Utilization of Resourcesat-1 data for improved crop discrimination

Subsequent to the launch of the state-of-art third generation Indian Remote Sensing satellite, Resourcesat-1, studies have been conducted to understand the capabilities of the on-board sensors for crop discrimination. The paper discusses the unique capabilities of the AWiFS, LISS-III and LISS-IV sensors in terms of their dimensionality, radiometry and spatial resolutions for crop discrimination and monitoring. The studies have indicated better crop discriminability especially using the short wave infrared data in 1.55–1.70 μm data among the spectrally confusing land cover classes, attributed to the relative differences of water contents. 10-bit radiometry of AWiFS data in four bands has been observed to be a better discriminant. Intrafield variability was very well captured by the LISS-IV data revealing the potential of data for applications like precision farming. The studies have revealed that potential of Resourcesat-1 data becoming the workhorse for several agricultural applications.     

Geometric and radiometric evaluation of Resouresat-1 sensors

Resourcesat-1, launched in October 2003, is the 10th in the series of Indian Remote Sensing satellites built by the Indian Space Research Organization. Resourcesat-1, also known as IRS-P6, provides continuity to applications developed using data from IRS-1C and IRS-1D satellites. It also offers newer applications owing to enhanced capabilities of the sensors. The satellite contains three different imaging sensors: LISS-IV, with a ground sampling distance (GSD) of 5.8 m; LISS-III, with a GSD of 23.5 m; and AWiFS, with a GSD of 56 m at nadir. This paper provides data quality evaluation of the Resourcesat-1 sensors in terms of geometric and radiometric qualities. It is found that the sensors onboard Resourcesat-1 spacecraft has met all the mission set specifications and will help to generate data products with the required image geolocation and radiometric quality.     

A novel method for enhancement of radiometric resolution using image fusion

The Resourcesat-I satellite is equipped with different types of sensors with varied characteristics. For the effective utilization of the available multi-sensor, multi-temporal, multi-spectral and multi-radiometric data from these sensors, fusion of digital image data has become a valuable technique. Image fusion enhances the information content and helps in better discrimination of various land cover types. The Resourcesat-1 has equipped with three sensors, AWiFS, LISS-III and LISS-IV, which are having identical spectral resolutions, with different spatial, radiometric and temporal resolutions. The spatial resolutions ratio of the data set for merging are required to be maximum of 1:6, where as the data sets (AWiFS and LISS-III) that are used in the current study are having the ratio of 1:2.5 approximately. A novel merging technique is designed, which retains the multi-spectral response of the input data in the output data. The merged data set provides the higher spatial and radiometric resolutions. In order to evaluate the fusion merits quantitatively, all the data sets are digitally classified and studied the output classes for homogeneity and clear discrimination. A comprehensive comparative study is carried out between the fused image and the LISS-III image based on the contingency matrix and the scatter plots, which demonstrates the strength of fused image for discriminating the object classes at 23.5 m spatial and 10-bit radiometric resolutions. The merged data set gives the improved classification accuracy.     

Cotton Crop Discrimination Using Fuzzy Classification Approach

Crop growth information represented through temporal remote sensing data is of great importance for specific agriculture crop discrimination. In this paper, the effect of various indices was empirically investigated using temporal images for cotton crop discrimination. Five spectral indices SR (Simple Ratio), NDVI (Normalized Difference Vegetation index), TNDVI (Transformed Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and TVI (Triangular Vegetation Index) were investigated to identify cotton crop using temporal multi-spectral images. Data used for this study was AWIFS (coarser resolution) for soft classification and LISS-III (medium coarser) data for soft testing from Resourcesat-1 (IRS-P6) satellite. The mixed pixel (i.e. multiple classes within a single pixel) problem had been handled using soft computing techniques. Possibilistic fuzzy classification approach is used to handle mixed pixels for extracting single class of interest. The classification results with respect to various indices were compared in terms of image to image fuzzy overall classification accuracy. It was observed that temporal SAVI indices database with data set-2 outperformed other temporal indices database for cotton crop discrimination. Temporal SAVI indices database gave highest fuzzy overall accuracy of 93.12% with data set-2 in comparison to others.     

Mapping crop phenology using NDVI time-series derived from HJ-1 A/B data

With the availability of high frequent satellite data, crop phenology could be accurately mapped using time-series remote sensing data. Vegetation index time-series data derived from AVHRR, MODIS, and SPOT-VEGETATION images usually have coarse spatial resolution. Mapping crop phenology parameters using higher spatial resolution images (e.g., Landsat TM-like) is unprecedented. Recently launched HJ-1 A/B CCD sensors boarded on China Environment Satellite provided a feasible and ideal data source for the construction of high spatio-temporal resolution vegetation index time-series. This paper presented a comprehensive method to construct NDVI time-series dataset derived from HJ-1 A/B CCD and demonstrated its application in cropland areas. The procedures of time-series data construction included image preprocessing, signal filtering, and interpolation for daily NDVI images then the NDVI time-series could present a smooth and complete phenological cycle. To demonstrate its application, TIMESAT program was employed to extract phenology parameters of crop lands located in Guanzhong Plain, China. The small-scale test showed that the crop season start/end derived from HJ-1 A/B NDVI time-series was comparable with local agro-metrological observation. The methodology for reconstructing time-series remote sensing data had been proved feasible, though forgoing researches will improve this a lot in mapping crop phenology. Last but not least, further studies should be focused on field-data collection, smoothing method and phenology definitions using time-series remote sensing data.     

Accuracy assessment in cotton acreage estimation using Indian remote sensing satellite data

The accuracy of cotton crop classification using satellite data has been assessed with respect to a detailed land cover map prepared by field survey. The effect of spatial resolution on classification accuracy was studied using LISS-I (spatial resolution 72.6 m) and LISS-II data (spatial resolution 36.25 m) of the Indian remote sensing satellite IRS-1B. The performances of the maximum likelihood and the minimum distance to mean as classifiers have also been assessed. LISS-II data have been found to give a higher classification accuracy. The estimate of cotton acreage using LISS-II data was closer to that obtained from the base map. The maximum likelihood classifier (MXL) and the minimum distance to mean (MDM) classifier performed equally well.     

Geospatial modeling of biological richness in Kuldiha wildlife sanctuary of Orissa,India

The paper presents a geospatial modeling approach for the assessment of biological richness in Kuldiha wildlife sanctuary in Orissa located in the northern tip of the Eastern Ghats in India. Indian Remote Sensing satellite data of Resourcesat-1 LISS III and field sampling were used to model biological richness at 1:50,000 scale. It was found that the sanctuary is dominated by Sal mixed dry deciduous forest. The vegetation map prepared through visual interpretation of satellite imagery was subjected to landscape analysis and assessment of biotic disturbance using SPLAM software. The disturbance index together with species richness, ecosystem uniqueness, terrain complexity and total importance value was modeled to access the biological richness in the sanctuary. A total of 3.9 per cent area was found to posses very high plant richness followed by high (21.2%), medium (42.1%) and low (32.8%) in the sanctuary. The study demonstrated the geospatial technology in conjunction with landscape analysis, ground inventory and geospatial modeling seizes good potential for rapid assessment of biological richness. The fringe areas of the sanctuary having disturbance more because most of the small villages which are relocated from sanctuary, settled in those areas.     

Spectral characteristics of sensors onboard IRS-1D and P6 satellites: Estimation and their influence on surface reflectance and NDVI

This paper reports the results of a modeling study carried out with two objectives, (1) to estimate and compare effective spectral characteristics (central wavelength, bandwidth and bandpass exo-atmospheric solar irradiance E_o) of various spectral channels of LISS-III, WiFS, LISS-III*, LISS-IV and AWiFS onboard Indian Remote Sensing Satellites IRS-ID and P6 using moment method based on the laboratory measurements of sensor spectral response, and (2) to quantify the influence of varying sensor spectral response on reflectance and Normalized Difference Vegetation Index (NDVI) measurements using surface reflectance spectra corresponding to different leaf area index conditions of crop target obtained through field experiment. Significant deviation of 4 to 14 nm in central wavelength and 1.6 to 14.07 nm in spectral width was observed for the corresponding channel of IRS sensors. Coefficient of variation of the order of 0.1 to 1.11% was noticed in E_o among various IRS sensors, which could induce a difference of 0.72 to 3.35% in the estimation of top of atmosphere reflectance for crop target. The variation in spectral response of IRS sensors implied a relative difference of the order of 0.91 to 3.38% in surface reflectance and NDVI measurements. Polynomial approximations are also provided for spectral correction that can be utilized for normalizing the artifacts introduced due to differences in spectral characteristics among IRS sensors.     

Retrieval of wheat leaf area index from AWiFS multispectral data using canopy radiative transfer simulation

Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over two different wheat growing regions, situated in different agro-climatic settings/environments: Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simulation of canopy reflectances in four AWiFS bands viz. green (0.52–0.59 μm), red (0.62–0.68 μm), NIR (0.77–0.86 μm) and SWIR (1.55–1.70 μm) were carried out to generate the look up table (LUT) using CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive wheat growing seasons (November 2005–March 2006 and November 2006–March 2007) datasets were used in this study. The empirical models were developed from first season data and second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R² of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise.     

Phenological metrics-based crop classification using HJ-1 CCD images and Landsat 8 imagery

Crop type data are an important piece of information for many applications in agriculture. Extracting crop type using remote sensing is not easy because multiple crops are usually planted into small parcels with limited availability of satellite images due to weather conditions. In this research, we aim at producing crop maps for areas with abundant rainfall and small-sized parcels by making full use of Landsat 8 and HJ-1 charge-coupled device (CCD) data. We masked out non-vegetation areas by using Landsat 8 images and then extracted a crop map from a long-term time-series of HJ-1 CCD satellite images acquired at 30-m spatial resolution and two-day temporal resolution. To increase accuracy, four key phenological metrics of crops were extracted from time-series Normalized Difference Vegetation Index curves plotted from the HJ-1 CCD images. These phenological metrics were used to further identify each of the crop types with less, but easier to access, ancillary field survey data. We used crop area data from the Jingzhou statistical yearbook and 5.8-m spatial resolution ZY-3 satellite images to perform an accuracy assessment. The results show that our classification accuracy was 92% when compared with the highly accurate but limited ZY-3 images and matched up to 80% to the statistical crop areas.     

Remote sensing based agricultural drought assessment in Palar basin of Tamil Nadu state,India

Agricultural drought has been a recurrent phenomenon in many parts of India. Remote sensing plays a vital role in real time monitoring of the agricultural drought conditions over large area, there by effectively supplementing the ground mechanism. Conventional drought monitoring is based on subjective data. The satellite based monitoring such as National Agricultural Drought Assessment and Monitoring System (NADAMS) is based on the crop condition, which is an integrated effect of soil, effective rainfall, weather, etc. Drought causes changes in the external appearance of vegetation, which can clearly be identified (by their changed spectral response) and judged using satellite sensors through the use of vegetation indices. These indices are functions of rate of growth of the plants and are sensitive to the changes of moisture stress in vegetation. The satellite based drought assessment methodology was developed based on relationship obtained between previous year’s Normalised Difference Vegetation Index (NDVI) profiles with corresponding agricultural performance available at district/block level. Palar basin, one of the major river basins in Tamil Nadu state was selected as the study area. The basin covers 3 districts, which contain 44 blocks. Wide Image Field Sensor (WiFS) of 188m spatial resolution from Indian Remote Sensing Satellite (IRS) data was used for the analysis. Satellite based vegetation index NDVI, was generated for Samba and Navarai seasons in the years 1998 and 1999. An attempt has been made to estimate the area under paddy. It was also observed that, there was reduction in the crop area as well as vigour in the vegetation in both Samba and Navarai seasons in 1999 when compared with 1998. Drought severity maps were prepared in GIS environment giving blockwise agricultural water deficiency status.     

Evaluation of NDWI and MNDWI for assessment of waterlogging by integrating digital elevation model and groundwater level

Accurate information on the extent of waterlogging is required for flood prediction, monitoring, relief and preventive measures. The rule-based classification algorithms were used for differentiating waterlogged areas from other ground features using Resourcesat-2 AWiFS satellite imagery (Indian Remote Sensing Satellite with spatial resolution of 56 m). Two spectral indices normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used for extracting waterlogged areas in Sri Muktsar Sahib district of Punjab, India. These indices extracted the waterlogged areas (cropped areas inundated with water) but the water features were less enhanced in the NDWI-derived image (when compared with MNDWI-derived image) due to negative values of NDWI and, mixing of water with built up features. The water features were more enhanced with MNDWI and the values of MNDWI were positive for water features mixed with vegetation. The overall accuracy of waterlogged areas extracted from the MNDWI image was 96.9% with the Kappa coefficient of 0.89. The digital elevation model (DEM) was extracted from ASTER-GDEM. The relationships among depth to the water table recorded before the incessant rain in the region, DEM and classified MNDWI images explained the differences in the extent of waterlogging in various directions of the study area. These results suggest that MNDWI can be used to better delineate water features mixed with vegetation compared to NDWI.     

Methodology for estimating availability of cloud-free image composites: A case study for southern Canada

Image composites are often used for earth surface phenomena studies at regional or national level. The compromise between residual clouds and the length of compositing period is a necessary corollary to the choice of satellite optical data for monitoring earth surface phenomena dynamics. This paper introduced a methodology for estimating availability of cloud-free image composites for optical sensors with various revisiting intervals, using MODIS MOD06 L2 cloud fraction product in the period of 2000–2008. The methodology starts with downscaling of the cloud fraction product to 1 km × 1 km cloud cover binary images. The binary images are then used for the exploration of spatial and temporal characteristics of cloud dynamics, and subsequently for the simulation of cloud-free composite availability with various revisiting intervals of optical sensors. Using Canada's southern provinces as an application case, the study explored several factors important for the design of environmental monitoring system using optical sensors of earth observation, in particular, cloud dynamics and its inter-annual variability, sensors’ revisiting intervals, and cloud-free threshold for targeting composites. While the cloud images used in the analysis are at 1 km × 1 km resolution, our analysis suggests that the simulated availabilities of cloud-free image composites may also provide reasonable estimates for optical sensors with higher than 1 km × 1 km resolution, though the closer to 1 km × 1 km resolution the optical sensor, the more pertinent the application. Also, the methodology can be parameterised to different temporal period and different spatial region, depending on applications.     

Climate Change Studies Using Space Based Observation

Climate change is associated with earth radiation budget that depends upon incoming solar radiation, surface albedo and radiative forcing by greenhouse gases. Human activities are contributing to climate change by causing changes in Earth’s atmosphere (greenhouse gases, aerosols) and biosphere (deforestation, urbanization, irrigation). Long term and precise measurements from calibrated global observation constellation is a vital component in climate system modelling. Space based records of biosphere, cryosphere, hydrosphere and atmosphere over more than three decades are providing important information on climate change. Space observations are an important source of climate variables due to multi scale simultaneous observation (local, regional, and global scales) capability with temporal revisit in tune with requirements of land, ocean and atmospheric processes. Essential climatic variables that can be measured from space include atmosphere (upper air temperature, water vapour, precipitation, clouds, aerosols, GHGs etc.), ocean (sea ice, sea level, SST, salinity, ocean colour etc.) and land (snow, glacier, albedo, biomass, LAI/fAPAR, soil moisture etc.). India’s Earth Observation Programme addresses various aspects of land, ocean and atmospheric applications. The present and planned missions such as Resourcesat-1, Oceansat-2, RISAT, Megha-Tropiques, INSAT-3D, SARAL, Resourcesat-2, Geo-HR Imager and series of Environmental satellites (I-STAG) would help in understanding the issues related to climate changes. The paper reviews observational needs, space observation systems and studies that have been carried out at ISRO (Indian Space Research Organization) towards mapping/detecting the indicators of climate change, monitoring the agents of climate change and understanding the impact of climate change, in national perspectives. Studies to assess glacier retreat, changes in polar ice cover, timberline change and coral bleaching are being carried out towards monitoring of climate change indicators. Spatial methane inventories from paddy rice, livestock and wetlands have been prepared and seasonal pattern of CO₂, and CO have been analysed. Future challenges in space observations include design and placement of adequate and accurate multi-platform observational systems to monitor all parameters related to various interaction processes and generation of long term calibrated climate data records pertaining to land ocean and atmosphere.     

Evaluation of fuzzy-based classifiers for cotton crop identification

In this study, an evaluation of fuzzy-based classifiers for specific crop identification using multi-spectral temporal data spanning over one growing season has been carried out. The temporal data sets have been georeferenced with 0.3 pixel rms error. Temporal information of cotton crop has been incorporated through the following five indices: simple ratio (SR), normalized difference vegetation index (NDVI), transformed normalized difference vegetation index (TNDVI), soil-adjusted vegetation index (SAVI) and triangular vegetation index (TVI), to study the effect of indices on classified output. For this purpose, a comparative study between two fuzzy-based soft classification approaches, possibilistic c-means (PCM) and noise classifier (NC), was undertaken. In this study, advanced wide field sensor (AWiFS) data for soft classification and linear imaging self scanner sensor (LISS III) data for soft testing purpose from Resourcesat-1 (IRS-P6) satellite were used. It has been observed that NC fuzzy classifier using TNDVI temporal index – dataset 2, which comprises four temporal images performs better than PCM classifier giving highest fuzzy overall accuracy of 96.03%.     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Updated Inventory of Glacial Lakes in Teesta Basin Using Remote Sensing Data for Use in GLOF Risk Assessment

Detailed inventory of glacial lakes in a Glacial Lake Outburst Flood (GLOF) prone area is vital for disaster mitigation. Availability of cheaper high resolution satellite data from Indian remote sensing satellites enables us to create up-to-date inventory for use in prioritisation of glacial lakes for GLOF risk assessment. Earlier inventories show presence of more glacial lakes in Brahmaputra basin in Indian Himalaya. Teesta River is one of the tributary of Brahmaputra and previous studies have shown that the inventory of glacial lakes in Teesta basin varies from 143 to 320. In the present study, the inventory carried out using satellite data of years 2000, 2007 and 2014 show presence of 301 (25.789 km²), 302 (26.081 km²) and 644 (29.706 km²) glacial lakes in Teesta basin respectively. The steep increase in number of lakes in the latest inventory is primarily due to the finer spatial resolution of satellite data used. Analysis of water spread area of glacial lakes at different altitudes shows that most of the lakes in the higher altitudes are small in size. It is observed that more than 66% of lakes are in the altitude beyond 4500 m and of size less than 50,000 sqm (5 ha). Out of 301 glacial lakes inventoried during 2000, water spread area of 6 lakes have decreased in 2014 and 31 lakes have shown increase in area. Out of these 31 lakes, 17 lakes are classified as end moraine dammed lakes and among them, 14 are located in Upper Teesta sub-basin and in higher altitudes (beyond 5000 m). The prioritisation of these lakes for GLOF risk needs to be carried out with detailed field investigation.     

利用时空信息的单波段地表温度遥感反演

地表温度被认为是影响生态系统的关键因子之一,它与许多地表过程有关。目前,热红外卫星遥感技术是获取有关区域和全球尺度地表温度信息的一个有效、可行的手段。针对不同卫星上搭载的热红外传感器,许多学者开展了大量的研究,其中针对单波段热红外的特点(如Landsat TM/ETM+,CBERS和HJ-1B)提出了单通道(或单窗)算法。该类算法需要准确的地表比辐射率和大气参数(如大气水分含量)。这些参数在现实中又很难轻易获得,从而在一定程度上限制了现有算法的应用。针对HJ-1B高回访频率的特点,本文提出了利用多时相影像的时空信息来直接反演地表温度的Multi-Temporal and Spatial Information-Based Single Channel(MTSC),以解决现有算法对地表比辐射率和大气参数的过度依赖性。实例分析结果显示,基于MTSC法由HJ-1B反演得到的地表温度结果与MODIS地表(陆表和海表)温度产品具有很好的空间一致性;HJ-1B的陆表温度结果总体上被高估了约1 K,而海表温度结果总体上被高估了0.5 K;同时,MTSC法得到的HJ-1B地表温度结果具有更好的细节和空间完整性。最后,通过分析和讨论指出了一些可能的完善途径,如相似像元的确定、修改优化求解中的目标函数、参数的自适应初始化等,以便提高MTSC法的反演精度和实用性。     

An enhanced spatiotemporal fusion method – Implications for coal fire monitoring using satellite imagery

Many real-world applications require remotely sensed images at both high spatial and temporal resolutions. This requirement, however, is generally not met by single satellite system. A number of spatiotemporal fusion models have been developed to overcome this constraint. Landsat and Visible Infrared Imaging Radiometer Suite (VIIRS) data have been extensively used for detection and monitoring of active fires at different scales. Fusing the data obtained from these sensors will, therefore, significantly contribute to the satellite-based monitoring of fires. Among the available spatiotemporal fusion methods, the spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) algorithms have been widely used for studying the land surface dynamics in the homogeneous and heterogeneous regions. The present study explores the applicability of STARFM and ESTARFM algorithms for fusing the high spatial resolution Landsat-8 OLI data with high temporal resolution VIIRS data in the context of active surface coal fire monitoring. Further, a modified version of ESTARFM algorithm, referred as modified-ESTARFM, is developed to improve the performance of the fusion model. Jharia coalfield (India), known for widespread occurrences of coal fires, is taken as the study area. The qualitative and quantitative assessments of the predicted (synthetic) Landsat-like images from different algorithms (STARFM, modified-STARFM, ESTARFM, modified-ESTARFM) indicate that the modified-ESTARFM outperforms the other fusion approaches used in this study. Considering the advantages, limitations and performance of the algorithms used, modified-ESTARFM along with STARFM can be used for surface coal fire monitoring. The study will not only contribute to remote sensing based coal fire studies but also to other applications, such as forest fires, crop residue burning, land cover and land use change, vegetation phenology, etc.     

Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Land-surface temperature (LST) is of great significance for the estimation of radiation and energy budgets associated with land-surface processes. However, the available satellite LST products have either low spatial resolution or low temporal resolution, which constrains their potential applications. This paper proposes a spatiotemporal fusion method for retrieving LST at high spatial and temporal resolutions. One important characteristic of the proposed method is the consideration of the sensor observation differences between different land-cover types. The other main contribution is that the spatial correlations between different pixels are effectively considered by the use of a variation-based model. The method was tested and assessed quantitatively using the different sensors of Landsat TM/ETM+, moderate resolution imaging spectroradiometer and the geostationary operational environmental satellite imager. The validation results indicate that the proposed multisensor fusion method is accurate to about 2.5 K.