National Oceanic And Atmospheric Administration Advanced Very High Resolution Radiometer Research Articles

Toward a Reliable Correction of NOAA AVHRR Orbital Drift

The NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High-Resolution Radiometer) orbital drift prevents the use of its derived land surface temperature (LST) data for global studies of temperature trends, especially for the 80s and 90s over land. In a previous study, we showed how orbital drift correction methods could be validated by simulating a reference and drifted time series from alternative MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and InfraRed Imager) data, thanks to their high (15 min) temporal resolution. In this study, we show how these alternative data allow identifying orbital drift effects on different land covers, and how these effects could be mitigated with novel approaches. We also identify two key statistical parameters to assess orbital drift correction performance: the bias between corrected and drifted time series and the trend of their difference. We present two methods and compare their results with an alternative orbital drift correction, validated against in situ data by their authors. Considering an ideal case where the whole influence of the orbital drift is known, our novel approach allows for an almost complete removal of the orbital drift effect (zero bias and 0.05 K/yr difference trend). However, in real cases, when we have only access to the drifted time series, our approach’s performance decreases slightly, mainly through a larger spread of the retrieved statistics. As for the alternative correction method, its performance is poorer, even if it actually succeeds in removing part of the observed orbital drift. These results, as well as the new insights we provide on the orbital drift effect on LST, pave the way toward a reliable correction of NOAA AVHRR orbital drift. We therefore recommend the use of simulated LST time series such as the ones used in this study for the validation of orbital drift correction methods.

Potential Temporal and Spatial Trends of Oceanographic Conditions with the Bloom of Ulva Prolifera in the West of the Southern Yellow Sea

Based on National Oceanic and Atmospheric Administration/Advanced Very High-Resolution Radiometer (NOAA/AVHRR) remote sensing and Cross-Calibrated Multi-Platform (CCMP) wind field data from 2007 to 2019, oceanographic conditions are analysed, respectively, in the Source Area (SA) and Typical Bloom Area (TBA) of Ulva prolifera (U. prolifera) in the west of the Southern Yellow Sea (SYS) using Sea Surface Temperature (SST), Suspended Sediment Concentration (SSC) and Wind Speed over the years. The results indicate that the annual maximum SST Difference (SSTD) between U. prolifera SA and TBA is strongly consistent with the intensity of U. prolifera, and a high SST Warming Rate (WR) from May to July may constrain the U. prolifera blooms. The Taiwan Warm Current (TWC), crossing Yangtze River Estuary northward from March to April, leads to SST increasing in the SA and becomes a key trigger for the growth of U. prolifera in the early period. The amount of U. prolifera may decrease in the early period because of the lower light intensity with high SSC and turbidity in SA. The summer monsoon is one of determinants for the spread of U. prolifera, and the distribution of U. prolifera reaches its highest point with a higher mean wind speed in the TBA.

NOAA-AVHRR Orbital Drift Correction: Validating Methods Using MSG-SEVIRI Data as a Benchmark Dataset

National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometer (NOAA-AVHRR) data provides the possibility to build the longest Land Surface Temperature (LST) dataset to date, starting in 1981 up to the present. However, due to the orbital drift of the NOAA platforms, no LST dataset is available before 2000 and the arrival of newer platforms. Although numerous methods have been developed to correct this orbital drift effect on the LST, a lack of validation has prevented their application. This is the gap we bridge here by using the 15 min temporal resolution of Meteosat Second Generation–Spinning Enhanced Visible and Infra-Red Imager (MSG-SEVIRI) data to simulate drifted and reference LST time series. We then use these time series to validate an orbital drift correction method based on solar zenith angle (SZA) anomalies that we presented in a previous work (C1), as well as two variations of this approach (C0 and C2). Our results show that the C0 method performs better than the two others, although its overall bias absolute value ranges up to 1 K, while standard deviation values remain around 3 K. This is verified for most land covers, for all NOAA platforms, and these statistics remain mostly stable with noise on SZA time series (from 0° to ±10°). With this study, we show that orbital drift correction methods can be thoroughly validated and that such validation should aim toward bias absolute values below 0.1 K and standard deviation values around 1.4 K at coarse spatial resolution. To validate other orbital drift correction approaches, the drifted and reference time series used in this work are freely available for download from the first author’s webpage. This will be the first step toward the building of an orbital-drift-corrected long-term LST dataset.

Space and time variability of meteorological drought in Syria

This study assesses the spatial and temporal characteristics (e.g., frequency, intensity, spatial extent) of meteorological drought in Syria. Specifically, drought was characterized using the observed rainfall data from 36 rain gauges spanning the period between 1990 and 2010 and covering the main climatic regions in Syria (i.e., Mediterranean, arid, semiarid and mountainous). Meteorological drought was assessed using the standardized precipitation index (SPI) at 12-month timescale, allowing for detecting the impacts of climate variability on agricultural droughts. The dominant modes of drought were defined using an S-mode of the principal component analysis. To assess the links between meteorological drought evolution and vegetation greening in Syria, the time series of SPI were correlated with the normalized difference vegetation index (NDVI). Time series of NDVI were retrieved from the remotely sensed National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer (NOAA/AVHRR) sensor at a spatial resolution of 25 km for the common period 1990–2010. Trend analysis suggests a statistically significant increase in the frequency and intensity of drought at 12-month timescale. The observed intensification of meteorological drought is mostly associated with the increase in mild and moderate droughts, relative to extremes and very extreme droughts. Results also suggest a statistically significant decrease (p < 0.05) in vegetation greening over Syria during the study period, especially in the eastern parts of the country. Our results demonstrate that the decrease in vegetation cover can directly be linked to the anomalous drought events, with Pearson’ r coefficients generally above 0.6. This dependency was more highlighted during wintertime for the Mediterranean vegetation and in northeastern portions of the country. Overall, the increase in the frequency and intensity of meteorological drought, combined with a series of unrest and political instability, have drastic impacts on the agricultural sector in Syria, with serious implications for crop yield.

Spatial Evaluation of Droughts Using Selected Satellite-based Indices in the Upper Tana River Watershed, Kenya

Aims: To identify the most appropriate drought indices for the identification and monitoring of historical meteorological and agricultural drought incidences and to explore the spatial characteristics of these droughts.&#x0D; Study design: GIS-based empirical research design.&#x0D; Place and Duration of Study: Upper Tana River Watershed, Kenya drought analysis covering a period of 1981 to 2013.&#x0D; Methodology: National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) provided raster maps for Normalized Difference Vegetation Index (NDVI) agricultural drought index, while GeoClim databased through Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was used for retrieval of raster maps for Standardized Precipitation Index (SPI) meteorological drought index. ArcGIS version 10.3.1 facilitated image enhancement and correction for better visualization and interpretation.&#x0D; Results: Agricultural drought years were in 1983, 1987, 1993, 1996, 2000, 2004, 2005, 2008, and 2009 while meteorological drought years were in 1983, 1984, 1992, 1996, 1999, 2002, 2003, and 2011.&#x0D; Conclusion: Meteorological drought triggered events of agricultural drought. Both droughts showed a widespread pattern and were found to manifest at relatively same intervals during the study period.

Evaluating changes in treeline position and land surface phenology in Sikkim Himalaya

Mountain ecosystems of the Himalaya have warmed more rapidly in recent decades than other areas of the globe. Landscape-level delineation of treeline position and its dynamics can give insight regarding climatic variability. Landsat-2 Multispectral Scanner (MSS), Resourcesat-2 Linear Imaging Self Scanning Sensor (LISS-III) and National Oceanic and Atmospheric Administration- Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived normalized difference vegetation index (NDVI) was used to study the long-term treeline dynamics. The treeline has shifted vertically 301 ± 66 m upward in 37 years at a rate of c. 81 m decade−1. The minimum air temperature has increased at a rate of 0.3 °C decade−1 (p < 0.001) depicting the favourable scenario for treeline growth in this temperature limited ecosystem. The annual cumulative precipitation has decreased at a rate of 206.5 mm decade−1. The length of growing season (LOS) has increased and the start (SOS) and end of growing season (EOS) has got earlier in the 1977 treeline. The elevation shifts and phenological changes of the treeline were observed in the warming scenario.

Long‐term spatial and temporal variations of vegetative drought based on vegetation condition index in China

AbstractInformation concerning the spatial and temporal characteristics of vegetative drought is essential for decision‐making and for many environmental and agricultural applications. This study presents a comprehensive spatio‐temporal analysis of vegetative drought conditions that used the vegetation condition index (VCI) data product obtained from NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer) to reveal the vegetative drought patterns across China from 1981 to 2015. Considering the characteristics of VCI as drought indicators, multiple methods were designed for these analyses, including drought frequency analysis, trend analysis, anomaly index analysis, and Mann‐Kendall testing. The results show that China is a country with high vegetative drought frequency, but that most areas experienced only slight or moderate periods of drought. In addition, drought has obvious seasonal and geographical differences and occurs with greater frequencies in regions that are highly affected by monsoons such as the northern and southern regions—particularly in the spring and autumn. In contrast, vegetative drought frequencies in regions less affected by monsoons such as the Tibetan Plateau and the northwestern regions were much lower except in northern Sinkiang, eastern Inner Mongolia, and southern Tibet. During 1981–2015, the VCI had an increasing trend in most areas of China (except in the southern region, where it decreased), indicating a reduced frequency of vegetative drought throughout the country. Moreover, the trend was wavelike rather than a one‐way change and could be divided into four phases: (1) a slowly increasing phase from 1981 to 1990, (2) an intensively fluctuating phase from 1991 to 2000, (3) a steadily increasing phase from 2001 to 2010, and (4) a slowly decreasing phase after 2010. Mann‐Kendall analysis further suggested that upward trends of the VCI were more evident in spring and autumn than in summer and that both trends mutated in 1997. Specifically, the trend reached the 0.05 level of significance in the spring.

The satellite-measured sea surface temperature change in the Gulf of Finland

ABSTRACTClimate change in Baltic region and in the Gulf of Finland is an accomplished fact in human brains and in science. The purpose of this research is to retrieve quantitative level of changes for sea surface temperature (SST) of the Gulf of Finland. Two space systems National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) provided satellite data about temperature of the sea surface. SST data covers period 1981–2014 and includes 444 monthly data scenes with spatial resolution about 10 km. Data quality analysis displays high reliability of NOAA/AVHRR and Aqua/MODIS satellite information. The Gulf of Finland’s average annual SST has changed from 6.8°C in 1982 up to 8.2°C in 2014. Its mean speed of warming is about 0.04°C year–1. The growth of the temperature was irregular, in the middle of 80th year, the temperature dropped down to 5.0°C, and then sharply increased up to 7.3°C in 1989. SST growth in the Gulf of Finland coincides with air temperature and sea temperature growth. The climate change in the Gulf of Finland has special significance due to the fragility of the northern ecosystems and high anthropogenic load.

Wildfires Dynamics in Siberian Larch Forests

Wildfire number and burned area temporal dynamics within all of Siberia and along a south-north transect in central Siberia (45°–73° N) were studied based on NOAA/AVHRR (National Oceanic and Atmospheric Administration/ Advanced Very High Resolution Radiometer) and Terra/MODIS (Moderate Resolution Imaging Spectroradiometer) data and field measurements for the period 1996–2015. In addition, fire return interval (FRI) along the south-north transect was analyzed. Both the number of forest fires and the size of the burned area increased during recent decades (p &lt; 0.05). Significant correlations were found between forest fires, burned areas and air temperature (r = 0.5) and drought index (The Standardized Precipitation Evapotranspiration Index, SPEI) (r = −0.43). Within larch stands along the transect, wildfire frequency was strongly correlated with incoming solar radiation (r = 0.91). Fire danger period length decreased linearly from south to north along the transect. Fire return interval increased from 80 years at 62° N to 200 years at the Arctic Circle (66°33’ N), and to about 300 years near the northern limit of closed forest stands (about 71°+ N). That increase was negatively correlated with incoming solar radiation (r = −0.95).

Calculation of Solar Radiation by Using Regression Methods

In this study, solar radiation was estimated at 53 location over Turkey with varying climatic conditions using the Linear, Ridge, Lasso, Smoother, Partial least, KNN and Gaussian process regression methods. The data of 2002 and 2003 years were used to obtain regression coefficients of relevant methods. The coefficients were obtained based on the input parameters. Input parameters were month, altitude, latitude, longitude and landsurface temperature (LST).The values for LST were obtained from the data of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite. Solar radiation was calculated using obtained coefficients in regression methods for 2004 year. The results were compared statistically. The most successful method was Gaussian process regression method. The most unsuccessful method was lasso regression method. While means bias error (MBE) value of Gaussian process regression method was 0,274 MJ/m2, root mean square error (RMSE) value of method was calculated as 2,260 MJ/m2. The correlation coefficient of related method was calculated as 0,941. Statistical results are consistent with the literature. Used the Gaussian process regression method is recommended for other studies.

Risk identification of agricultural drought for sustainable Agroecosystems

Abstract. Drought is considered as one of the major natural hazards with a significant impact on agriculture, environment, society and economy. Droughts affect sustainability of agriculture and may result in environmental degradation of a region, which is one of the factors contributing to the vulnerability of agriculture. This paper addresses agrometeorological or agricultural drought within the risk management framework. Risk management consists of risk assessment, as well as a feedback on the adopted risk reduction measures. And risk assessment comprises three distinct steps, namely risk identification, risk estimation and risk evaluation. This paper deals with risk identification of agricultural drought, which involves drought quantification and monitoring, as well as statistical inference. For the quantitative assessment of agricultural drought, as well as the computation of spatiotemporal features, one of the most reliable and widely used indices is applied, namely the vegetation health index (VHI). The computation of VHI is based on satellite data of temperature and the normalized difference vegetation index (NDVI). The spatiotemporal features of drought, which are extracted from VHI, are areal extent, onset and end time, duration and severity. In this paper, a 20-year (1981–2001) time series of the National Oceanic and Atmospheric Administration/advanced very high resolution radiometer (NOAA/AVHRR) satellite data is used, where monthly images of VHI are extracted. Application is implemented in Thessaly, which is the major agricultural drought-prone region of Greece, characterized by vulnerable agriculture. The results show that agricultural drought appears every year during the warm season in the region. The severity of drought is increasing from mild to extreme throughout the warm season, with peaks appearing in the summer. Similarly, the areal extent of drought is also increasing during the warm season, whereas the number of extreme drought pixels is much less than those of mild to moderate drought throughout the warm season. Finally, the areas with diachronic drought persistence can be located. Drought early warning is developed using empirical functional relationships of severity and areal extent. In particular, two second-order polynomials are fitted, one for low and the other for high severity drought classes, respectively. The two fitted curves offer a forecasting tool on a monthly basis from May to October. The results of this drought risk identification effort are considered quite satisfactory offering a prognostic potential. The adopted remote-sensing data and methods have proven very effective in delineating spatial variability and features in drought quantification and monitoring.

SAR-derived wind fields at the coastal region in the East/Japan Sea and relation to coastal upwelling

The relationship between the modification of synthetic aperture radar (SAR) wind field and coastal upwelling was investigated using high-resolution wind fields from Advanced Land Observing Satellite (ALOS) Phased Array type L-band synthetic aperture radar (PALSAR) imagery and sea-surface temperature (SST) from National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA/AVHRR) data. The retrieved SAR wind speeds seem to agree well with in situ buoy measurements with only a relatively small error of 0.7 m s−1. The SAR wind fields retrieved from the east coast of Korea in August 2007 revealed a spatial distinction between near and offshore regions. Low wind speeds of less than 3 m s−1 were associated with cold water regions with dominant coastal upwelling. Time series of in situ measurements of both wind speed and water temperature indicated that the upwelling was induced by the wind field. The low wind field from SAR was mainly induced by changes in atmospheric stability due to air–sea temperature differences. In addition, wind speed magnitude showed a positive correlation with the difference between SST and air temperature (R2 = 0.63). The dependence of viscosity of water on radar backscattering at the present upwelling region was negligible since SAR data showed a relatively large backscattering attenuation to an SST ratio of 1.2 dB °C−1. This study also addressed the important role of coastal upwelling on biological bloom under oligotrophic environments during summer.

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.

Comparison of modelling ANN and ELM to estimate solar radiation over Turkey using NOAA satellite data

In this study, solar radiation (SR) is estimated at 61 locations with varying climatic conditions using the artificial neural network (ANN) and extreme learning machine (ELM). While the ANN and ELM methods are trained with data for the years 2002 and 2003, the accuracy of these methods was tested with data for 2004. The values for month, altitude, latitude, longitude, and land-surface temperature (LST) obtained from the data of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite are chosen as input in developing the ANN and ELM models. SR is found to be the output in modelling of the methods. Results are then compared with meteorological values by statistical methods. Using ANN, the determination coefficient (R2), mean bias error (MBE), root mean square error (RMSE), and Willmott’s index (WI) values were calculated as 0.943, −0.148 MJ m−2, 1.604 MJ m−2, and 0.996, respectively. While R2 was 0.961, MBE, RMSE, and WI were found to be in the order 0.045 MJ m−2, 0.672 MJ m−2, and 0.997 by ELM. As can be understood from the statistics, ELM is clearly more successful than ANN in SR estimation.

Multiparameter seismo-ionospheric anomaly observation before the 2008 Wenchuan, China, Mw7.9 earthquake

The atmosphere-ionosphere response before 2008 Mw7.9 Wenchuan earthquake based on global positioning system (GPS), detection of electro-magnetic emission transmitted from earthquake regions (DEMETER), and National Oceanic and Atmospheric Administration—advanced very high resolution radiometer (NOAA/AVHRR) satellite data is analyzed. It is found that GPS total electron content (TEC) above the epicenter continuously decreased in the afternoon periods from 6 to 10 May but increased in the afternoon of 9 May. Contour analysis on ionospheric plasma using Instrument Sonde de Langmuir data onboard DEMETER satellite showed that electron density (Ne) and ion density (Ni) was also reduced from 6 to 10 May, the anomalous region mainly distributed in the south of the epicenter, conforming to the anomalous TEC area. The brightness temperature (T b ) from NOAA/AVHRR data, calculated by K index method, shows a noticeable enhancement on the northwest side of the epicenter on 7 May, while ion temperature (T i ) from DEMETER data increased on 9 May with the order of ∼12.5% . The energetic particle flux presents an obvious enhancement for the spectrum at 100 to 600 keV energy range on 6 May. Preliminary study suggests the perturbations of these parameters (TEC, Ne, Ni, T b , and T i ) before the Wenchuan earthquake may be related to the changes of vertical electric field in the atmosphere and ionosphere, induced by the enhancement of stress in tectonic regions and the electromagnetic signal propagation from the earthquake preparation area.

Vegetation dynamics of Zimbabwe investigated using NOAA-AVHRR NDVI from 1982 to 2006: a principal component analysis

The dominant modes of vegetation variability over Zimbabwe are investigated using principal component analysis (PCA) on National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) normalized difference vegetation index (NDVI) monthly imagery from 1982 to 2006. Spectral analysis is also used to determine the periodicities of the component loadings. NDVI PCA-1 corresponds to the major vegetation types of Zimbabwe, and we demonstrated that grasslands and dry savannah have the strongest relationship with mean annual precipitation. Furthermore, the March–April loadings showed the highest correlation (r = 0.73) with mean annual precipitation. NDVI PCA-1 sheds some light on the land reform challenge in Zimbabwe. NDVI PCA-2 is highly correlated (r = 0.87) with the mean annual relative variability of the rainfall map indicating a southeast/north mode of anomalies associated with the convectional rainfall-bearing systems over Zimbabwe. NDVI PCA-2 is also highly correlated (r = 0.86) with precipitation PCA-2. NDVI PCA-3 shows a southeast/west mode and is highly correlated (r = 0.87) with precipitation PCA-3. A high correlation (r = 0.66) is also noted between NDVI PCA-4 and the elevation map. Spectral analysis of the PCA loadings revealed several periodicities corresponding to those found in tropical sea surface temperatures (SSTs).

Retrieval of aerosol optical depth and surface reflectance over land from NOAA AVHRR data

An algorithm for the land aerosol and bidirectional reflectance inversion by times series technique (LABITS) is proposed and applied to data from the National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer (AVHRR). The surface reflectance and aerosol optical depth are inverted from AVHRR Channel 1 data using a model for the earth-atmosphere system which couples an atmospheric radiative transfer model with the Ross–Thick–Li-sparse bidirectional reflectance factor (BRF) model. Basic assumptions are that the surface bidirectional reflective property does not vary over a 2–4day periods and that the aerosol characteristics are uniform within a 0.1°×0.1° window (approximately 10km×10km). The LABITS algorithm is applied to data from AVHRR on the NOAA-15, NOAA-16, and NOAA-18 satellites over four distinct areas, namely, North America, Europe, the Sahara and India, to simultaneously retrieve the aerosol optical depth (AOD), BRF parameters and surface albedo. Preliminary results show that AOD and reflectance retrieved from the three different instruments are in good agreement and that LABITS provides good results over both bright surfaces, e.g. the Sahara, and dark surfaces, e.g. Europe. Evaluation of the AOD versus data from the Aerosol Robotic Network (AERONET) provides a correlation coefficient R2 of 0.88 and a root-mean-square error (RMSE) of approximately 0.07; and the uncertainty is approximately Δτ=±0.05±0.20τ. Comparing our results with the moderate resolution imaging spectroradiometer (MODIS) AOD products, over many areas, provides biases in the range of ±0.05. The surface albedo values calculated from the retrieved BRF parameters are similar to those provided by the MODIS albedo product (MCD43). The robustness and applicability of the LABITS algorithm are demonstrated with the retrieval of AOD over China during August 2008. Daily and monthly averaged results show good agreement with collocated AERONET observations and AQUA MODIS products (MYD04 and MYD08). The AOD uncertainty is estimated as Δτ=±0.05±0.30τ. The preliminary analysis of time series over selected AERONET sites shows that the temporal variations of the AOD values retrieved by application of LABITS to AVHRR data are overall similar to temporal variations of AOD provided by the MODIS and AERONET. The algorithm has the potential to retrieve global AOD over land for long time series of NOAA AVHRR data going back to the 1980s, which are urgently needed for studies on aerosol climatology and global climate change.

Estimation of the vapour pressure deficit using NOAA-AVHRR data

In this study, the calculation of vapour pressure deficit (VPD) using the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images were arranged and turned to account for both VPD and land surface temperature (LST), which was necessary to calculate the VPD. The most accurate LST values were obtained from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of 2.7 K, whereas the VPD values were retrieved with an RMSE of 6 mb. Furthermore, the VPD value was calculated on an average monthly basis and its correlation coefficient was found to be 0.991, while the RMSE value was calculated to be 2.67 mb. As a result, VPD can be used in studies that examine plants (germination, growth, and harvest), controlling illness outbreak, drought determination, and evapotranspiration.

VARIABILITY OF CHLOROPHYLL-A CONCENTRATION AND SEA SURFACE TEMPERATURE OF NATUNA WATERS

&lt;p&gt;Variability of chlorophyll-a concentration and sea surface temperature (SST) in Natuna waters were analyzed using satellite data Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR). SeaWiFS data with a resolution of 9×9 km2 and AVHRR with a resolution of 4×4 km2 were the monthly average data downloaded from NASA website. Chlorophyll-a concentrations and SST were estimated using OC4v4 and MCSST algorithms. In general, the concentration of chlorophyll-a in Natuna waters ranged between 0.11-4.92 mg/m3 with an average of 0.56 mg/m3 during the west season and 0.09-2.93 mg/m3 with an average of 0.66 mg/m3 during the east season. Chlorophyll-a concentrations were relatively high seen in coastal areas, especially around the mouth of the Kapuas, Musi, and Batang Hari rivers allegedly caused by the high nutrient intake from the mainland. SST variability in Natuna waters ranged from 23.46-30.88 °C during the west season and tended to be lower than that the east season (27.91-31.95 °C). In addition, the SST values tended to be lower in the offshore than that inshore. During the west season (Nov-Feb) and the transitional season (Apr) in the years of Elnino Southern Oscillation (ENSO), the concentration of chlorophyll-a and the SST in Natuna waters was generally higher than that in non-ENSO years. The results of wind analyses showed that ENSO caused the change of direction and speed of wind from its normal conditions.&lt;/p&gt;&lt;p&gt;Keywords: Sea surface temperature, chlorophyll-a, Natuna waters, ENSO, SeaWiFS, AVHRR&lt;/p&gt;

VARIABILITY OF CHLOROPHYLL-A CONCENTRATION AND SEA SURFACE TEMPERATURE OF NATUNA WATERS

Variability of chlorophyll-a concentration and sea surface temperature (SST) in Natuna waters were analyzed using satellite data Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR). SeaWiFS data with a resolution of 9×9 km2 and AVHRR with a resolution of 4×4 km2 were the monthly average data downloaded from NASA website. Chlorophyll-a concentrations and SST were estimated using OC4v4 and MCSST algorithms. In general, the concentration of chlorophyll-a in Natuna waters ranged between 0.11-4.92 mg/m3 with an average of 0.56 mg/m3 during the west season and 0.09-2.93 mg/m3 with an average of 0.66 mg/m3 during the east season. Chlorophyll-a concentrations were relatively high seen in coastal areas, especially around the mouth of the Kapuas, Musi, and Batang Hari rivers allegedly caused by the high nutrient intake from the mainland. SST variability in Natuna waters ranged from 23.46-30.88 °C during the west season and tended to be lower than that the east season (27.91-31.95 °C). In addition, the SST values tended to be lower in the offshore than that inshore. During the west season (Nov-Feb) and the transitional season (Apr) in the years of Elnino Southern Oscillation (ENSO), the concentration of chlorophyll-a and the SST in Natuna waters was generally higher than that in non-ENSO years. The results of wind analyses showed that ENSO caused the change of direction and speed of wind from its normal conditions.Keywords: Sea surface temperature, chlorophyll-a, Natuna waters, ENSO, SeaWiFS, AVHRR