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.     

Post-launch calibration of the visible and near-infrared channels of the Advanced Very High Resolution Radiometer on the NOAA-14 spacecraft

The post-launch calibration of the visible (channel l:≈0·58–0·68μm) and near-infrared (channel 2: ≈ 0·72–1·1 μm) channels of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-14 spacecraft is described. The southeastern part of the Libyan desert (21–23° N latitude; 28–29° E longitude) is used as a radiometrically stable calibration target to determine the ‘slope’—the inverse of the gain—of the AVHRR, expressed in units of W (m^?2 sr^?1 μm^?1 count^?1), in the two channels in the course of 1995. The variation of the ‘slope’ with time during 1995 indicates that channel 1 has degraded at the annual rate of 7·7 per cent; and channel 2 at the rate of 10·5 per cent. Comparison of the AVHRR ‘slopes’ immediately after launch of NOAA-14 with the results of pre-launch calibration performed in September/October 1993 indicates that channel 2 experienced a deterioration of ≈ 18 per cent (relative) immediately after launch while channel 1 was not appreciably affected. Formulae are given for the calculation of the post-launch calibration coefficients for the two channels.     

Cloud reflectivity profile classification using MSG/SEVIRI infrared multichannel and TRMM data

This work analyses the capability of utilizing cloud-top multispectral radiation to extract information about the vertical reflectivity profile of clouds. Reflectivity profiles and cloud type classification were collected using the Tropical Rainfall Measuring Mission (TRMM) 2A25 algorithm and brightness temperature multispectral channels (3.9, 6.2, 8.7, 10.8, and 12 μm) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite. The analysis was performed on four cloud types: convective, warm, and stratiform with and without bright band, using a four-channel combination (10.8–3.9, 6.2–10.8, 8.7–10.8, and 10.8–12.0 μm). The study was applied over Tropical Africa at the MSG subsatellite point, in August 2006. Sixteen individual profile types were detected: three warm, four convective, three stratiform without bright band, and six stratiform with bright band. These cloud profile types were examined using cloud-top multichannel brightness temperature differences. The channel combination results demonstrated that the information obtained from cloud-top radiation enables us to detect specific individual characteristics within the cloud reflectivity profile. The channel combinations employed in this study were effective in identifying warm and cold cloud types. In the 10.8–3.9 and 8.7–10.8 μm channels, brightness temperature differences were indicated in the detection of warm clouds, while the 6.2–10.8 μm channel was noted to be very efficient in classifying cold clouds. Cold clouds types were much more difficult to classify because they possess a similar multichannel signature, which caused ambiguity in the classification. In order to reduce this uncertainty, it was necessary to use texture information (space variability) to acquire a clearer distinction between different cloud types. The survey analysis showed good performance in classifying cloud types, with an accuracy of about 77.4% and 73.5% for night and day, respectively.     

Removal of atmospheric effects on a pixel by pixel basis from the thermal infrared data from instruments on satellites. The Advanced Very High Resolution Radiometer (AVHRR)

This paper is concerned with those values of sea-surface temperatures which lie between 270 and 300 K. The thermal infrared (THIR) data under consideration are from the 3-7, II and 12μm channels of the Advanced Very High Resolution Radiometer (AVHRR) instruments on TIROS-N, NOAA-6 and 7 satellites.

Simple relations for calculating the brightness temperatures from the THIR channels of the AVHRR are derived. Algorithms are presented for correcting these brightness temperatures for the non-linear response of the detectors used in the 11 and 12μm channels and for the emissivity of sea-water. Assuming the emissivity of sea-water is equal to 0.98, it is shown that, say, at 290 K. the emissivity corrections are about 0.45, 1.27 and 1.37K, respectively, in the 3.7, 11 and 12 μm channels.

For comparison purpose, we have included a brief account of the atmospheric correction procedure' which is intended to be employed for correcting the thermal infrared data from the European Remote Sensing satellite, ERS-1, in the late 1980s.

Using the standard atmospheric transmittances which were calculated by Phulpin and Deschamps (1980) we have developed a simple procedure for applying atmospheric corrections to the Advanced Very High Resolution Radiometer data using two spectral channels. This atmospheric correction procedure (i) does not require a knowledge of the distribution and abundance of the absorbers, emitters and scatterers in the atmosphere, and (ii) still enables one to evaluate the effective transmittance of the atmosphere which lies within the instantaneous field of view of the remote sensor. This means that one can apply the atmospheric correction on a pixel by pixel basis. An algorithm for the determination of the sea-surface temperature (SST) from the satellite data is presented. This algorithm utilizes the 11 and 12μm channel data from the NOAA-7 satellite. The reliability of this algorithm has been tested.

Comparison of atmospherically corrected SSTs with the simultaneous in situ bulk and point temperature data set (17 points) for relatively cloud-free atmosphere resulted in a bias of 0.63 K and a root mean square difference (r.m.s.d.) of ±0.69 K. When the algorithm for SST determination was corrected for this bias then the r.m.s.d. reduced to ±022 K.     

Rainfall sensitivity analyses for the HSB sounder: an Amazon case study

This work examines the sensitivity of the different channels of the HSB (Humidity Sensor for Brazil), on board the AQUA satellite, for the purpose of retrieving surface rainfall over land. The analysis is carried out in two steps: (a) a theoretical study performed using two radiative transfer models, RTTOV and the so‐called Eddington method; and (b) the determination of the correlation between coincident measurements of HSB brightness temperatures and radar rainfall estimates during the DRY‐TO‐WET/AMC/LBA field campaign held in the Amazon region during September and October 2002. Theoretical results indicate the sensitivity of the HSB to water vapour content and cloud liquid water in the precipitation estimation. Theoretical and experimental analyses show that the channels 150 and 183±7 GHz are more adapted to estimate precipitation than the 183±1 and 183±3 GHz channels. The simulation analyses clearly show a hierarchy in physical effects that determine the brightness temperature of these channels. The rain and ice scattering dominate over the absorption of liquid water, and the liquid water absorption effect dominates over the absorption of water vapour. The results show that the 150 and 183±7 channels are more sensitive to the variation of liquid water and ice than the 183±1 and 183±3 channels. For the precipitation estimation using these channels, it was found that it is best adapted to the low precipitation rate situations, since the brightness temperature is rapidly saturated in the presence of high intense precipitation. A case study to estimate precipitation using the radar data has shown that it is possible to adjust a curve that relates the precipitation rate to the brightness temperature of the 150 GHz channel with a good level of accuracy for low precipitation rates.     

AVHRR split window temperature differences and total precipitable water over land surfaces

Measurements from the thermal infrared split window channels of the AVHRR sensor were investigated for their relationship to the total atmospheric water vapour amount over land surfaces. The difference in brightness temperature between the AVHRR channel 4 and 5 (10·3–11·3μm and 11·4–12·3μm respectively) was found to be a linear function of total precipitable water, for several stations in differing climatic regimes. For each individual location the total precipitable water was estimated with a standard error ranging from 0·22 to 0·48 cm for the complete range of conditions from wet to dry season or summer to winter. For mid-latitude continental locations there is very little influence of atmospheric aerosols on the relationship while for the African Sahel region the effect of large airborne particulates with a silicate component introduces a significant effect at large values of aerosol optical depth due to absorption. The influence of spectral emissivity variation in the split window region was also observed for arid regions where there is a significant quartz component to the soil. It is concluded that for regional retrieval of precipitable water, this technique provides sufficient accuracy for application to correction of near-infrared satellite data such as AVHRR channel 2 (0·71 –0·98 μm), however the site specific relation between T ₄-T ₅ and PW needs to be established with independent PW measurements.     

On the use of synthetic 12 μm data in a split-window retrieval of sea surface temperature from AVHRR measurements

A numerical model of the radiation transfer through the atmosphere is used with a set of mid-latitude atmospheric profiles to simulate split-window measurements of the AVHRR/2 on the NOAA-7 satellite. These are used to quantify the degradation in accuracy which results in going from a sea surface temperature retrieval in which the effect of the atmosphere is estimated using measurements at 11 μm and 12 μm (channels 4 and 5), to one in which it is estimated using synthetic channel 5 data, as advocated by Singh et al (1985).     

AVHRR channel-3 noise and methods for its removal

Abstract

Data from the 37 μm channel of the Advanced Very High Resolution Radiometer (AVHRR) are contaminated by interference which appears in the images as a herring-bone pattern. This interference has been present, to a greater or lesser extent, in the data from all six of the instruments which have flown to date. A measure of the quality of the imagery, estimated from the NOAA-7 and NOAA-9 instruments’ calibration data, is used to gain an idea of their progressive deterioration over each satellite's lifetime. The spectral properties of the Fourier transform of the data are examined and used to attenuate the unwanted components of the signal to an acceptable level. Two simple restoration algorithms are applied to a particularly severely contaminated NOAA-7 image and the resulting images presented.     

Moments from space captured by MSG SEVIRI

A method for the generation of full disk MSG (METEOSAT Second Generation) SEVIRI (Scanning-Enhanced Visible and Infrared Imager) true colour composite images is presented. The algorithm mainly uses the SEVIRI channels VIS006 (0.6 μm), NIR008 (0.8 μm) and NIR016 (1.6 μm). Only one of the full disk SEVIRI channels is located in the visible spectral region. This is channel VIS006 which covers mainly the yellow to red parts of the visible spectrum. The lack of information in the blue and green parts of the visible spectrum is compensated by using data from NASA's (National Aeronautics and Space Administration's) Blue Marble next generation (BMNG) project to fill a look-up table (LUT) transforming RGB (red/green/blue) false colour composite images of VIS006/NIR008/NIR016 into true colour images. Tabulated radiative transfer calculations of a pure Rayleigh atmosphere are used to add an impression of Rayleigh scattering towards the sunlit horizon. The resulting images satisfy naive expectations: clouds are white or transparent, vegetated surfaces are greenish, deserts are sandy-coloured, the ocean is dark blue to black and a narrow halo due to Rayleigh scattering is visible at the sunlit horizon. Therefore, such images are easily interpretable also for inexperienced users not familiar with the characteristics of typical MSG false colour composite images.     

Simulations of microwave brightness temperatures at AMSU-B frequencies over a 3D convective cloud system

Numerical simulations have been carried out to understand the effects of clouds associated with a tropical deep convective cloud system on the Advanced Microwave Sensor Unit-B (AMSU-B) channels at 89, 150, 183.3 ± 7, 183.3 ± 3, and 183.3 ± 1 GHz. The hydrometeor profiles including cloud liquid water, cloud ice, snow, graupel, and rain water for a deep convective cloud system simulated by a realistic dynamical cloud model, the Goddard Cumulus Ensemble model, have been input to a Vector Discrete Ordinate Radiative Transfer model to simulate the nadir down-looking microwave brightness temperatures at the top of the atmosphere. It is found that the AMSU-B channels have large brightness temperature depressions occurring over the clouds with large ice water paths. Moreover, for the three water vapour sounding frequencies around 183.3 GHz, the frequencies broader and further away from the centre of the water vapour absorption line show stronger depressions. The three water vapour channels, particularly the channels closer to the absorption line centre, essentially have negligible influence from liquid water. However, the window frequencies at 89 and 150 GHz have distinct influence from liquid water, particularly the 150 GHz, although they are also strongly influenced by frozen hydrometeors. The AMSU-B frequencies at 150 GHz and water vapour channels of 183.3 ± 7 and 183.3 ± 3 GHz are sensitive to cirrus clouds with total ice water paths above 0.1–0.2 kg m^?2. The influence of deep convective clouds and thick cirrus clouds on the AMSU-B water vapour channels demonstrates that they have a potential to estimate ice water paths in thick cirrus clouds and in the upper parts of deep convective clouds, which can complement the retrievals from the 89 and 150 GHz channels.     

Mapping the degree of serpentinization within ultramafic rock bodies using imaging spectrometer data

In 0·4-2·5 μm reflectance spectra of serpentinized peridotites and synthetic olivine-serpentine-magnetite mineral mixtures, serpentinization is responsible for a decrease in contrast of olivine-pyroxene iron absorption features and an appearance and increase in OH^? absorption features near 1·4 μm and 2·3 μm. It is demonstrated that the degree of serpentinization is correlated positively with the depth of the 2·3 μm absorption feature, although small amounts of magnetite may obscure the spectral contrast and decrease the overall brightness of weakly serpentinized samples. This linear relationship is applied to map the degree of serpentinization from GER 63-channel imaging spectrometer data using the following methodology: (1) vegetation masking, (2) calculating the absorption band-depth of the 2·3 μm absorption feature, (3) translating this value into percentage serpentine-group minerals using an empirical linear model, and (4) estimating the degree of serpentinization at the remaining locations using conditional simulation techniques. Comparison or the results of the simulation with 49 field samples showed differences between + 33 per cent and ? 23 per cent serpentine-group minerals estimated.     

Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views

Methods for absolute calibration of visible and near-infrared sensors using ocean and cloud views have been developed and applied to channels 1 (red) and 2 (near-infrared) of the Advanced Very High Resolution Radiometer (AVHRR) for the NOAA-7, -9 and -11 satellites. The approach includes two steps. First step is intercalibration between channels 1 and 2 using high altitude (12 km and above) bright clouds as ‘ white’ targets. This cloud intercalibration is compared with intercalibration using ocean glint. The second step is an absolute calibration of channel 1 employing ocean off-nadir view (40-70° ) in channels 1 and 2 and correction for the aerosol effect. In this process the satellite measurements in channel 2, corrected for water vapour absorption are used to correct channel 1 for aerosol effect. The net signal in channel 1 composed from the predictable Rayleigh scattering component is used to calibrate this channel. The result is an absolute calibration of the two AVHRR channels. NOAA-9 channels I and 2 show a degradation rate of 8-8 per cent and 6 per cent, respectively, during 1985-1988 and no further degradation during 1988-1989 period. NOAA-II shows no degradation during the 1989 mid 1991 period. This trend is similar to the calibration trend obtained using desert site observations, the absolute calibration found in this work for both sensors is lower by 17 to 20 per cent ( suggesting higher degradation) from the absolute calibration of Abel et al. ( 1993 Journal of Atmospheric and Ocean Technology,10, 493-508 that used aircraft measurements. Furthermore we show that application of the calibration of Abel et al. or the present one for remote sensing of aerosol over Tasmania, Australia failed to predict correctly the aerosol optical thickness measured there. The only way to reconcile all these differences is by allowing for a shift of 17 nm towards longer wavelengths of the AVHRR channel 1 effective wavelength. We show that with this shift, we get an agreement between the two absolute calibration techniques ( ± 3 percent), and both of them do predict correctly the optical thickness in the two channels ( + 0.02) Recent work in preparation for publication (Vermote el al, 1995, in preparation indicates that this shift is due to an out of band transmission ( 6 per cent at 900nm) for AVHRR channel 1 previously unidentified.     

Simulation study of multispectral estimation of sea-surface temperature from Infrared observations

A general multispectral analysis technique is described to estimate sea-surface temperature (SST) using data from the High-Resolution Infrared Radiation Sounder (HIRS) on board the TIROS-N/NOAA-6 series of weather satellites. Our study, based on infrared radiative transfer simulations, shows that the radiometric observations for spectral channels at 11.1, 8.3, 13.4, and 4.57 μm are sufficient to provide SST values to a high degree of accuracy. These multispectral calculations automatically incorporate corrections due to the variations in atmospheric water vapor and temperature.     

Detecting and tracking mesoscale precipitating objects using machine learning algorithms

Accurate identification of precipitating clouds is a challenging task. In the present work, Support Vector Machines (SVMs), Decision Trees (DT), and Random Forests (RD) algorithms were applied to extract and track mesoscale convective precipitating clouds from a series of 22 Geostationary Operational Environmental Satellite-13 meteorological image sub-scenes over the continental territory of Colombia. This study’s aims are twofold: (i) to establish whether the use of five meteorological spectral channels, rather than a single infrared (IR) channel, improves rainfall objects detection and (ii) to evaluate the potential of machine learning algorithms to locate precipitation clouds. Results show that while the SVM algorithm provides more accurate classification of rainfall cloud objects than the traditional IR brightness temperature threshold method, such improvement is not statistically significant. Accuracy assessment was performed using STEP (shape (S), theme (T), edge (E), and position (P)) object-based similarity matrix method, taking as reference precipitation satellite images from the Tropical Rainfall Measuring Mission. Best thematic and geometric accuracies were obtained applying the SVM algorithm.     

Inter-satellite calibration linkages for the visible and near-infared channels of the Advanced Very High Resolution Radiometer on the NOAA-7, -9, and -11 spacecraft

The post-launch degradation of the visible (channel 1:≈0· 58–0·68μm) and near-infrared (channel 2: ≈ O·72–1·1 μm) channels of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA–7, –9, and –11 Polar-orbiting Operational Environmental Satellites (POES) was estimated using the south-eastern part of the Libyan desert as a radiometrically stable calibration target. The relative annual degradation rates, in per cent, for the two channels are, respectively: 3·6 and 4·3 (NOAA–7) 5·9 and 3·5 (NOAA–9); and 1·2 and 2·0 (NOAA–11). Using the relative degradation rates thus determined, in conjunction with absolute calibrations based on congruent path aircraft/satellite radiance measurements over White Sands, New Mexico (U.S.A.), the variation in time of the absolute gain or ‘slope’ of the AVHRR on NOAA–9 was evaluated. Inter-satellite calibration linkages were established, using the AYHRR on NOAA–9 as a normalization standard. Formulae for the calculation of calibrated radiances and albedos (AYHRR usage), based on these interlinkages, are given for the three AYHRRs.     

Retrieval of precipitable water from bhaskara-II microwave measurements and its comparison with NOAA-7 and radiosonde data

The Satellite Microwave Radiometer (SAMIR-II) on board the second Indian remote sensing satellite, Bhaskara-II, measured microwave radiation at 19.35, 22.235, and 31.4 GHz. These measurements are primarily influenced by water vapor, cloud liquid water, and wind speed. The effect of collinearity among SAMIR-II channels has been examined to determine the effectiveness of a two- or three-channel subset for retrieving precipitable water (PW). The analysis indicated a decreasing correlation coefficient value from 0.87 to 0.65 with increasing frequency separation due to dynamical interdependence of meteorological variables on brightness temperature measurements. A correlation coefficient of 0.65 between two window channels, 19.35 and 31.4 GHz, is suggestive of using either of the two channels for water vapor retrieval. Performance of SAMIR-II channels has therefore been evaluated by retrieving (PW) using regression technique with all the three channels of SAMIR-II and its subset of the two channels, 19.35/22.235 and 22.235/31.4 GHz and comparing the results from NOAA-7 supplied PW and limited radiosonde derived PW. Comparison of PW derived from the regression models and NOAA-7 gave an rms difference of 0.7 to 1.0 g/cm². Retrievals of PW using 19.35 and 22.235 GHz channels and its comparison with near concurrent coastal radiosonde observations gave an rms difference of 0.32 g/cm², comparable to the accuracy (0.2–0.4 g/cm²) obtained from previous satellite-borne microwave radiometers.     

The estimation of atmospheric corrections to one-channel (11 μ m) data from the AVHRR; simulation using AVHRR/ 2

It has been established that the sea-surface brightness temperatures Tb₄ in the 11 μ m channel and T_b4in the 12 μ m channel of the Advanced Very High Resolution Radiometer (AVHRR/ 2) are linearly related to a good degree of accuracy, i.e. T_b5= α+ β T_b4 Using AVHRR/ 2 data for various dates and from different parts of the world's oceans, the parameters a and 0 have been determined. The above relation may then be used for simulating T_b5 for those cases for which only Tb4 is available (e.g. for the AVHRR on TIROS-N, NOAA-6, NOAA-8, etc.). The brightness temperature TM and pseudo-brightness temperature T_b5 then enable one to use the split-window technique for estimating atmospherically-corrected sea-surface temperatures (SSTs) from the 11μ m channel data alone. Such an atmospheric correction technique should be a possibility because the 11μ m channel of the AVHRR on the various satellites in question are almost identical

This technique has been used with two split-window algorithms for correcting the data from the 11μ m channel of the AVHRR instrument on the TIROS-N satellite obtained off south-western Portugal. One of the algorithms gives ‘ skin’ temperatures and the other algorithm gives bulk temperatures. The resulting SSTs for twelve dates from 15 June 1979 to 14 June 1980 have been compared with sea-surface (skin) temperatures which were obtained with airborne radiometer data obtained on the same dates.     

Global land monitoring from AVHRR: potential and limitations

Global Vegetation Index ( GVI) time series of visible, near-IR and thermal IR Advanced Very High Resolution Radiometer (AVHRR)weekly composite data with a 015^° spatial resolution collected from NOAA-9 and -11 satellites have been used to develop a prototype global land monitoring system. The system is based on standardized anomalies of the Normalized Difference Vegetation Index (NDVI) and channel 4 brightness temperature ( T₄ )for the period April 1985-September 1994. Processing included: post-launch updated calibration; cloud screening; filling in the cloud induced data gaps by monthly averaging and spatial interpolation; suppressing residual noise by smoothing; calculating 5-year monthly means and standard deviations of NDVI and T₄and their standardized anomalies. The derived anomalies show potential for detecting and interpreting the seasonal cycle and statistically significant interannual variability. Yet, discontinuities and residua! trends can be traced in time series of NDVI and T4. Discontinuities result from the switch from NOAA-9 to NOAA-11 in 1988, and the Mount Pinatubo eruption in 1991. Trends are a combined effect of satellite orbit drift and a possible persistent error in post-launch calibration of solar channels. The orbit drift affects the solar and thermal IR channels through systematic variation of illumination geometry and diurnal heating/cooling of the surface and atmosphere, respectively. Examples are given to illustrate the magnitude of these effects, which reduce the ability to monitor small year-to-year surface changes. The present system yields more accurate results in geographic regions, where atmospheric, angular and diurnal variability effects have a lesser impact on the derived anomalies, i.e. over vegetated areas outside the tropics during local summers. For global-scale monitoring, angular, atmospheric and diurnal variability corrections must be incorporated in the present system.     

On remote sensing of convective clouds over Indian continent and quantification of their variability in a warming environment

Convective clouds are associated with extreme precipitation events triggering floods. They are an important part of atmospheric circulation and hydrological cycle. Changes in convective clouds in changing climate remain one of the most challenging aspects of forecasting future climate change. The present research focuses on identification of convective clouds using multispectral measurements at split window channels (near 10.5 µm and 12.5 µm) and water vapour absorption channels (near 6.7 µm) from Meteosat 7 observations. Variability of convective clouds has been examined in warming climate using observations from Meteosat First Generation (MFG). It has been reported that convective clouds show high density over Western, Central, North Eastern Indian region, and the Western Ghats during the monsoon period. This observation is consistent with measurement from Precipitation Radar (PR) (reflectivity-based threshold) on-board Tropical Rainfall Measuring Mission (TRMM) and rain gauge-based product. The present technique fails to detect shallow convective clouds over the Western Ghats. An increase of about 32.68% ± 5.81% per degree increase in temperature has been reported in convective clouds over India.     

Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A

This work extends the previous study of Trishchenko et al. [Trishchenko, A. P., Cihlar, J., & Li, Z. (2002). Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sensing of Environment 81 (1), 1-18] that analyzed the spectral response function (SRF) effect for the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA satellites NOAA-6 to NOAA-16 as well as the Moderate Resolution Imaging Spectroradiometer (MODIS), the VEGETATION sensor (VGT) and the Global Imager (GLI). The developed approach is now applied to cover three new AVHRR sensors launched in recent years on NOAA-17, 18, and METOP-A platforms. As in the previous study, the results are provided relative to the reference sensor AVHRR NOAA-9. The differences in reflectance among these three radiometers relative to the AVHRR NOAA-9 are similar to each other and range from − 0.015 to 0.015 (− 20% to + 2% relative) for visible (red) channel, and from − 0.03 to 0.02 (− 5% to 5%) for the near infrared (NIR) channel. The absolute change in the Normalized Difference Vegetation Index (NDVI) ranged from − 0.03 to + 0.06. Due to systematic biases of the visible channels toward smaller values and the NIR channels toward slightly larger values, the overall systematic biases for NDVI are positive. The polynomial approximations are provided for the bulk spectral correction with respect to the AVHRR NOAA-9 for consistency with previous study. Analysis was also conducted for the SRF effect only among the AVHRR-3 type of radiometer on NOAA-15, 16, 17, 18 and METOP-A using AVHRR NOAA-18 as a reference. The results show more consistency between sensors with typical correction being under 5% (or 0.01 in absolute values). The AVHRR METOP-A reveals the most different behavior among the AVHRR-3 group with generally positive bias for visible channel (up to + 5%, relative), slightly negative bias for the NIR channel (1%-2% relative), and negative NDVI bias (− 0.02 to + 0.005). Polynomial corrections are also suggested for normalization of AVHRR on NOAA-15, 16, 17 and METOP-A to AVHRR NOAA-18.