Application of advanced very high-resolution radiometer (AVHRR)-based vegetation health indices for modelling and predicting malaria in Northern Benin, West Africa.

BACKGROUND: Vegetation health (VH) is a powerful characteristic for forecasting malaria incidence in regions where the disease is prevalent. This study aims to determine how vegetation health affects the prevalence of malaria and create seasonal weather forecasts using NOAA/AVHRR environmental satellite data that can be substituted for malaria epidemic forecasts. METHODS: Weekly advanced very high-resolution radiometer (AVHRR) data were retrieved from the NOAA satellite website from 2009 to 2021. The monthly number of malaria cases was collected from the Ministry of Health of Benin from 2009 to 2021 and matched with AVHRR data. Pearson correlation was calculated to investigate the impact of vegetation health on malaria transmission. Ordinary least squares (OLS), support vector machine (SVM) and principal component regression (PCR) were applied to forecast the monthly number of cases of malaria in Northern Benin. A random sample of proposed models was used to assess accuracy and bias. RESULTS: Estimates place the annual percentage rise in malaria cases at 9.07% over 2009-2021 period. Moisture (VCI) for weeks 19-21 predicts 75% of the number of malaria cases in the month of the start of high mosquito activities. Soil temperature (TCI) and vegetation health index (VHI) predicted one month earlier than the start of mosquito activities through transmission, 78% of monthly malaria incidence. CONCLUSIONS: SVM model D is more effective than OLS model A in the prediction of malaria incidence in Northern Benin. These models are a very useful tool for stakeholders looking to lessen the impact of malaria in Benin.

Monitoring of aerosols and studying its effects on the environment and humans health in Iran.

Natural hazards affect different parts of living organisms. One of these hazards is aerosols. Addressing this issue in areas that suffer from this hazard is of critical importance. Scientific research over the past two decades has shown that aerosol particles are one of the main pollutants from the perspective of public health and health. The purpose of the present study is to monitor and model aerosol temporal and spatial variations in Iran. Aerosols or airborne particles with health effects such as heart, vascular and respiratory diseases are associated. For this purpose, MODIS (or Moderate Resolution Imaging Spectroradiometer) and NOAA (or National Oceanic and Atmospheric Administration) satellite data and BTD (or Brightness Temperature Difference) were used. Given the 20-year study period (2000-2019), the output of much satellite data was divided into four five-year periods to monitor aerosols with high accuracy. The results showed that aerosol values in terms of intensity and frequency of optical depth (AOD) increased over the 20-year time series, and its intensity was higher in the last 5 years. According to the results obtained from the NODIS and NOAA satellite data and comparing their outputs, NOAA satellite data were associated with outliers, which was significantly different from the other cohort data. For this reason, the MODIS satellite image output was used to monitor aerosol images. The innovation of the present study is the use of remote sensing science to monitor the effects of aerosols on the environment and human's health. According to the results from MODIS satellite data, the maximum optical depth (AOD) of the aerosols is for July 2003 with a value of 0.63, but according to the NOAA satellite data output, the maximum optical depth (AOD) of the aerosols is for March 2013 with a value of 2.54. As the values of aerosols increase in frequency and intensity and the areas with the highest intensity of aerosols have been identified, it can be overcome by careful planning of their problems. Areas, where the amount and volume of aerosols were higher, should be observed in health protocols. By preventing and maintaining good hygiene, the negative effects of aerosols can be reduced.

Persistence of marine heat waves for coral bleaching and their spectral characteristics around Andaman coral reef.

Coral reefs are fragile and endangered ecosystems in the tropical marine and coastal environment. Thermal stress due to marine heat waves (MHW) could cause significantly negative impacts on the health conditions, i.e., bleaching of the coral ecosystem. The current study is an attempt to quantify the intensity of coral bleaching in the Andaman region in recent decades using the intensity of marine heat wave (IMHW) estimated from satellite measured sea surface temperature (SST). A linear regression model was developed between IMHW and in situ observations of percent coral bleaching (PCB) which has the slope 7.767 (of IMHW unit) and intercept (- 141.7). Further, an attempt was also made to establish the relationship between PCB and the ratio between the remote sensing reflectance (Rrs) at 443 and 531 nm to upscale the percentage of coral bleaching at synoptic scales. A significant positive correlation between the PCB and band ratio index was found (R2 = 0.72). This approach can be used for the operational monitoring of coral reef beaching in this region.

Early warning systems development for agricultural drought assessment in Nigeria.

The existing drought monitoring mechanisms in the sub-Saharan Africa region mostly depend on the conventional methods of drought monitoring. These methods have limitations based on timeliness, objectivity, reliability, and adequacy. This study aims to identify the spread and frequency of drought in Nigeria using Remote Sensing/Geographic Information Systems techniques to determine the areas that are at risk of drought events within the country. The study further develops a web-GIS application platform that provides drought early warning signals. Monthly NOAA-AVHRR Pathfinder NDVI images of 1 km by 1 km spatial resolution and MODIS with a spatial resolution of 500 m by 500 m were used in this study together with rainfall data from 25 synoptic stations covering 32 years. The spatio-temporal variation of drought showed that drought occurred at different times of the year in all parts of the country with the highest drought risk in the north-eastern parts. The map view showed that the high drought risk covered 5.98% (55,312 km2) of the country's landmass, while low drought risk covered 42.4% (391,881 km2) and very low drought risk areas 51.5% (476,578 km2). Results revealed that a strong relationship exists between annual rainfall and season-integrated NDVI (r2 = 0.6). Based on the spatio-temporal distribution and frequency of droughts in Nigeria, drought monitoring using remote sensing techniques of VCI and NDVI could play an invaluable role in food security and drought preparedness. The map view from the web-based drought monitoring system, developed in this study, is accessible through localhost.

Investigating Volcanic Plumes from Mt. Etna Eruptions of December 2015 by Means of AVHRR and SEVIRI Data.

In early December 2015, a rapid sequence of strong paroxysmal events took place at the Mt. Etna crater area (Sicily, Italy). Intense paroxysms from the Voragine crater (VOR) generated an eruptive column extending up to an altitude of about 15 km above sea level. In the following days, other minor ash emissions occurred from summit craters. In this study, we present results achieved by monitoring Mt. Etna plumes by means of RSTASH (Robust Satellite Techniques-Ash) algorithm, running operationally at the Institute of Methodologies for Environmental Analysis (IMAA) on Advanced Very High Resolution Radiometer (AVHRR) data. Results showed that RSTASH detected an ash plume dispersing from Mt. Etna towards Ionian Sea starting from 3 December at 08:40 UTC, whereas it did not identify ash pixels on satellite data of same day at 04:20 UTC and 04:40 UTC (acquired soon after the end of first paroxysm from VOR), due to a mixed cloud containing SO2 and ice. During 8⁻10 December, the continuity of RSTASH detections allowed us to estimate the mass eruption rate (an average value of about 1.5 × 10³ kg/s was retrieved here), quantitatively characterizing the eruptive activity from North East Crater (NEC). The work, exploiting information provided also by Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, confirms the important contribution offered by RSTASH in identifying and tracking ash plumes emitted from Mt. Etna, despite some operational limitations (e.g., cloud coverage). Moreover, it shows that an experimental RST product, tailored to SEVIRI data, for the first time used and preliminarily assessed here, may complement RSTASH detections providing information about areas mostly affected by volcanic SO2.

Comparison and Evaluation of Annual NDVI Time Series in China Derived from the NOAA AVHRR LTDR and Terra MODIS MOD13C1 Products.

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.

Remote sensing strategies to monitoring land use maps with AVHRR and MODIS data over the South Asia regions.

In South Asia, annual land use and land cover (LULC) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vitally essential to obtain correct information on the LULC in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LULC map of South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using the annual map of the LULC time series, and the space-time dynamics of the LULC map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years show the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrublands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Accuracy, stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue long-term land aerosol retrieval in Asia.

The deep blue (DB) aerosol algorithm applied to four satellite instruments, AVHRR, SeaWiFS, MODIS, and VIIRS, produced a long-term aerosol data set since 1989. This study first evaluated and compared the accuracy, stability, and continuity of four DB aerosol optical depth (AOD) products in Asia using AErosol RObotic NETwork measurements. Then, the regional AOD spatial distributions, coverages, and series trends are analyzed. The results show that VIIRS DB has the highest accuracy and stability, with an expected error (EE, ±(0.05 + 20%)) of 76.59% and stability of approximately 0.027 per decade. The performance of MODIS DB is slightly worse than that of VIIRS. However, their AOD pattern, coverage, and trend are comparable. The performance of AVHRR (EE = 58.10%) and the stability of SeaWiFS (0.093 per decade) are less good. Therefore, SeaWiFS DB data should be used with caution for trend analysis. The AOD accuracy and coverage together determine the AOD pattern and the continuity of multi-sensor data. In addition to consistent algorithm accuracy, it is necessary to consider the influences in sensor sampling and inappropriate-pixel screening schemes in the joint multi-sensor analysis. Encouragingly, although multiple DB products have different AOD averages of regional series, their changing trends are consistent. Error analysis shows that the AOD bias characteristic is different in different surface conditions. This indicates that the surface reflectance estimated by the DB algorithm using different techniques is divergent, which may be the direction for the improvement of the algorithm.

Monitoring land cover change detection with NOAA-AVHRR and MODIS remotely sensed data in the North and West of Africa from 1982 to 2015.

Mapping land cover changes (LCC) cover three decades over North and West Africa regions provides critical insights for the climate research that inspects the land-atmosphere interaction. LCC is a serious problem in the Earth science domain for this impacts the regional climate by modifying the distribution of terrestrial carbon stocking and roughness of the Earth's surface. In this study, the normalized difference vegetation index (NDVI) generated from advanced very high resolution radiometer (AVHRR) was used to produce a continuous set of annual land cover (LC) maps of land cover over North and West Africa between 1982 and 2015, based on the random forest classification. We used the MODIS land cover product (MCD12Q1) as a reference data for training the classifier. The result has validated using annual LC maps listed by time series and the spatio-temporal dynamics of land cover has illustrated over the last three decades. The comparison with Google Earth image 2015 shows that the overall accuracy of the simpler nine-class type of our land cover 2015 map is 76% and 2% higher than that of the MODIS map of the same year. The detection of changes indicated that over the last three decades, the urban and built-up, barren or sparsely vegetated, savannas and deciduous broadleaf forest have increased; in contrast, the open shrublands, woody savannas and water bodies have decreased.

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements.

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG).We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand largescale vegetation growth dynamics above the tree line in the European Alps.

Long-term Satellite NDVI Data Sets: Evaluating Their Ability to Detect Ecosystem Functional Changes in South America.

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR "Normalized Difference Vegetation Index" (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the "Eastern Paraguay" and "Uruguay River margins" focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative "Land ecosystem change utility for South America", which facilitates this type of evaluations and helps to identify the most important functional changes of the continent.

Estimation of Actual Evapotranspiration by Remote Sensing: Application in Thessaly Plain, Greece.

Remote sensing can assist in improving the estimation of the geographical distribution of evapotranspiration, and consequently water demand in large cultivated areas for irrigation purposes and sustainable water resources management. In the direction of these objectives, the daily actual evapotranspiration was calculated in this study during the summer season of 2001 over the Thessaly plain in Greece, a wide irrigated area of great agricultural importance. Three different methods were adapted and applied: the remotesensing methods by Granger (2000) and Carlson and Buffum (1989) that use satellite data in conjunction with ground meteorological measurements and an adapted FAO (Food and Agriculture Organisation) Penman-Monteith method (Allen at al. 1998), which was selected to be the reference method. The satellite data were used in conjunction with ground data collected on the three closest meteorological stations. All three methods, exploit visible channels 1 and 2 and infrared channels 4 and 5 of NOAA-AVHRR (National Oceanic and Atmospheric Administration - Advanced Very High Resolution Radiometer) sensor images to calculate albedo and NDVI (Normalised Difference Vegetation Index), as well as surface temperatures. The FAO Penman-Monteith and the Granger method have used exclusively NOAA-15 satellite images to obtain mean surface temperatures. For the Carlson-Buffum method a combination of NOAA-14 and ΝΟΑΑ-15 satellite images was used, since the average rate of surface temperature rise during the morning was required. The resulting estimations show that both the Carlson-Buffum and Granger methods follow in general the variations of the reference FAO Penman-Monteith method. Both methods have potential for estimating the spatial distribution of evapotranspiration, whereby the degree of the relative agreement with the reference FAO Penman-Monteith method depends on the crop growth stage. In particular, the Carlson- Buffum method performed better during the first half of the crop development stage, while the Granger method performed better during the remaining of the development stage and the entire maturing stage. The parameter that influences the estimations significantly is the wind speed whose high values result in high underestimates of evapotranspiration. Thus, it should be studied further in future.

Using Long-Term Satellite Observations to Identify Sensitive Regimes and Active Regions of Aerosol Indirect Effects for Liquid Clouds Over Global Oceans.

Long-term (1981-2011) satellite climate data records of clouds and aerosols are used to investigate the aerosol-cloud interaction of marine water cloud from a climatology perspective. Our focus is on identifying the regimes and regions where the aerosol indirect effects (AIEs) are evident in long-term averages over the global oceans through analyzing the correlation features between aerosol loading and the key cloud variables including cloud droplet effective radius (CDER), cloud optical depth (COD), cloud water path (CWP), cloud top height (CTH), and cloud top temperature (CTT). An aerosol optical thickness (AOT) range of 0.13 < AOT < 0.3 is identified as the sensitive regime of the conventional first AIE where CDER is more susceptible to AOT than the other cloud variables. The first AIE that manifests as the change of long-term averaged CDER appears only in limited oceanic regions. The signature of aerosol invigoration of water clouds as revealed by the increase of cloud cover fraction (CCF) and CTH with increasing AOT at the middle/high latitudes of both hemispheres is identified for a pristine atmosphere (AOT < 0.08). Aerosol invigoration signature is also revealed by the concurrent increase of CDER, COD, and CWP with increasing AOT for a polluted marine atmosphere (AOT > 0.3) in the tropical convergence zones. The regions where the second AIE is likely to manifest in the CCF change are limited to several oceanic areas with high CCF of the warm water clouds near the western coasts of continents. The second AIE signature as represented by the reduction of the precipitation efficiency with increasing AOT is more likely to be observed in the AOT regime of 0.08 < AOT < 0.4. The corresponding AIE active regions manifested themselves as the decline of the precipitation efficiency are mainly limited to the oceanic areas downwind of continental aerosols. The sensitive regime of the conventional AIE identified in this observational study is likely associated with the transitional regime from the aerosol-limited regime to the updraft-limited regime identified for aerosol-cloud interaction in cloud model simulations.

Evaluation of Grassland Dynamics in the Northern-Tibet Plateau of China Using Remote Sensing and Climate Data.

The grassland ecosystem in the Northern-Tibet Plateau (NTP) of China is verysensitive to weather and climate conditions of the region. In this study, we investigate thespatial and temporal variations of the grassland ecosystem in the NTP using theNOAA/AVHRR ten-day maximum NDVI composite data of 1981-2001. The relationshipsamong Vegetation Peak-Normalized Difference Vegetation Index (VP-NDVI) and climatevariables were quantified for six counties within the NTP. The notable and unevenalterations of the grassland in response to variation of climate and human impact in theNTP were revealed. Over the last two decades of the 20th century, the maximum greennessof the grassland has exhibited high increase, slight increase, no-change, slight decrease andhigh decrease, each occupies 0.27%, 8.71%, 77.27%, 13.06% and 0.69% of the total area ofthe NTP, respectively. A remarkable increase (decrease) in VP-NDVI occurred in thecentral-eastern (eastern) NTP whereas little change was observed in the western andnorthwestern NTP. A strong negative relationship between VP-NDVI and ET0 was foundin sub-frigid, semi-arid and frigid- arid regions of the NTP (i.e., Nakchu, Shantsa, Palgonand Amdo counties), suggesting that the ET0 is one limiting factor affecting grasslanddegradation. In the temperate-humid, sub-frigid and sub-humid regions of the NTP (Chaliand Sokshan counties), a significant inverse correlation between VP-NDVI and populationindicates that human activities have adversely affected the grassland condition as waspreviously reported in the literature. Results from this research suggest that the alterationand degradation of the grassland in the lower altitude of the NTP over the last two decades of the 20th century are likely caused by variations of climate and anthropogenic activities.

A 30+ year AVHRR Land Surface Reflectance Climate Data Record and its application to wheat yield monitoring.

The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980's to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR Surface Reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geo-location, improvement of the cloud masking and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream Leaf Area Index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by [1] and [2] are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980's, the results have errors equivalent to those derived from MODIS.

Assimilation for skin SST in the NASA GEOS atmospheric data assimilation system.

The present article describes the sea surface temperature (SST) developments implemented in the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric Data Assimilation System (ADAS). These are enhancements that contribute to the development of an atmosphere-ocean coupled data assimilation system using GEOS. In the current quasi-operational GEOS-ADAS, the SST is a boundary condition prescribed based on the OSTIA product, therefore SST and skin SST (Ts) are identical. This work modifies the GEOS-ADAS Ts by modeling and assimilating near sea surface sensitive satellite infrared (IR) observations. The atmosphere-ocean interface layer of the GEOS atmospheric general circulation model (AGCM) is updated to include near surface diurnal warming and cool-skin effects. The GEOS analysis system is also updated to directly assimilate SST-relevant Advanced Very High Resolution Radiometer (AVHRR) radiance observations. Data assimilation experiments designed to evaluate the Ts modification in GEOS-ADAS show improvements in the assimilation of radiance observations that extends beyond the thermal IR bands of AVHRR. In particular, many channels of hyperspectral sensors, such as those of the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI) are also better assimilated. We also obtained improved fit to withheld, in-situ buoy measurement of near-surface SST. Evaluation of forecast skill scores show marginal to neutral benefit from the modified Ts.

Spatially and temporally continuous LAI datasets based on the mixed pixel decomposition method.

The leaf area index (LAI) is a key biophysical parameter that determines the state of plant growth. A global LAI has been routinely produced by the Moderate Resolution Imaging Spectro-radiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, the MODIS and AVHRR LAI products cannot be synchronized with the same spatial and temporal resolution. The LAI features are not discernible when a global LAI product is implemented at the regional scale because it has low resolution and different land cover types. To obtain high spatial and temporal resolution of LAI products, an empirical model based on the pixel scale was developed. The approach to generate a long (multi-decade) time series of a 1-km spatial resolution LAI normally integrates both AVHRR and MODIS datasets for different land cover types. In this paper, a regression-based model for generating a vegetation LAI was developed using the AVHRR Global Inventory Modelling and Mapping Studies Normalized Difference Vegetation Index (NDVI), MODIS LAI and land cover as input data; the model was evaluated by using relevant data from the same period data from 2000 to 2006. The results of this method show a good consistency in LAI values retrieved from the AVHRR NDVI and MODIS LAI. This simple method has no specific-limited data requirements and can provide improved spatial and temporal resolution in a region without ground data.

Role of African protected areas in maintaining connectivity for large mammals.

The African protected area (PA) network has the potential to act as a set of functionally interconnected patches that conserve meta-populations of mammal species, but individual PAs are vulnerable to habitat change which may disrupt connectivity and increase extinction risk. Individual PAs have different roles in maintaining connectivity, depending on their size and location. We measured their contribution to network connectivity (irreplaceability) for carnivores and ungulates and combined it with a measure of vulnerability based on a 30-year trend in remotely sensed vegetation cover (Normalized Difference Vegetation Index). Highly irreplaceable PAs occurred mainly in southern and eastern Africa. Vegetation cover change was generally faster outside than inside PAs and particularly so in southern Africa. The extent of change increased with the distance from PAs. About 5% of highly irreplaceable PAs experienced a faster vegetation cover loss than their surroundings, thus requiring particular conservation attention. Our analysis identified PAs at risk whose isolation would disrupt the connectivity of the PA network for large mammals. This is an example of how ecological spatial modelling can be combined with large-scale remote sensing data to investigate how land cover change may affect ecological processes and species conservation.

Woody encroachment and forest degradation in sub-Saharan Africa's woodlands and savannas 1982-2006.

We review the literature and find 16 studies from across Africa's savannas and woodlands where woody encroachment dominates. These small-scale studies are supplemented by an analysis of long-term continent-wide satellite data, specifically the Normalized Difference Vegetation Index (NDVI) time series from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset. Using dry-season data to separate the tree and grass signals, we find 4.0% of non-rainforest woody vegetation in sub-Saharan Africa (excluding West Africa) significantly increased in NDVI from 1982 to 2006, whereas 3.52% decreased. The increases in NDVI were found predominantly to the north of the Congo Basin, with decreases concentrated in the Miombo woodland belt. We hypothesize that areas of increasing dry-season NDVI are undergoing woody encroachment, but the coarse resolution of the study and uncertain relationship between NDVI and woody cover mean that the results should be interpreted with caution; certainly, these results do not contradict studies finding widespread deforestation throughout the continent. However, woody encroachment could be widespread, and warrants further investigation as it has important consequences for the global carbon cycle and land-climate interactions.