AVHRR Channels Research Articles

A machine learning-based accuracy enhancement on EUMETSAT H-SAF H35 effective snow-covered area product

Snow is a major element of the cryosphere with significant impact on the Earth's water cycle and global energy budget. Acquiring consistent and long time series data on the spatial extent of snow cover doubtlessly plays a key role in our understanding and modeling of the current and future environmental dynamics. Remote sensing offers a powerful tool for continuous retrieval of snow cover information by utilizing snow's contrasting reflectance characteristics at optical wavelengths. The pre-operational H35 covers the Northern Hemisphere, and it is the successor of the operational Pan-European H12 daily fractional snow-covered area (fSCA) product at ~1 km. Both products are developed through the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of EUMETSAT by exploiting AVHRR channels. This study is focused on developing an alternative fully data-driven H35 product with improved accuracy using a machine learning (ML)-based approach. Multivariate adaptive regression splines (MARS) algorithm is trained by using AVHRR reflectance data as well as the well-known snow and vegetation indices (i.e., NDSI and NDVI) to generate the new version of H35 fSCA product. The reference fSCA maps required for the training of MARS models are obtained from the higher resolution Sentinel 2 multispectral imagery. The MARS-based fSCA models are validated against an initial test dataset composed of 15 Sentinel 2 scenes over European Alps, Tatra Mountain Range, and Turkey. The final MARS-H35 product is then rigorously assessed over the whole Northern Hemisphere within a temporal domain spanning from Nov 2018 to Nov 2019. The quantitative testing process involves the use of reference data in both continuous and dichotomous scales: i) Sentinel 2 derived reference fSCA maps, ii) ERA5-Land snow depth data, iii) MODIS MOD10A1 NDSI snow cover data, and finally iv) in-situ snow depth data. Additionally, qualitative assessment is also performed by visually comparing MARS-H35/MODIS false-color and MARS-H35/Sentinel 2-derived reference fSCA image pairs over various geographic regions. The overall results indicate that: i) the proposed MARS-H35 fSCA product overperforms the original H35, and ii) it has higher capability in detecting the fine variations in the extent of snow cover, especially across the fringes of the slopes in complex mountainous terrains.

Selecting a first‐guess sea surface temperature field as input to forward radiative transfer models

Advanced Clear‐Sky Processor for Oceans (ACSPO), developed at National Environmental Satellite, Data, and Information Service, reports observed top‐of‐atmosphere clear‐sky brightness temperatures (BT) in AVHRR channels 3B (3.7), 4 (11), and 5 (12μm) along with the sea surface temperatures (SST) retrieved from these BTs as a level 2 (L2) product. Additionally, ACSPO provides the corresponding BTs simulated with Community Radiative Transfer Model (CRTM), using Reynolds daily Level 4 (L4) optimum interpolation SST (OISST) and NCEP‐GFS profiles as inputs. Accuracy of simulated BTs is critical for ACSPO functionalities, including detecting clouds, retrieving physical SSTs, monitoring sensor performance, and validating CRTM. This paper tests 11 gap‐free gridded L4 SSTs for their potential use as first‐guess fields in ACSPO to improve accuracy of simulated BTs. As a first step toward the objective, this study checks for consistency between various L4 products and ACSPO L2 SSTs. This SST consistency was shown earlier to be representative of, and equivalent to, the consistency between measured and simulated BTs, thus avoiding expensive CRTM calculations. The metrics employed in L4 comparisons include the global spatial L4‐L2 SST biases and variances and their temporal stability. Also, the effect of L4 fields on the corresponding satellite‐to‐satellite consistency (calculated as L2‐L2 double differences) is examined. Several L4 products, including the GHRSST Multi‐Product Ensemble and Canadian Meteorological Centre analysis (CMC‐0.2°), show better consistency with ACSPO L2 SST and will be explored in the future versions of ACSPO.

Optimised frequency grids for infrared radiative transfer simulations in cloudy conditions

This paper shows that radiometer channel radiances for cloudy atmospheric conditions can be simulated with an optimised frequency grid derived under clear-sky conditions. A new clear-sky optimised grid is derived for AVHRR channel 5(12μm,833cm−1). For HIRS channel 11 (7.33μm,1364cm−1) and AVHRR channel 5, radiative transfer simulations using an optimised frequency grid are compared with simulations using a reference grid, where the optimised grid has roughly 100–1000 times less frequencies than the full grid. The root mean square error between the optimised and the reference simulation is found to be less than 0.3K for both comparisons, with the magnitude of the bias less than 0.03K. The simulations have been carried out with the radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS), version 2, using a backward Monte Carlo module for the treatment of clouds. With this module, the optimised simulations are more than 10 times faster than the reference simulations. Although the number of photons is the same, the smaller number of frequencies reduces the overhead for preparing the optical properties for each frequency. With deterministic scattering solvers, the relative decrease in runtime would be even more. The results allow for new radiative transfer applications, such as the development of new retrievals, because it becomes much quicker to carry out a large number of simulations. The conclusions are applicable to any downlooking infrared radiometer.

Validation of the Community Radiative Transfer Model by using CloudSat data

NOAA‐18 Advanced Microwave Sounding Unit‐A (AMSUA), Microwave Humidity Sounder (MHS), and Advanced Very High Resolution Radiometer/3 (AVHRR/3), along with collocated CloudSat data under nonprecipitation conditions, are used to validate the accuracy of the Community Radiative Transfer Model (CRTM). The observed brightness temperatures (BTs) from NOAA‐18 instruments are compared to those simulated by the CRTM using the inputs of CloudSat retrieved hydrometeor profiles. The forward model biases are computed for various cloudy conditions, which are required for the assimilation of satellite cloudy radiances in operational forecast systems. Simulated BTs under nonprecipitation, cloudy conditions are averaged in space to account for the cloud inhomogeneity within the sensors' fields of view. The simulated and observed BT fields, BT distributions, and BT difference distributions show good agreement for all microwave channels. Simulations under clear skies in general have low biases and standard deviation errors, and these errors are only marginally increased under cloudy conditions for microwave channels. For AVHRR channels 4 and 5, the biases and standard deviation errors are low and very accurate for clear and water cloud conditions. However, there are larger standard deviation errors under cirrus and mixed‐phase cloud conditions for those channels. The spatial averaging method significantly reduced the standard deviation errors under cloudy conditions. In this study, we have validated the CRTM modules (gaseous absorption model, cloud absorption and scattering model, and surface emissivity models over ocean) that generate optical properties of the atmosphere and surface in the microwave and thermal infrared spectral region.

Application of principal component analysis and information fusion technique to detect hotspots in NOAA/AVHRR images of Jharia coalfield, India

Present paper proposes an algorithm for hotspot (sub-surface fire) detection in NOAA/AVHRR images in Jharia region of India by employing Principal Component Analysis (PCA) and fusion technique. Proposed technique is very simple to implement and is more adaptive in comparison to thresholding, multi-thresholding and contextual algorithms. The algorithm takes into account the information of AVHRR channels 1, 2, 3, 4 and vegetation indices NDVI and MSAVI for the required purpose. Proposed technique consists of three steps: (1) detection and removal of cloud and water pixels from preprocessed AVHRR image and screening out the noise of channel 3, (2) application of PCA on multichannel information along with vegetation index information of NOAA/AVHRR image to obtain principal components, and (3) fusion of information obtained from principal component 1 and 2 to classify image pixels as hotspots. Image processing techniques are applied to fuse information in first two principal component images and no absolute threshold is incorporated to specify whether particular pixel belongs to hotspot class or not, hence, proposed method is adaptive in nature and works successfully for most of the AVHRR images with average 87.27% detection accuracy and 0.201% false alarm rate while comparing with ground truth points in Jharia region of India.

Development of a daily long term record of NOAA-14 AVHRR land surface temperature over Africa

We developed a new 6-year daily, daytime and nighttime, NOAA-14 AVHRR based land surface temperature (LST) dataset over continental Africa for the period 1995 through 2000. The processing chain was developed within the Global Inventory Modeling and Mapping System (GIMMS) at NASA's Goddard Space Flight Center. This paper describes the processing methodology used to convert the Global Area Coverage Level-1b data into LST and collateral data layers, such as sun and view geometries, cloud mask, local time of observation, and latitude and longitude. We used the Ulivieri et al. [Ulivieri, C., M.M. Castronuovo, R. Francioni, and A. Cardillo (1994), A split window algorithm for estimating land surface temperature from satellites, Adv. Space Research, 14(3):59–65.] split window algorithm to determine LST values. This algorithm requires as input values of surface emissivity in AVHRR channels 4 and 5. Thus, we developed continental maps of emissivity using an ensemble approach that combines laboratory emissivity spectra, MODIS-derived maps of herbaceous and woody fractional cover, and the UNESCO FAO soil map. A preliminary evaluation of the resulting LST product over a savanna woodland in South Africa showed a bias of < 0.3 K and an uncertainty of < 1.3 K for daytime retrievals (< 2.5 K for night). More extensive validation is required before statistically significant uncertainties can be determined. The LST production chain described here could be adapted for any wide field of view sensor (e.g., MODIS, VIIRS), and the LST product may be suitable for monitoring spatial and temporal temperature trends, or as input to many process models (e.g., hydrological, ecosystem).

Detecting and monitoring plumes caused by major industrial accidents with JPLUME, a new software tool for low-resolution image analysis

A new software tool for the automatic detection and monitoring of plumes caused by major industrial accidents is described. This tool has been designed in order to use near real time information as provided by satellite images, perform sophisticated image analysis and elaborate a user-friendly operational environment for the detection of plumes caused by major industrial accidents. The methodology, based on NOAA/AVHRR (Advanced Very High Resolution Radiometer) imagery, uses a two-dimensional feature space in order to discriminate pixels that contain plumes from those that correspond to clouds or to the underlying surface. The two-dimensional feature space is generated by combining AVHRR channels 1 (visible), 2 (near infrared) and 5 (thermal infrared). The software tool proposed has been coded in JAVA2 language, using the concepts of interoperability and object-oriented programming. This study demonstrates the applicability of the tool for the detection of a plume caused by a massive explosion in a firework factory in Enschede, The Netherlands, on May 13, 2000. The effectiveness and reliability of the software tool was found to be satisfactory, as plume was automatically detected and discriminated from the underlying surface.

Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method

The temperature-independent thermal infrared spectral indices (TISI) method is employed for the separation of land surface temperature (LST) and emissivity from surface radiances (atmospherically corrected satellite data). The daytime reflected solar irradiance and the surface emission at ∼3.8 μm have comparable magnitudes. Using surface radiances and a combination of day–night 2-channel TISI ratios, the ∼3.8 μm reflectivity is derived. For implementing the TISI method, coefficients for NOAA 9–16 AVHRR channels are obtained. A numerical analysis with simulated surface radiances shows that for most surface types (showing nearly Lambertian behavior) the achievable accuracy is ∼0.005 for emissivity (AVHRR channel-5) and ∼1.5 K for LST. Data from the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for calculation of atmospheric attenuation. Comparisons are made over a part of central Europe on two different dates (seasons). Clouds pose a major problem to surface observations; hence, monthly emissivity composites are derived. Additionally, using TISI-based monthly composites of emissivities, a normalized difference vegetation index (NDVI)-based method is tuned to the particular study area and the results are intercompared. Once the coefficients are known, the NDVI method is easily implemented but holds well only for vegetated areas. The error of the NDVI-based emissivities (with respect to the TISI results) ranges between −0.038 and 0.032, but for vegetated areas the peak of the error-histogram is at ∼0.002. The algorithm for retrieving emissivity via TISI was validated with synthetic data. Due to the different spatial scales of satellite and surface measurements and the lack of homogeneous areas, which are representative for low-resolution pixels and ground measurements, ground-validation is a daunting task. However, for operational products ground-truth validation is necessary. Therefore, also an approach to identify suitable validation sites for meteorological satellite products in Europe is described.

REMOTE SENSING OF THE SEDIMENTATION PLUME OF THE RIVER SAN JUAN

The River San Juan (RSJ), in the border between Nicaragua and Costa Rica, is one the major rivers in Central America and drains the largest basin in the region (38570 km2) in terms of volume. Extending from Lago Cicibolca to the Caribbean Sea, the RSJ is an important source of freshwater, sediments, nutrients and pollutants to the continental shelf. Ecosystems degradation, contamination of water bodies and overexploitation of natural resources, particularly deforestation in the southern part of the basin, modified and incremented sedimentation processes in the basin and the coastal margin during the last few decades. Advanced Very High Resolution Radiometer and Sea-viewing Wide Field-of-view Sensor data, together with in-situ CTD, current meter, turbidity sensor and water sampling for chemical analysis data are used to study, for the first time, the spatial and temporal variability of the plume of the RSJ and the coastal zone under its influence. An atmospheric correction has been implemented in order to obtain water reflectance from the AVHRR channels 1 and 2. Results from the above satellite and in-situ observations are presented

A generalized single‐channel method for retrieving land surface temperature from remote sensing data

Many papers have developed algorithms to retrieve land surface temperature from at‐sensor and land surface emissivity data. These algorithms have been specified for different thermal sensors on board satellites, i.e., the algorithm used for one thermal sensor (or a combination of thermal sensors) cannot be used for other thermal sensor. The main goal of this paper is to propose a generalized single‐channel algorithm that only uses the total atmospheric water vapour content and the channel effective wavelength (assuming that emissivity is known), and can be applied to thermal sensors characterized with a FWHM (Full‐Width Half‐Maximum) of around 1 μm actually operative on board satellites. The main advantage of this algorithm compared with the other single‐channel methods is that in‐situ radiosoundings or effective mean atmospheric temperature values are not needed, whereas the main advantage of this algorithm compared with split‐window and dual‐angle methods is that it can be applied to different thermal sensors using the same equation and coefficients. The validation for different test sites shows root mean square deviations lower than 2 K for AVHRR channel 4 (λ ≈ 10.8 μm) and ATSR‐2 channel 2 (λ ≈ 11 μm), and lower than 1.5 K for Landsat Thematic Mapper (TM) band 6 (λ ≈ 11.5 μm).

New observational evidence for global warming from satellite

Accurate measurements of surface radiative temperature, i.e. skin temperature, would be more directly interpretable in terms of the surface response to increase of greenhouse gases than the more conventional screen temperatures. Such measurements have not previously been attempted because of the difficulties of converting existing observations into a meaningful measurement. We have developed procedures for removing the effects of changing satellite orbits and cloud contamination from skin temperatures estimated from AVHRR channels 4 and 5, and so provide a first estimate of the trends of land surface skin temperature over the last two decades. The estimated land temperature increase is not only much greater than that for the atmosphere but also apparently somewhat larger than the estimates of surface air temperature increase from in situ measurement.

Estimation of roughness length and sensible heat flux from WiFS and NOAA AVHRR data

Development of representative NOAA AVHRR data based Land Surface Processes (LSP) parameters, towards incorporation into GCMs in quasi-dynamic mode, was undertaken by using (i) Normalized Difference Vegetation Index (NDVI) for the computation of roughness length (Z o), and (ii) Land Surface Temperature (T s) along with air density, specific heat of air at constant pressure, air temperature and aerodynamic resistance data to compute Sensible Heat Flux (SHF). NDVI was corrected for sensor degradation and atmospheric errors. Surface temperature (T s) was computed for the pixel by weighted mixing of the temperatures computed for the assumed bare soil (T g) and full canopy (T v) conditions, using respective emissivity values in AVHRR channels 4 and 5 and split window algorithm, based on relative normalized distance of NDVI (for the pixel) from NDVI (soil) and NDVI (full canopy). The results reported in this study compared well with the Z o and SHF computed from tower-based meteorological measurements. This paper also discusses Z o computed with 188 m resolution IRS-1C/WiFS data and validation of results obtained from the AVHRR data with those derived from WiFS data

A simplified method for estimating the total water vapor content over sea surfaces using NOAA-AVHRR channels 4 and 5

A simplified method for estimating the total amount of atmospheric water vapor, W, over sea surfaces using NOAA-AVHRR Channels 4 and 5 is presented. This study has been carried out using simulated AVHRR data at 11 and 12 /spl mu/m (with MODTRAN 3.5 code and the TIGR database) and AVHRR, PODAAC, and AVISO databases provided by the Louis Pasteur University (Strasbourg-France), NASA-NOAA, and Meteo France, respectively. The method is named linear atmosphere-surface temperature relationship (LASTR). It is based on a linear relationship between the effective atmospheric temperature in AVHRR Channel 4 and sea surface temperature. The LASTR method was compared with the linear split-window relationship (LSWR), which is based on a linear regression between W and the difference of brightness temperature measured in the same channels (/spl Delta/T=T4-TS). The results demonstrate the advantage of the LASTR method, which is capable of estimating W from NOAA-14 afternoon passes with a bias accuracy of 0.5 g cm/sup -2/ and a standard deviation of 0.3 g cm/sup -2/, compared with the W obtained by the AVISO database. In turn, a global bias accuracy of 0.1 g cm/sup -2/ and a standard deviation within 0.6 g cm/sup -2/ have been obtained in comparison with the W included in the PODAAC database derived from the special sensor microwave/imager (SSM/I) instrument.

Mapping temperature independent spectral indice of emissivity and directional emissivity in AVHRR channels 4 and 5

This work addresses the retrieval of AVHRR channel 4 and channel 5 emissivities from time series of images over two extended regions, one over Northern Africa and centred on Tunisia, the other centred on the Iberian Peninsula. The retrieval is based on the Temperature Independent Spectral Indice of Emissivity (TISIE) concept, a particular ratio of channel emissivities, which values are computed from a temperature independent combination of infrared channel radiances. This paper is focused on the use of two-channels TISIE indices relating channel 3 emissivity to either channel 4 or channel 5 emissivity. The retrieval of channel 3 directional emissivity from the data sets was achieved and discussed in Part I, whereas this paper emphasizes the determination and characteristics of the TISIE indices. These indices are found to be, within experimental error, ( i ) temperature independent; ( ii ) independent of zenithal view angle; ( iii ) stable over the whole period for a given surface type, allowing for building pixel-wise mean temporal indices. These mean values are then used to determine and model the directional emissivities in both channel 4 and 5. Emissivities exhibit little spectral and angular variations for surfaces covered with vegetation, with values higher than 0.96. Over arid areas (bare soils), emissivities are mostly less than 0.95, slightly higher in channel 5 than in channel 4, and exhibit significant angular variation, up to 3% decrease from nadir to 60° for low emissivity surface. Results suggest that TISIE indices may have a good potential for discriminating between different bare soil types.

Updated calibration coefficients for NOAA-14 AVHRR Channels 1 and 2

Updated calibration coefficients for NOAA-14 AVHRR Channels 1 and 2

Sea ice surface temperature in the Weddell Sea (Antarctica), from drifting buoy and AVHRR data

The surface temperature of sea ice is derived from infrared AVHRR measurements and from in situ measurements at drifting buoys in the Weddell Sea, Antarctica. For clear sky cases, the surface temperature is calculated from AVHRR channel 4 data (10.3–11.3 μm). After calibrating and geo-referencing the infrared measurements, an atmospheric correction is carried out using the radiation transfer model MODTRAN and temperature and humidity data from ECMWF analyses. The results are compared with surface temperatures derived from air temperature measurements from drifting buoys with an energy balance model. Results from both methods are linearly related with a correlation coefficient of 0.97 and a slope of 1.03. The bias is less than 1.3 K and the standard deviation of the residuals is 2.56 K. Accuracies and worst case error estimates of both methods are quantified by sensitivity studies with respect to the relevant sources of errors. A typical accuracy of the buoy-based surface temperature is 2 K and a worst case error estimate is 4–7 K depending on the ice thickness. The accuracy and worst case error estimate of satellite-derived surface temperature are of the same magnitude. Thus, none of the derivation methods is considered to be a validation of the other. Ice surface temperature variations in the Antarctic sea ice zone are caused by moving depressions in all seasons and cover a range of up to 20 K. Satellite measurements with a typical accuracy of 2 K may provide surface temperatures with a satisfactory signal to noise ratio in this data-sparse region, although clouds bear a significant restriction to their application.

Quantitative dynamic flood monitoring with NOAA AVHRR

The analysis of the spatial extent and temporal pattern of flood inundation from remotely sensed imagery is of critical importance to flood mitigation. With a high frequency of global coverage, NOAA/AVHRR has the advantage of detecting flood dynamics during devastating floods. In this paper, we describe a systematic approach to flood monitoring using AVHRR data. Four critical issues for successful flood monitoring with AVHRR were identified: correct identification of water bodies, effective reduction of cloud contamination, accurate area estimation of flood extent, and dynamic monitoring of flood processes. In accordance with the spectral characteristics of water and land in AVHRR channels, a simple but effective water identification method was developed with the ability to reduce cloud influences. The area of flooded regions was calculated with the consideration of areal distortion due to map projection, and mixed pixels at water/land boundaries. Flood dynamics were analysed from flood distributions in b...

Quantitative dynamic flood monitoring with NOAA AVHRR

The analysis of the spatial extent and temporal pattern of flood inundation from remotely sensed imagery is of critical importance to flood mitigation. With a high frequency of global coverage, NOAA/AVHRR has the advantage of detecting flood dynamics during devastating floods. In this paper, we describe a systematic approach to flood monitoring using AVHRR data. Four critical issues for successful flood monitoring with AVHRR were identified: correct identification of water bodies, effective reduction of cloud contamination, accurate area estimation of flood extent, and dynamic monitoring of flood processes. In accordance with the spectral characteristics of water and land in AVHRR channels, a simple but effective water identification method was developed with the ability to reduce cloud influences. The area of flooded regions was calculated with the consideration of areal distortion due to map projection, and mixed pixels at water/land boundaries. Flood dynamics were analysed from flood distributions in both space and time. The maximum spatial extent of floods, generated by compiling the time series of flood maps, was informative about flood damages. We report a successful use of this approach to monitor the Huaihe river flood, a centennial devastating disaster occurred in the Huaihe river basin of China in the summer of 1991.

Updated calibration coefficients for NOAA-14 AVHRR Channels 1 and 2

New calibration coefficients for channels 1 and 2 of NOAA-14 AVHRR are presented. Observations from the radiometrically stable permanent ice sheet of central Antarctica for the period from launch through January 2001 are used to evaluate the accuracy of published calibration coefficients. The rate of sensor degradation in channels 1 and 2 is found to change after January 1998. The new calibration coefficients more accurately characterize sensor performance to date.

Comparison of NOAA-AVHRR and DMSP-OLS for operational fire monitoring in Kalimantan, Indonesia

Fire products derived from NOAA-AVHRR channel 3 images and DMSP-OLS are compared to determine the most appropriate AVHRR channel 3 thresholds and methods for operational monitoring of fire in Kalimantan, Indonesia. The results indicate that about 77% of AVHRR fire cells derived from a temperature threshold of 321 deg K applied to channel 3 daytime images corresponded with DMSP-OLS fire cells at monthly or longer time scales. However, lower temperature thresholds produced slightly greater correspondence between the two sensors on the same date. By contrast, a contextual algorithm produced a lower correspondence (approximately 50%) between fire products from the two sensors. Although the number and extent of DMSP-OLS fire cells was much greater than fire-cells derived from AVHRR channel 3, the spatial distribution of the two products was broadly similar in the study area.