta-test MODIS (MOD10A1) daily albedo product using in situ data collected in Greenland during summer 2004. Results indicate the beta-test product tracks the general seasonal variability in albedo but exhibits significant more temporal variability than observed at the stations. This may indicate problems with the cloud detection algorithm, and/or failure of the BRDF model to adequately model the bidirectional reflectance of snow. Comparisons with in situ observations at five automatic weather stations in Greenland indicate an overall RMSE of 0.067 for the Terra instrument and an RMSE of 0.075 on Aqua. The Terra-retrieved-albedo are slightly better correlated with the in situ data than the Aqua retrievals (r = 0.79 versus r = 0.77). Comparisons were also made between the MODIS daily albedo product and the MODIS 16-day albedo product (MOD43B3). Results indicate general correspondence between the two products, with better agreement found using the Terra-retrieved-albedo than the Aqua-retrieved albedo. The reason for the differences in albedo between the Aqua and Terra satellites remains unclear. At the stations examined, both the Terra and Aqua retrievals were made at nearly the same time of the day and therefore the differences in albedo between the satellites cannot be explained by differences in solar illumination. Finally, the albedo derived using MODIS data and the direct estimation algorithm (DEA) was also compared with 2004 Greenland in situ data. Results from this comparison suggest that the DEA performs well as long as the solar zenith angle of the observation is not greater than 70°.     

Long-term high-frequency validation of MODIS LST products in a Mediterranean mountain environment

The accuracy of Moderate-resolution Imaging Spectroradiometer (MODIS) level 3 1 km land surface temperature (LST) products was assessed through long-term validation carried out in a mountainous site in Sierra Nevada, southeast Spain. A total of 1458 day and night thermal images, acquired by Terra and Aqua satellites during 2008, were processed and compared to ground-truth data recorded at the meteorological station of Robledal de Cañar with a frequency of one measurement every 10 min. The purpose of this investigation was to understand whether MODIS LST data can be used as input for climate models to be constructed for mountainous environments. Several trends in the MODIS LST data were observed, including the underestimation of daytime values and the overestimation of night-time values. Although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation coefficients with ground measurements, only night values maintained a relatively high accuracy of approximately 2°C of annual average error. Factors that may cause errors in the MODIS LST data, like acquisition angle, cloud, and snow cover, were analysed without conclusive results. High accuracy levels, i.e. close to 1°C, similar to other validation studies carried out over simpler and much more homogenous land-cover types such as cultivated fields, have been achieved for night images acquired during the summer months, thus making these datasets reliable for their use in climatic models over mountainous regions.     

Assessing two large area burnt area products across Australian Southern Forests

Burnt area is a critical parameter for estimating emissions of greenhouse gases associated with biomass burning. Several burnt area products (BAPs) derived from Earth Observation satellites/sensors have been released; these are based on different spatial resolutions and derived using different methodologies so that accuracies can vary amongst them. This study validates a global (MODIS) and a national (AVHRR) BAP across Australian southern forests using two reference datasets: state fire histories (SFHs) from 2000 to 2013 and a forest cover map derived through high resolution air photo interpretation (API). The spatial and temporal agreement between fires in the BAPs and reference SFH were analysed based on 2610 sample points representative of Australian southern forest types (successful detection was evaluated according to fire type: planned burn vs. wildfire, size of fire, and land tenure). Results show that both BAPs were most successful when identifying large wildfires (>5000 ha). Overall accuracy for AVHRR and MODIS was 73.9% and 62.5%, respectively. When compared to the API derived forest cover map as reference dataset, both products achieved higher overall accuracies (94.1% for AVHRR and 87.1% for MODIS); an expected result given that the fires detected in this dataset are known to be observable using Earth observation data. But regardless of reference dataset, the AVHRR BAP which is tailored to Australian conditions achieved better results than the MODIS global BAP. Also, the AVHRR archive in Australia goes back to 1988, which is an important consideration for calculating wildfire history for greenhouse gas accounting.     

New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America

Vegetation fires are a key global terrestrial disturbance factor and a major source of atmospheric trace gases and aerosols. Therefore, many earth-system science and operational monitoring applications require access to repetitive, frequent and well-characterized information on fire emissions source strengths. Geostationary imagers offer important temporal advantages when studying rapidly changing phenomena such as vegetation fires. Here we present a new algorithm for detecting and characterising active fires burning within the imager footprints of the Geostationary Operational Environmental Satellites (GOES), including consideration of cloud-cover and calculation of fire radiative power (FRP), a metric shown to be strongly related to fuel consumption and smoke emission rates. The approach is based on a set of algorithms now delivering near real time (NRT) operational FRP products from the Meteosat Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) imager (available from http://landsaf.meteo.pt/), and the GOES processing chain presented here is designed to deliver a compatible fire product to complete geostationary coverage of the Western hemisphere. Results from the two GOES imagers are intercompared, and are independently verified against the well regarded MODIS cloud mask and active fire products. We find that the detection of cloud and active fires from GOES matches that of MODIS very well for fire pixels having FRP > 30 MW, when the GOES omission error falls to less than 10%. The FRP of fire clusters detected near simultaneously by both GOES and MODIS have a bias of only 22 MW, and a similar bias is found when comparing near-simultaneous GOES East and GOES West FRP observations. However, many fire pixels having FRP < 30 MW remain undetected by GOES, probably unavoidably since it has a much coarser spatial resolution than MODIS. Adjustment using data from the less frequent but more accurate views obtained from high spatial resolution polar orbiting imagers could be used to bias correct regional FRP totals. Temporal integration of the GOES FRP record indicates that during the summer months, biomass burning combusts thousands of millions of tonnes of fuel daily across the Americas. Comparison of these results to those of the Global Fire Emissions Database (GFEDv2) indicate strong linear relationships (r² > 0.9), suggesting that the timely FRP data available from a GOES real-time data feed is likely to be a suitable fire emissions source strength term for inclusion in schemes aiming to forecast the concentrations of atmospheric constituents affected by biomass burning.     

Estimating afternoon MODIS land surface temperatures (LST) based on morning MODIS overpass,location and elevation information

The MODerate Resolution Imaging Spectroradiometer (MODIS) instrument on‐board the Terra and Aqua satellites is a critical tool for providing daily estimates of land surface temperature (LST). Terra launched in late 1999 has a morning (AM) overpass, whereas Aqua launched in early 2002 has an afternoon (PM) overpass. Generally, LST is expected, under cloudless conditions, to be warmer in the early afternoon than the morning due to the link between maximum skin temperature and solar insolation peak time, therefore the Aqua PM LST is likely to be closer to the maximum daily LST than that acquired from Terra. This letter investigated differences between the Aqua MODIS PM and Terra MODIS AM LST estimates over a range of land cover classes, locations, and dates, across Canada. The aim was to develop a simple adjustment which can be applied to Terra AM LST estimates to approximate a “synthetic” Aqua PM LST product from 2000 to mid‐2002, thereby providing a seamless afternoon MODIS LST product from 2000 to 2006. Results indicate that there are statistically significant differences between the AM and PM LST ranging from 0.3°C to 3.2°C depending on cover type, and between 1.2° and 5.0° depending on time of year. On average, over 90% of the variation observed in the PM record can be explained by the AM LST, land cover types and location.     

Monitoring forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System nighttime satellite data

Monitoring and management of forest fires is very important in countries like India where 55% of the total forest cover is prone to fires annually. The present study aims at effective monitoring of forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime satellite data and to evaluate the active fire detection capabilities of the sensor. Nightly DMSP-OLS fire products were generated from February to May 2005 (peak fire season) and analyzed to study the occurrence and behavior of fires over different forest physiognomies in Indian region. Fire products generated from DMSP-OLS were validated with ground observations of fire records from state forest departments to evaluate the accuracy of fire products. Further, inter-comparison of the DMSP-OLS derived fire products with contemporary fire products from Moderate resolution Imaging Spectroradiometer (MODIS) (both daytime and nighttime products) in addition to fires and burnt areas derived from Indian Remote sensing Satellite (IRS-P6) Advanced Wide Field Sensor (AWiFS) data has been done to analyze spatial agreement of fire locations given by the above sensors.Results from the DMSP-OLS fire products (derived from February to May 2005) over Indian region showed high forest fires in southern dry deciduous forests during February-March; central Indian dry and mixed deciduous forests during March-April; northeastern tropical forests during February-April and northern pine forests during May. Spatial pattern in fires showed a typical seasonal shift in fire activity from the southern dry deciduous forests to the northern pine forests and temperate forests as the fire season progressed. Statistical evaluation of DMSP-OLS fire products with ground observations showed an over all accuracy of 98%. Comparison of DMSP-OLS derived fires with consecutive MODIS and AWiFS derived fires for individual days indicated that 69% of the fires continued from current day (DMSP-OLS pass around ∼ 7 pm to ∼ 10 pm local time) to the next day (MODIS and AWiFS pass ∼ 10:30 am local time). Comparison of DMSP-OLS derived fires with burnt areas estimated from AWiFS showed that 98% of DMSP-OLS derived fires on the current day fell within the burnt area of AWiFS on subsequent day. Since the worst forest fires are those that extend from the current to the consecutive days, DMSP-OLS derived fires provide a valuable augmentation to the fires derived from other sensors operating in daytime.     

Fire and smoke observed from the Earth Observing System MODIS instrument--products,validation, and operational use

The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, launched on the National Aeronautics and Space Administration Terra satellite at the end of 1999, was designed with 36 spectral channels for a wide array of land, ocean, and atmospheric investigations. MODIS has a unique ability to observe fires, smoke, and burn scars globally. Its main fire detection channels saturate at high brightness temperatures: 500 K at 4 µm and 400 K at 11 µm, which can only be attained in rare circumstances at the 1 km fire detection spatial resolution. Thus, unlike other polar orbiting satellite sensors with similar thermal and spatial resolutions, but much lower saturation temperatures (e.g. Advanced Very High Resolution Radiometer and Along Track Scanning Radiometer), MODIS can distinguish between low intensity ground surface fires and high intensity crown forest fires. Smoke column concentration over land is for the first time being derived from the MODIS solar channels, extending from 0.41 µm to 2.1 µm. The smoke product has been provisionally validated both globally and regionally over southern Africa and central and south America. Burn scars are observed from MODIS even in the presence of smoke, using the 1.2 to 2.1 µm channels. MODIS burned area information is used to estimate pyrogenic emissions. A wide range of these fire and related products and validation are demonstrated for the wild fires that occurred in northwestern USA in Summer 2000. The MODIS rapid response system and direct broadcast capability is being developed to enable users to obtain and generate data in near real-time. It is expected that health and land management organizations will use these systems for monitoring the occurrence of fires and the dispersion of smoke within two to six hours after data acquisition.     

Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data

Understory fires in Amazon forests alter forest structure, species composition, and the likelihood of future disturbance. The annual extent of fire-damaged forest in Amazonia remains uncertain due to difficulties in separating burning from other types of forest damage in satellite data. We developed a new approach, the Burn Damage and Recovery (BDR) algorithm, to identify fire-related canopy damages using spatial and spectral information from multi-year time series of satellite data. The BDR approach identifies understory fires in intact and logged Amazon forests based on the reduction and recovery of live canopy cover in the years following fire damages and the size and shape of individual understory burn scars. The BDR algorithm was applied to time series of Landsat (1997-2004) and MODIS (2000-2005) data covering one Landsat scene (path/row 226/068) in southern Amazonia and the results were compared to field observations, image-derived burn scars, and independent data on selective logging and deforestation. Landsat resolution was essential for detection of burn scars < 50 ha, yet these small burns contributed only 12% of all burned forest detected during 1997-2002. MODIS data were suitable for mapping medium (50-500 ha) and large (> 500 ha) burn scars that accounted for the majority of all fire-damaged forests in this study. Therefore, moderate resolution satellite data may be suitable to provide estimates of the extent of fire-damaged Amazon forest at a regional scale. In the study region, Landsat-based understory fire damages in 1999 (1508 km²) were an order of magnitude higher than during the 1997-1998 El Niño event (124 km² and 39 km², respectively), suggesting a different link between climate and understory fires than previously reported for other Amazon regions. The results in this study illustrate the potential to address critical questions concerning climate and fire risk in Amazon forests by applying the BDR algorithm over larger areas and longer image time series.     

东北-内蒙古地区基于MODIS单、双卫星积雪数据及常规积雪观测结合的积雪日数研究

结合Terra和Aqua卫星的积雪产品,获取2001~2008年东北-内蒙古地区逐年积雪日数分布,并利用此数据对比Terra卫星积雪数据获取的逐年积雪日数。结果表明随海拔的升高,双星与单颗卫星积雪日数差异呈现明显增加的趋势。整个东北-内蒙古地区双星积雪日数平均高出单颗卫星积雪日15 d,但与台站积雪日数对比发现,双星积雪日数平均仍然偏低27 d。这说明,利用Terra和Aqua双卫星积雪监测数据能明显改善山区云对遥感监测的影响,同时也可以减少降雪初期和消融期由于积雪消融较快带来的积雪漏测,但不足以消除云等因素的影响。考虑到获取的2001~2006年台站年积雪日数与MODIS年积雪日数与有良好的统计关系,利用两者建立的线性统计关系修正整个东北-内蒙古地区的MODIS积雪日数,能够很好地消除云等因素带来的MODIS双卫星积雪日数偏小的问题,修正后台站与双星积雪日数之间的绝对误差由原来的27 d减小到18 d。