Evaluating the calibration of MTSAT‐1R infrared channels using collocated Terra MODIS measurements

The calibration of four MTSAT‐1R infrared channels was evaluated by comparing MTSAT measurements with Terra/MODIS inferred MTSAT‐equivalent brightness temperatures during August 2005 and August 2006. Theoretical relationships converting MODIS brightness temperatures to MTSAT‐equivalent values were obtained and used for the comparison. Results indicate that MTSAT two split window channels are well calibrated, and no serious systematic errors or biases are found; and the MTSAT water‐vapour channel shows a good linear relationship but with a warm bias up to 2 K. The significant cold bias of MTSAT 3.7 µm channel about ?6.7 K in August 2005 is found to be much removed in August 2006, after correction of the electrical crosstalk between MTSAT‐1R SWIR channel and WV channel starting from March 2006. Since then, calibration performances of MTSAT‐1R split window channels and shortwave IR channel seem to be comparable with MODIS calibration, while the water‐vapour channel shows more uncertainties up to 2 K of bias.     

Validation of active forest fires detected by MSG‐SEVIRI by means of MODIS hot spots and AWiFS images

The detection of forest fires and the determination of their parameters have been usually carried out by polar‐orbit sensors: AVHRR, (A)ATSR, BIRD, and MODIS mainly. However, their time resolution prevents them from operating in real time. In contrast, the new geostationary sensors have very appropriate capacities for the observation of the Earth and monitoring of forest fires, as is being proved. GOES, MSG, and MTSAT are already operative, and they have led the international community to think that the global observation network in real time may become a reality. The implementation of this network is the aim of the Global Observations of Forest Cover and Land Cover Dynamics (GOFC/GOLD) FIRE Mapping and Monitoring programme, focused internationally on taking decisions concerning the research of the Global Change. In this Letter, the operation in real time by the MSG‐SEVIRI sensor over the Iberian Peninsula is studied. On the other hand, the reliability of validation results by means of polar sensors, with a finer spatial resolution, is difficult to analyse due to errors caused by confused location of fires. This Letter shows that fires detected by means of MSG‐SEVIRI can be an useful option in order to estimate burnt areas at global scale, considering a spatial resolution of 40 km.     

Split‐window algorithm for land surface temperature estimation from MSG1‐SEVIRI data

This letter addresses the land surface temperature (LST) estimation from the data acquired by the spinning enhanced visible and infra‐red imager (SEVIRI) on board the first geostationary satellite meteosat second generation (MSG1) using the generalized split‐window algorithm proposed by Wan and Dozier (1996 Wan, Z. and Dozier, J. 1996. A generalized split‐window algorithm for retrieving land‐surface temperature from space.. IEEE Transactions on Geoscience and Remote Sensing, 34: 892–905. [Crossref], [Web of Science ®] , [Google Scholar]). The generalized split‐window algorithm was developed for eight view zenith angles (VZAs) by dividing the LST, the average emissivity (ε) and the column water vapour (W) into several sub‐ranges to improve the LST estimating accuracy. The simulated results show that the root mean square errors (RMSEs) increase with VZAs and W, and they are less than 1.0 K for all sub‐ranges with the VZA less than 45°, or for the sub‐ranges with VZA less than 60° and W less than 3.5 cm. The land surface emissivities (LSEs) and W used in the generalized split‐window algorithm were estimated from MSG1‐SEVIRI data by the method developed by us in previous studies. The results at the four specific locations show that the LSEs were well derived, and the LSTs estimated from MSG1‐SEVIRI data are basically consistent with the ones extracted from MODIS/Terra LST products.     

Tracking the calibration stability and consistency of the 3.7, 11.0 and 12.0 μm channels of the NOAA-KLM AVHRR with MODIS

The NOAA-KLM satellites (NOAA-15 to 18) are the current polar-orbiting operational environmental satellites (POES) that carry the Advanced Very High Resolution Radiometer (AVHRR). This study examines the calibration stability and consistency of all three infrared channels (3.7, 11.0 and 12.0 μm) of AVHRR onboard NOAA-15 to 18. The short-term stability is examined from variations of the scan-by-scan gain response, while the long-term stability and calibration consistency are examined by tracking the trends of gain response and measured scene brightness temperatures. The relative differences of observed scene brightness temperatures among NOAA-15 to 18 AVHRR are determined using MODIS as a transfer radiometer based on observations from simultaneous nadir overpasses (SNO). Results show that variations of the scan-to-scan gain responses are within 0.10% under normal operational conditions, while long-term gain changes over six years from 2001 to 2006 vary from 2 to 4% depending on channel. Long-term trending results show that total six-year drifts in observed brightness temperature from NOAA-15 to 18 AVHRR are less than 0.5 K for a given scene temperature in the 250 to 270 K range for the 3.7, 11.0 and 12.0 μm channels, respectively. The calibration consistency is examined for a scene temperature range of 220 to 290 K. The temperature biases among NOAA-16 to 18 AVHRR are within ±0.5 K for the 11.0 and 12.0 μm channels. For NOAA-15 AVHRR, biases of –2.0 K at 11.0 μm and –1.5 K at 12.0 μm are found in comparison with others at the low end of the temperature range. For the 3.7 μm channel, relative biases up to a few degrees among NOAA-15 to 18 could be found at low brightness temperatures.     

Inferences of all‐sky solar irradiance using Terra and Aqua MODIS satellite data

Solar irradiance is a key environmental control, and accurate spatial and temporal solar irradiance data are important for a wide range of applications related to energy and carbon cycling, weather prediction, and climate change. This study presents a satellite‐based scheme for the retrieval of all‐sky solar irradiance components, which links a physically based clear‐sky model with a neural network version of a rigorous radiative transfer model. The scheme exploits the improved cloud characterization and retrieval capabilities of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and employs a cloud motion tracking scheme for the production of hourly solar irradiance data throughout the day. The scheme was implemented for the Island of Zealand, Denmark (56° N, 12° E) and Southern Arizona, USA (31° N, 110° W) permitting model evaluation for two highly contrasting climates and cloud environments. Information on the atmospheric state was provided by MODIS data products and verifications against AErosol RObotic NETwork (AERONET) data demonstrated usefulness of MODIS aerosol optical depth and total precipitable water vapour retrievals for the delineation of spatial gradients. However, aerosol retrievals were significantly biased for the semi‐arid region, and water‐vapour retrievals were characterized by systematic deviations from the measurements. Hourly global solar irradiance data were retrieved with overall root mean square deviations of 11.5% (60 W m^?2) and 26.6% (72 W m^?2) for Southern Arizona and the Island of Zealand, respectively. For both regions, hourly satellite estimates were shown to be more reliable than pyranometer measurements from ground stations only 15 km away from the point of interest, which is comparable to the accuracy level obtainable from geostationary satellites with image acquisitions every 15–30 min. The proposed scheme is particularly useful for solar irradiance mapping in high‐latitude regions as data from geostationary satellites experience a gradual degradation in spatial resolution and overall quality with latitude and become unusable above approximately 60° latitude. However, in principle, the scheme can be applied anywhere on the globe, and a synergistic use of MODIS and geostationary satellite datasets may be envisaged for some applications.     

First algorithm for chlorophyll‐a retrieval from MODIS‐Terra imagery of Sun‐induced fluorescence in the Southern Ocean

The measurements of in situ samplers, the ENEA Light Detection and Ranging (Lidar) Fluorosensor (ELF) and Moderate Resolution Imaging Spectroradiometer on‐board the Terra satellite (MODIS‐Terra), carried out in the Southern Ocean during the Austral summer 2002–2003, were used to provide the first algorithm for chlorophyll‐a (Chl‐a) retrieval from MODIS‐Terra imagery of Sun‐induced fluorescence in the Southern Ocean. The results of the algorithm indicate that the standard MODIS‐Terra algorithm underestimated Chl‐a. The discrepancy (20%) is below the expected error of MODIS (35%).     

Terra and Aqua MODIS inter‐comparison of three reflective solar bands using AVHRR onboard the NOAA‐KLM satellites

Cross‐sensor inter‐comparison is important to assess calibration quality and consistency and ensure continuity of observational datasets. This study conducts an inter‐comparison of Terra and Aqua MODIS (the MODerate Resolution Imaging Spectroradiometer) to examine the overall calibration consistency of the reflective solar bands. Observations obtained from AVHRR (the Advanced Very High Resolution Radiometer) onboard the NOAA‐KLM series of satellites are used as a transfer radiometer to examine three MODIS bands at 0.65 (visible), 0.85 (near‐IR) and 1.64 µm (far near‐IR) that match spectrally with AVHRR channels. Coincident events are sampled at a frequency of about once per month with each containing at least 3000 pixel‐by‐pixel matched data points. Multiple AVHRR sensors on‐board NOAA‐15 to 18 satellites are used to check the repeatability of the Terra/Aqua MODIS inter‐comparison results. The same approach applied in previous studies is used with defined criteria to generate coincident and co‐located near nadir MODIS and AVHRR pixel pairs matched in footprint. Terra and Aqua MODIS to AVHRR reflectance ratios are derived from matched pixel pairs with the same AVHRR used as a transfer radiometer. The ratio differences between Terra and Aqua MODIS/AVHRR give an indication of the calibration biases between the two MODIS instruments. Effects due to pixel footprint mismatch, band spectral differences and surface and atmospheric bi‐directional reflectance distributions (BRDFs) are discussed. Trending results from 2002 to 2006 show that Terra and Aqua MODIS reflectances agree with each other within 2% for the three reflective solar bands.     

Simultaneous non‐linear retrieval of atmospheric profiles and surface skin temperature from MODIS infrared radiances in Taiwan Strait

A non‐linear iterative physical algorithm that simultaneously retrieves atmospheric temperature, water vapour distribution and surface skin temperature from Moderate Resolution Imaging Spectroradiometer (MODIS) longwave infrared radiances is presented. The retrieval algorithm uses clear‐sky radiances measured by MODIS in Taiwan Strait for both day and night, and shows that it is capable of retrieving medium‐scale atmospheric temperature and water vapour. Sea surface temperature is retrieved with an accuracy similar to that achieved by MOD07 products. Evaluation of retrieval total precipitable water vapour (TPW) is performed by comparisons with retrievals from MOD07 products and data from a ground‐based sunphotometer. These show that MODIS retrieval of TPW, in general, agrees with other sounder retrievals of TPW. The total totals index (TTI) distribution retrieved from MODIS data is similar to that from MOD07 products.     

Fast over‐land atmospheric correction of visible and near‐infrared satellite images

A rapid atmospheric correction method is proposed to be used for visible and near‐infrared satellite sensor images over land. The method is based on a simplified use of a radiative transfer code (RTC), which is used only a priori, to generate Look‐Up‐Tables (LUTs) of the estimated surface reflectance. A typical scenario and ranges of values for the main atmospheric correction parameters are initially established. Each image pixel is treated as a slight deviation from the reference scenario defined by the vector of the typical values for the parameters. The assumption of the parameter's independence allows the use of one‐dimensional LUTs. The method is suitable for near real‐time processing or whenever a large number of data are to be handled rapidly. The operator intervention is minimal, and the computation time involved in the correction of a whole image is about 1000 times shorter than the full use of the base RTC. A test is performed with advanced very‐high‐resolution radiometer (AVHRR) visible and near‐infrared data, using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) RTC as the base code. The accuracy of the proposed method was compared with the standard use of the 6S RTC over the same dataset with resulting root mean square errors of 0.0114 and 0.0104 for AVHRR bands 1 and 2 for the estimated surface reflectance, respectively.     

Near‐infrared discrimination of leafless saltcedar in wintertime Landsat TM

To test a hypothesis that leafless riparian canopies enable accurate multi‐spectral discrimination of saltcedar (Tamarix ramosissima Ledeb.) from other native species, winter Landsat TM5 data (16 November 2005) were analysed for a reach of the Arkansas River in Colorado, USA. Supporting spectroscopic analysis confirmed that saltcedar could not easily be discriminated from other riparian vegetation using TM5 data when in‐leaf, but bare branches could be easily distinguished due to much lower reflectance than other riparian cover. Use of TM Band 4 (B4) allowed differentiation of wintertime saltcedar into four qualitative density classes judged from high‐resolution low‐oblique aerial photography: high (76%–100%), medium (51%–75%), low (16%–50%), and none (0%–15%). Spectral overlap was removed from the B4 saltcedar classification using TM Band 5 (B5) thresholds to eliminate low‐reflectant wet areas and higher‐reflectant multi‐year darkened weed canopies. The accuracy of a classification algorithm that used B5 thresholds followed by a B4 density slice was judged against high‐resolution aerial photography as providing 98% discrimination of saltcedar cover from other riparian cover and about 90% discrimination of the qualitative density classes. Applying this method to the 2835 km² riparian corridor study area, 1298 km² (45.78%) was identified as containing saltcedar, with over 43% having medium or greater density.     

基于Terra/MODIS数据的HJ-1B/CCD1交叉定标方法研究

交叉辐射定标是国际上新近发展起来的一种无场地定标方法,它的应用弥补了场地定标成本较高、定标参数更新周期较长的不足。对于我国2008年发射的环境与灾害监测预报小卫星CCD数据而言,探索交叉辐射定标方法的适用性,对及时发现传感器辐射性能的变化,促进CCD遥感数据的定量化应用具有重要意义。本研究以辐射定标精度较高的Terra/MODIS数据为参考,分别使用光线匹配法（RM）和辐射传输模型方法（RTM）对HJ-1B/CCD1数据进行交叉辐射定标,并与相同条件下进行的场地定标结果比较。实验结果表明,使用这两种方法获取的CCD1的第2、3、4波段的定标结果与场地定标结果差异较小,只有第1波段定标结果与场地定标结果差异相对较大,这证明了交叉辐射定标方法的有效性。另外,虽然RTM方法考虑了参考传感器和待定标传感器光谱响应和观测几何的差异,但是由于RTM方法会受到所使用的6S模型本身的误差以及输入的大气参数、地表参数测量误差的影响,该方法并不总是优于RM方法。     

Closed‐loop control for self‐calibration of accelerometer achieved through integrated sensor and actuator system

Microsystem Technologies - In-situ self-calibration can fundamentally solve the problem of long-term stability of the accelerometer. The implementation of external integrated micro structure to...     

Multi‐temporal analysis of MODIS data to classify sugarcane crop

This paper presents a feasibility study using multi‐temporal Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data to classify sugarcane crop. This study was carried out in São Paulo State which is the major sugarcane producer in Brazil, occupying more than 3.1 million hectares. Cloud‐free MODIS images (16 days mosaics) were acquired over a period of almost 15 months. Samples of sugarcane and non‐sugarcane were randomly selected and cluster analysis was performed to establish similar EVI temporal behaviour clusters. It was observed that EVI was sensitive to variations in land‐use cover mainly due to phenology and land management practices. Therefore, selection of sugarcane samples with similar EVI temporal behaviour for supervised classification was difficult due to both large planting and large harvesting periods. Consequently, cluster analysis was chosen to carry out an unsupervised classification. The best results were obtained in regions occupied by: natural and planted forest, soybean, peanuts, water bodies and urban areas which contrasted with the temporal‐spectral behaviour of sugarcane. The lowest performance was observed mainly in regions dominated by pasture, which has similar temporal‐spectral behaviour to sugarcane. This study provided useful information to define a MODIS image classification procedure for sugarcane crop for the whole State area based on the large amount of cloud‐free MODIS images when compared with other currently available optical sensors.     

WATeRS: an open Web Map Service with near‐real time MODIS Level‐2 standard chlorophyll products of the North Sea

WATeRS (http://ivm10.ivm.vu.nl/mapserver/WATeRS) is a portal for near‐real time (NRT) satellite‐derived water quality (chlorophyll) information products that is openly and interactively available for all on the Internet. It is based on automated conversion of remotely sensed data (in scientific formats) to geographic information system (GIS) formats, and comprises a customized Arc Internet Map Server (ArcIMS) application with an OpenGIS compliant Web Map Server connector. The resulting GIS‐based open map service comprises a simple, clear and intuitive user interface, grid‐cell query functionality, and is complemented by a metadata catalogue that provides full lineage of the chlorophyll maps, and automated archiving. WATeRS enables users to interactively explore remote sensing products, and to seamlessly combine this with other geographical data.     

Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in a semi‐arid environment

Much effort has been made in recent years to improve the spectral and spatial resolution of satellite sensors to develop improved vegetation indices reflecting surface conditions. In this study satellite vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR) are evaluated against two years of in situ measurements of vegetation indices in Senegal. The in situ measurements are obtained using four masts equipped with self‐registrating multispectral radiometers designed for the same wavelengths as the satellite sensor channels. In situ measurements of the MODIS Normalized Difference Vegetation Index (NDVI) and AVHRR NDVI are equally sensitive to vegetation; however, the MODIS NDVI is consistently higher than the AVHRR NDVI. The MODIS Enhanced Vegetation Index (EVI) proved more sensitive to dense vegetation than both AVHRR NDVI and MODIS NDVI. EVI and NDVI based on the MODIS 16‐day constrained view angle maximum value composite (CV‐MVC) product captured the seasonal dynamics of the field observations satisfactorily but a standard 16‐day MVC product estimated from the daily MODIS surface reflectance data without view angle constraints yielded higher correlations between the satellite indices and field measurements (R ² values ranging from 0.74 to 0.98). The standard MVC regressions furthermore approach a 1?:?1 line with in situ measured values compared to the CV‐MVC regressions. The 16‐day MVC AVHRR data did not satisfactorily reflect the variation in the in situ data. Seasonal variation in the in situ measurements is captured reasonably with R ² values of 0.75 in 2001 and 0.64 in 2002, but the dynamic range of the AVHRR satellite data is very low—about a third to a half of the values from in situ measurements. Consequently the in situ vegetation indices were emulated much better by the MODIS indices than by the AVHRR NDVI.     

Retrieval of total suspended matter concentration from Gaofen-1 Wide Field Imager (WFI) multispectral imagery with the assistance of Terra MODIS in turbid water – case in Deep Bay

This study proposed a method for developing high spatial resolution Gaofen-1 satellite (GF-1) Wide Field Imager (WFI)-based total suspended matter concentration (C_TSM) retrieval model with the assistance of Moderate Resolution Imaging Spectroradiometer (MODIS) data, using the Deep Bay in China as a case. Based on long-term calibrated C_TSM measurements of optical backscatter (OBS) 3A turbidity and temperature monitoring system of two stationary stations from January 2007 through November 2008, 33 match-ups were selected to build an exponential retrieval model for MODIS atmospherically corrected remote-sensing reflectance (R_rs) ratio (R_rs,645/R_rs,555). Validation of the MODIS model showed well agreement with the seven in situ C_TSM measurements with a root mean squared error (RMSE) of 5.06 mg l^?1 and a coefficient of determination R² of 0.80. Aided with six MODIS retrieved C_TSM products, different band combinations (single band (R_rc,660), band subtraction (R_rc,660–R_rc,560), band ratio (R_rc,660/R_rc,560), and total suspended matter index at 660 nm band (TSMI₆₆₀) were evaluated for simultaneous GF-1 WFI Rayleigh-corrected reflectance (R_rc). The results showed that the exponential model based on the Rayleigh-corrected reflectance ratio (R_rc,660/R_rc,560) could achieve acceptable accuracy, with RMSE of 14.80 mg l^?1 and R² of 0.62. The proposed method would be helpful for dynamic monitoring in the Deep Bay, and more important could also provide an alternative approach for studies when in situ measurements are unreachable.     

Differentiation of semi‐arid vegetation types based on multi‐angular observations from MISR and MODIS

Mapping accurately vegetation type is one of the main challenges for monitoring arid and semi‐arid grasslands with remote sensing. The multi‐angle approach has been demonstrated to be useful for mapping vegetation types in deserts. The current paper presents a study on the use of directional reflectance derived from two sensor systems, using two different models to analyse the data and two different classifiers as a means of mapping vegetation types. The multiangle imaging spectroradiometer (MISR) and the moderate resolution imaging spectroradiometer (MODIS) provide multi‐spectral and angular, off‐nadir observations. In this study, we demonstrate that reflectance from MISR observations and reflectance anisotropy patterns derived from MODIS observations are capable of working together to increase classification accuracy. The patterns are described by parameters of the modified Rahman–Pinty–Verstraete and the RossThin‐LiSparseMODIS bidirectional reflectance distribution function (BRDF) models. The anisotropy patterns derived from MODIS observations are highly complementary to reflectance derived from radiances observed by MISR. Support vector machine algorithms exploit the information carried by the same data sets more effectively than the maximum likelihood classifier.     

Variability of fire‐induced changes in MODIS surface reflectance by land‐cover type in Borneo

This study investigates fire‐induced spectral changes detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) in different land‐cover types in Borneo. Linear discriminant analysis is used to determine the most powerful band combinations among the MODIS reflective bands for discrimination between burnt and unburnt areas in each land‐cover type. The results show that the nature of fire‐induced changes is dependent on pre‐fire vegetation characteristics in this region. Bands 1 (0.64 µm), 2 (0.86 µm), and 7 (2.14 µm) are found to be the most sensitive bands in land‐cover types dominated by green vegetation, and consequently indices or combinations of indices using these three bands are potentially effective for burnt‐area detection in the majority of areas. In land‐cover types dominated by dry vegetation and soil, MODIS band 5 (1.24 µm) alone showed the greatest statistical separability and could not be significantly improved by any multiband index.     

The assessment of leaf water content using leaf reflectance ratios in the visible,near‐, and short‐wave‐infrared

The common features of spectral reflectance from vegetation foliage upon leaf dehydration are decreasing water absorption troughs in the near‐infrared (NIR) and short‐wave‐infrared (SWIR). We studied which leaf water index in the NIR and SWIR is most suitable for the assessment of leaf water content and the detection of leaf dehydration from the laboratory standpoint. We also examined the influence of the thickness of leaves upon leaf water indices. All leaf water content indices examined exhibited basic correlations with the relative water content (RWC) of leaves, while the R ₁₃₀₀/R ₁₄₅₀ leaf water index also demonstrated a high signal strength and low variability (R ²>0.94). All examined leaf reflectance ratios could also be correlated with leaf thickness. The thickness of leaves, however, was not independent of leaf RWC but appeared to decrease substantially as a result of leaf dehydration.     

Array calibration of angularly dependent gain and phase uncertainties with carry-on instrumental sensors

Array calibration with angularly dependent gain and phase uncertainties has long been a difficult problem. Although many array calibration methods have been reported extensively in the literature, they almost all assumed an angularly independent model for array uncertainties. Few calibration methods have been developed for the angularly dependent array uncertainties. A novel and efficient auto-calibration method for angularly dependent gain and phase uncertainties is proposed in this paper, which is called ISM (Instrumental Sensors Method). With the help of a few well-calibrated instrumental sensors, the ISM is able to achieve favorable and unambiguous direction-of-arrivals (DOAs) estimate and the corresponding angularly dependent gain and phase estimate simultaneously, even in the case of multiple non-disjoint sources. Since the mutual coupling and sensor position errors can all be described as angularly dependent gain/phase uncertainties, the ISM proposed still works in the presence of a combination of