Moderate Resolution Imaging Spectroradiometer Sensor Research Articles

Satellite Assessment of Early-Season Forecasts for Vegetation Conditions of Grazing Allotments in Nevada, United States

The extent and heterogeneity of rangelands in the state of Nevada (United States) pose a challenging situation for land managers when determining stocking levels for livestock grazing. Overutilization can cause lasting environmental damage, while underutilization can create unnecessary economic hardship for livestock operators. An improved ability to forecast vegetation stress later in the growing season would allow resource managers to better manage the tradeoffs between ecological and economic concerns. This research maps how well growing season conditions for vegetation within grazing allotments of Nevada can be predicted at different times of the year by analyzing 15 yr of enhanced vegetation index (EVI) data from the Moderate Resolution Imaging Spectroradiometer sensor, cumulative monthly precipitation, and the Palmer drought severity index. Land cover classes within the grazing allotments that are not relevant to grazing were removed from the analysis, as well as areas that showed > 50% change in EVI since these likely represented transitions or disturbances that were not related to interannual climate variability. The datasets were gridded at spatial resolutions from 4 to 72 km, and the correspondence between image and meteorological datasets was found to improve as measurements were averaged over larger areas. A 16-km sampling grid was judged to provide the best balance between predictive ability and spatial precision. The average R2 of regressions between the vegetation index and meteorological variables within each of the 16-km grid cells was 0.69. For most of Nevada, the ability to predict vegetation conditions for the entire growing season (February–September) generally peaks by the end of May. However, results vary by region, with the northeast particularly benefiting from late-season data. Regressions were performed with and without very wet years, and the ability to make early predictions is better when including wet years than in dry to typical conditions.

Size-dependent validation of MODIS MCD64A1 burned area over six vegetation types in boreal Eurasia: Large underestimation in croplands

Pollutants emitted from wildfires in boreal Eurasia can be transported to the Arctic, and their subsequent deposition could accelerate global warming. The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product is the basis of fire emission products. However, uncertainties due to the “moderate resolution” (500 m) characteristic of the MODIS sensor could be introduced. Here, we present a size-dependent validation of MCD64A1 with reference to higher resolution (better than 30 m) satellite products (Landsat 7 ETM+, RapidEye, WorldView-2, and GeoEye-1) for six ecotypes over 12 regions of boreal Eurasia. We considered the 2012 boreal Eurasia burning season when severe wildfires occurred and when Arctic sea ice extent was historically low. Among the six ecotypes, we found MCD64A1 burned areas comprised only 13% of the reference products in croplands because of inadequate detection of small fires (<100 ha). Our results indicate that over all ecotypes, the actual burned area in boreal Eurasia (15,256 km2) could have been ~16% greater than suggested by MCD64A1 (13,187 km2) when applying the correction factors proposed in this study. This implies the effects of wildfire emissions in boreal Eurasia on Arctic warming could be greater than currently estimated.

Unmanned aerial system nadir reflectance and MODIS nadir BRDF-adjusted surface reflectances intercompared over Greenland

Abstract. Albedo is a fundamental parameter in earth sciences, and many analyses utilize the Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/albedo (MCD43) algorithms. While derivative albedo products have been evaluated over Greenland, we present a novel, direct comparison with nadir surface reflectance collected from an unmanned aerial system (UAS). The UAS was flown from Summit, Greenland, on 210 km transects coincident with the MODIS sensor overpass on board the Aqua and Terra satellites on 5 and 6 August 2010. Clear-sky acquisitions were available from the overpasses within 2 h of the UAS flights. The UAS was equipped with upward- and downward-looking spectrometers (300–920 nm) with a spectral resolution of 10 nm, allowing for direct integration into the MODIS bands 1, 3, and 4. The data provide a unique opportunity to directly compare UAS nadir reflectance with the MODIS nadir BRDF-adjusted surface reflectance (NBAR) products. The data show UAS measurements are slightly higher than the MODIS NBARs for all bands but agree within their stated uncertainties. Differences in variability are observed as expected due to different footprints of the platforms. The UAS data demonstrate potentially large sub-pixel variability of MODIS reflectance products and the potential to explore this variability using the UAS as a platform. It is also found that, even at the low elevations flown typically by a UAS, reflectance measurements may be influenced by haze if present at and/or below the flight altitude of the UAS. This impact could explain some differences between data from the two platforms and should be considered in any use of airborne platforms.

Estimation and Prediction of Grassland Cover in Western Mongolia Using MODIS-Derived Vegetation Indices

Spectral indices derived from satellite observations, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), are widely used for grassland monitoring and management around the globe. In this study we contrasted performance of NDVI and EVI metrics obtained from Aqua and Terra, the two satellite platforms carrying the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, for estimating grassland cover measured at ground level on ninety-two 1 × 1 km blocks distributed from semidesert to high montane grasslands in the Sailugem Range of western Mongolia, where overgrazing and overstocking of domestic livestock are concerns for pastureland management. We also explored utility of late spring (May) vegetation indices for forecasting vegetation cover at the peak of the growing season (July). Vegetation indices developed using MODIS 1-km monthly data (MOD13A3 and MYD13A3) were strongly related to on-the-ground field estimates of the percentage of vegetation cover in July (74−85% variation explained), with second-order polynomial regressions demonstrating better fit to the data than first-order regressions, Aqua vegetation indices (VIs) explaining slightly more variance than Terra’s VIs, and NDVI performing comparably to EVI for both Aqua and Terra. Both Aqua and Terra VIs for May were highly predictive of July vegetation cover (R2 = 0.80−0.84). We conclude that monthly MODIS NDVI and EVI datasets can be useful for rangeland managers in western Mongolia to monitor and predict summer pasture conditions at the regional level, where science-based guidance on grazing policy and practices is much needed.

Global Performance of a Fast Parameterization Scheme for Estimating Surface Solar Radiation From MODIS Data

A fast parameterization scheme named SUNFLUX is first used in this paper to estimate instantaneous surface solar radiation (SSR) based on products from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard both Terra and Aqua platforms. The scheme mainly takes into account the absorption and scattering processes due to clouds, aerosols, and gas in the atmosphere. The estimated instantaneous SSR is evaluated against surface observations obtained from seven stations of the surface radiation budget network (SURFRAD), four stations in the North China Plain (NCP) and 40 stations of the baseline surface radiation network (BSRN). The statistical results for evaluation against these three data sets show that the relative root-mean-square error (RMSE) values of SUNFLUX are less than 15%, 16%, and 17%, respectively. Daily SSR is derived through temporal upscaling from the MODIS-based instantaneous SSR estimates, and is validated against surface observations. The relative RMSE values for daily SSR estimates are about 16% at the seven SURFRAD stations, four NCP stations, 40 BSRN stations, and 90 China Meteorological Administration (CMA) radiation stations. The accuracy of the scheme is generally higher than those of previous algorithms, and thus can be potentially applied on geostationary satellites for mapping high-resolution SSR data in the future.

CHLOROPHYLL-A CONCENTRATIONS ESTIMATION FROM AQUA-MODIS AND VIIRS-NPP SATELLITE SENSORS IN SOUTH JAVA SEA WATERS

This study aimed to estimate the concentration of chlorophyll-a from satellite imagery of National Polar-Orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) in the south Java Sea waters and compare it to the concentrations of chlorophyll-a estimation result from the MODIS-Aqua satellite. NPP satellite had Visible/Infrared Imager Radiometer Suite (VIIRS) sensors which performance was same as Moderate Resolution Imaging Spectroradiometer (MODIS) sensor with a better spatial resolution. This study used daily satellite imagery of VIIRS-NPP for the period of September 2012 to August 2013. The algorithm that was used to estimate the concentration of chlorophyll-a was Ocean Color 3-band ratio (OC-3). The results showed that the spatial distribution pattern of chlorophyll-a concentration between VIIRS - NPP sensor and MODIS had the same pattern, but the estimation of chlorophyll-a concentration from the MODIS sensor was higher than VIIRS -NPP sensor. The concentration of chlorophyll-a showed that there were spatial and temporal variation in the south Java Sea waters. Generally, concentrations of chlorophyll-a was higher in East monsoon than West monsoon.

Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble

ABSTRACTThe objective of this study is to predict the sugarcane yield in São Paulo State, Brazil, using metrics derived from normalized difference vegetation index (NDVI) time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and an ensemble model of artificial neural networks (ANNs). Sixty municipalities were selected and spectral metrics were extracted from the NDVI time series for each municipality from 2003 to 2012. A neural network wrapper with sequential backward elimination was applied to remove irrelevant and/or redundant features from the initial data set, reducing over-fitting and improving the prediction performance. Afterwards the sugarcane yield was predicted using a stacking ensemble model with ANN. At the predicted yield, the relative root mean square error (RRMSE) was 6.8% and the coefficient of determination (R2) was 0.61. The last three months were removed from the initial time-series data set to forecast the final sugarcane yield, and the process was repeated. The feature selection (FS) improved again the prediction performance and Stacking improved the FS results: RRMSE increased to 8% and R2 to 0.43. The yield was also estimated for the entire State, based on the average of the 60 selected municipalities, which were compared to the official data surveys. The Stacking method was able to estimate the sugarcane yield for São Paulo State with a smaller RMSE than the official data surveys, anticipating the crop forecast by three months before the harvest.

Using MODIS Cloud Regimes to Sort Diagnostic Signals of Aerosol-Cloud-Precipitation Interactions.

Coincident multi-year measurements of aerosol, cloud, precipitation and radiation at near-global scales are analyzed to diagnose their apparent relationships as suggestive of interactions previously proposed based on theoretical, observational, and model constructs. Specifically, we examine whether differences in aerosol loading in separate observations go along with consistently different precipitation, cloud properties, and cloud radiative effects. Our analysis uses a cloud regime (CR) framework to dissect and sort the results. The CRs come from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and are defined as distinct groups of cloud systems with similar co-variations of cloud top pressure and cloud optical thickness. Aerosol optical depth used as proxy for aerosol loading comes from two sources, MODIS observations, and the MERRA-2 re-analysis, and its variability is defined with respect to local seasonal climatologies. The choice of aerosol dataset impacts our results substantially. We also find that the responses of the marine and continental component of a CR are frequently quite disparate. Overall, CRs dominated by warm clouds tend to exhibit less ambiguous signals, but also have more uncertainty with regard to precipitation changes. Finally, we find weak, but occasionally systematic co-variations of select meteorological indicators and aerosol, which serves as a sober reminder that ascribing changes in cloud and cloud-affected variables solely to aerosol variations is precarious.

Evaluation of radiative fluxes over the north Indian Ocean

Radiative fluxes are a key component of the surface heat budget of the oceans. Yet, observations over oceanic region are sparse due to the complexity of radiation measurements; moreover, certain oceanic regions are substantially under-sampled, such as the north Indian Ocean. The National Institute of Ocean Technology, Chennai, India, under its Ocean Observation Program has deployed an Ocean Moored Network for the Northern Indian Ocean (OMNI) both in the Arabian Sea and the Bay of Bengal. These buoys are equipped with sensors to measure radiation and rainfall, in addition to other basic meteorological parameters. They are also equipped with sensors to measure sub-surface currents, temperature, and conductivity from the surface up to a depth of 500 m. Observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the National Aeronautics and Space Administration (NASA) AQUA and TERRA satellites have been used to infer surface radiation over the north Indian Ocean. In this study, we focus only on the shortwave (SW↓) fluxes. The evaluations of the MODIS-based SW↓ fluxes against the RAMA observing network have shown a very good agreement between them, and therefore, we use the MODIS-derived fluxes as a reference for the evaluation of the OMNI observations. In an early deployment of the OMNI buoys, the radiation sensors were placed at 2 m above the sea surface; subsequently, the height of the sensors was raised to 3 m. In this study, we show that there was a substantial improvement in the agreement between the buoy observations and the satellite estimates, once the sensors were raised to higher levels. The correlation coefficient increased from 0.87 to 0.93, and both the bias and standard deviations decreased substantially.

Spectral–Spatial Adaptive Area-to-Point Regression Kriging for MODIS Image Downscaling

The moderate resolution imaging spectroradiometer (MODIS) sensor contains 36 bands at spatial resolutions of 250 m (e.g., bands 1–2), 500 m (e.g., bands 3–7), and 1000 m (e.g., bands 8–36). The first seven bands covering the visible to midinfrared wavelengths have been used widely for monitoring the Earth's surface. However, 500 m MODIS bands 3–7 present challenges for use in land cover/land use applications, as many land cover features on the Earth's surface possess complex structures with a spatial resolution finer than 500 m. Fusing MODIS 250 m bands 1–2 and 500 m bands 3–7 is an attractive proposition, that is, increasing the spatial resolution of bands 3–7. The geostatistical based downscaling approach, area-to-point regression kriging (ATPRK), has shown great potential for MODIS image downscaling. However, it considers the global relationship between bands 1–2 and each of bands 3–7 to select a 250 m PAN-like band from bands 1–2, which may not take full advantage of both bands 1 and 2. In this paper, a new geostatistical downscaling method of spectral–spatial adaptive ATPRK (SSAATPRK) is proposed for MODIS image downscaling. Both fine spatial resolution bands (i.e., bands 1 and 2) are used as the input to SSAATPRK, and there is no need to choose a PAN-like band for each coarse band, as in the original ATPRK method. SSAATPRK was compared to four benchmark image fusion methods, including principal component analysis, high-pass filtering, ATPRK, and adaptive ATPRK (AATPRK), using one synthetic MODIS image experiment and two real MODIS image experiments. Both visual and quantitative evaluations demonstrated that SSAATPRK produced results consistently with the greatest amount of spatial detail and the largest accuracy. Furthermore, SSAATPRK inherits completely the advantages of ATPRK and AATPRK, while extending them for MODIS image downscaling.

Estimating evaporative fraction from readily obtainable variables in mangrove forests of the Everglades, U.S.A.

ABSTRACTA remote-sensing-based model to estimate evaporative fraction (EF) – the ratio of latent heat (LE; energy equivalent of evapotranspiration –ET–) to total available energy – from easily obtainable remotely-sensed and meteorological parameters is presented. This research specifically addresses the shortcomings of existing ET retrieval methods such as calibration requirements of extensive accurate in situ micrometeorological and flux tower observations or of a large set of coarse-resolution or model-derived input datasets. The trapezoid model is capable of generating spatially varying EF maps from standard products such as land surface temperature () normalized difference vegetation index (NDVI) and daily maximum air temperature (). The 2009 model results were validated at an eddy-covariance tower (Fluxnet ID: US-Skr) in the Everglades using and NDVI products from Landsat as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Results indicate that the model accuracy is within the range of instrument uncertainty, and is dependent on the spatial resolution and selection of end-members (i.e. wet/dry edge). The most accurate results were achieved with the from Landsat relative to the from the MODIS flown on the Terra and Aqua platforms due to the fine spatial resolution of Landsat (30 m). The bias, mean absolute percentage error and root mean square percentage error were as low as 2.9% (3.0%), 9.8% (13.3%), and 12.1% (16.1%) for Landsat-based (MODIS-based) EF estimates, respectively. Overall, this methodology shows promise for bridging the gap between temporally limited ET estimates at Landsat scales and more complex and difficult to constrain global ET remote-sensing models.

Sub-pixel analysis to enhance the accuracy of evapotranspiration determined using MODIS images

A study was carried out to estimate the actual evapotranspiration (ET) over a 1074 km2 of the humid area of Perak State (Malaysia), where water and evaporation cycle deeply influences the climate, natural resources and human living aspects. Images from both Terra and Aqua platforms of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor were used for ET estimation by employing the Surface Energy Balance Algorithm for Land (SEBAL) model. As a part of the accuracy assessment process, in-situ measurements on soil temperature and reference ET (ET0) were recorded at the time of satellite overpass. In order to enhance the accuracy of the generated ET maps, MODIS images were subjected to sub-pixel analysis by assigning weights for different land surface cover (urban, agriculture and multi-surface areas) reflections. The weighting process was achieved by integrating ET from pure pixels with the respective site-specific ET0 of each land cover. The enhanced SEBAL model estimated ET exhibited a good correlation with the in-situ measured Penman-Montieth ET0, with R2 values for the Aqua and the Terra platforms of 0.67 and 0.73, respectively. However, the correlation of the non-enhanced ET maps resulted in R2 values of 0.61 and 0.68 for the Aqua and the Terra platforms, respectively. Hence, the results of this study revealed the feasibility of employing the sub-pixel analysis method for an accurate estimation of ET over large areas. Keywords: evapotranspiration, sub-pixel analysis, MODIS image, MODIS sensor, remote sensing, land surface cover DOI: 10.3965/j.ijabe.20171002.2890 Citation: Hassaballa A A, Matori A, Al-Gaadi K A, Tola E H, Madugundu R. Sub-pixel analysis to enhance the accuracy of evapotranspiration determined using MODIS images. Int J Agric & Biol Eng, 2017; 10(2): 103–113.

The Influence of Land Use on the Grassland Fire Occurrence in the Northeastern Inner Mongolia Autonomous Region, China.

Grassland, as one of the most important ecosystems on Earth, experiences fires that affect the local ecology, economy and society. Notably, grassland fires occur frequently each year in northeastern China. Fire occurrence is a complex problem with multiple causes, such as natural factors, human activities and land use. This paper investigates the disruptive effects of grassland fire in the northeastern Inner Mongolia Autonomous Region of China. In this study, we relied on thermal anomaly detection from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to identify fire occurrences, and land use data were acquired by Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM). We discussed the relationship between land use and the spatial distribution of grassland fires. The results showed that the impact of land use on grassland fires was significant. Spatially, approximately 80% of grassland fires were clustered within 10 km of cultivated land, and grassland fires generally occurred in areas of intense human activity. The correlation between the spatial distribution of grassland fires and the land use degree in 2000, 2005 and 2010 was high, with R2 values of 0.686, 0.716, 0.633, respectively (p < 0.01). These results highlight the importance of the relationship between land use and grassland fire occurrence in the northeastern Inner Mongolia Autonomous Region. This study provides significance for local fire management and prevention.

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis

ABSTRACTThis study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM10), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM2.5) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM2.5 (r2 = 0.62), and the two-variable (AOD-PM2.5) model predicted PM2.5 (r2 = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM2.5. For the relevant period in 2008, in Roda, VA, the predicted PM2.5 mass concentration is 9.11 ± 5.16 μg m-3 (mean ± 1SD).Implications: This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or “hollows,” where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.

Flood mapping in the lower Mekong River Basin using daily MODIS observations

ABSTRACTIn flat homogenous terrain such as in Cambodia and Vietnam, the monsoon season brings significant and consistent flooding between May and November. To monitor flooding in the Lower Mekong region, the near real-time NASA Flood Extent Product (NASA-FEP) was developed using seasonal normalized difference vegetation index (NDVI) differences from the 250 m resolution Moderate Resolution Imaging Spectroradiometer (MODIS) sensor compared to daily observations. The use of a percentage change interval classification relating to various stages of flooding reduces might be confusing to viewers or potential users, and therefore reducing the product usage. To increase the product usability through simplification, the classification intervals were compared with other commonly used change detection schemes to identify the change classification scheme that best delineates flooded areas. The percentage change method used in the NASA-FEP proved to be helpful in delineating flood boundaries compared to other change detection methods. The results of the accuracy assessments indicate that the −75% NDVI change interval can be reclassified to a descriptive ‘flood’ classification. A binary system was used to simplify the interpretation of the NASA-FEP by removing extraneous information from lower interval change classes.

Global warming and local air pollution have reduced wheat yields in India

We use regression analysis on data from 208 districts over the period 1981–2009 to examine the impact of temperature and solar radiation (affected by pollution from aerosols) on wheat yields in India. We find that a 1 °C increase in average daily maximum and minimum temperatures tends to lower yields by 2–4% each. A 1% increase in solar radiation increases yields by nearly 1%. Yields are estimated to be about 5.2% lower than they would have been if temperatures had not increased during the study period. We combine the estimated impacts of weather on yield with the estimated impacts of aerosol pollution (measured by moderate resolution imaging spectroradiometer sensor in terms of aerosol optical depth, aerosol optical depth (AOD) in 2001–2013) on weather to compute the net impact of reducing aerosol pollution on wheat yields. A one-standard-deviation decrease in AOD is estimated to increase yields by about 4.8%. Our results imply reducing regional pollution and curbing global warming in the coming decades can counter wheat yield losses.

Modeling gross primary production of paddy rice cropland through analyses of data from CO2 eddy flux tower sites and MODIS images

Accurate information on the gross primary production (GPP) of paddy rice cropland is critical for assessing and monitoring rice growing conditions. The eddy co-variance technique was used to measure net ecosystem exchange (NEE) of CO2 between paddy rice croplands and the atmosphere, and the resultant NEE data then partitioned into GPP (GPPEC) and ecosystem respiration. In this study, we first used the GPPEC data from four paddy rice flux tower sites in South Korea, Japan and the USA to evaluate the biophysical performance of three vegetation indices: Normalized Difference Vegetation Index (NDVI); Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) in terms of phenology (crop growing seasons) and GPPEC, which are derived from images taken by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. We also ran the Vegetation Photosynthesis Model (VPM), which is driven by EVI, LSWI, photosynthetically active radiation (PAR) and air temperature, to estimate GPP over multiple years at these four sites (GPPVPM). The 14 site-years of simulations show that the seasonal dynamics of GPPVPM successfully tracked the seasonal dynamics of GPPEC (R2>0.88 or higher). The cross-site comparison also shows that GPPVPM agreed reasonably well with the variations of GPPEC across both years and sites. The simulation results clearly demonstrate the potential of the VPM model and MODIS images for estimating GPP of paddy rice croplands in the monsoon climates of South Korea and Japan and the Mediterranean climate in California, USA. The application of VPM to regional simulations in the near future may provide crucial GPP data to support the studies of food security and cropland carbon cycle around the world.

MODIS Environmental Data to Assess Chikungunya, Dengue, and Zika Diseases Through Aedes (Stegomia) aegypti Oviposition Activity Estimation

Aedes aegypti is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a main threat for our region. Taking into account this situation, several efforts have been done to use remote sensing to support public health decision making. Moderate resolution imaging spectroradiometer (MODIS) sensor provides moderate-resolution remote sensing products; therefore, we explore the application of MODIS products to vector-borne disease problems in Argentina. We develop temporal forecasting models of Ae. aegypti oviposition, and we include its validation and its application to the 2016 Dengue outbreak. Temporal series (10/2005 to 09/2007) from MODIS products of normalized difference vegetation index and diurnal land surface temperature were built. Two linear regression models were developed: model 1 which uses environmental variables with time lag and model 2 uses environmental variables without time lags. Model 2 was the best model (AIC = 112) with high correlation ( $r$ = 0.88, p Ae. aegypti oviposition activity and risks for Ae. aegypti -borne diseases. That statement is also supported by model results for 2016 when a dengue outbreak that started unusually earlier this season. If such activity could be forecast by a model based on remote sensing data, then a potential outbreak could be predicted.

Monitoring Drought Trends Induced Climate Variability over Egypt Using MODIS NDVI Satellite Data and Drought Indices

The Drought is considered as a regional phenomenon because the main causes that can produce it are linked to the regional scale. Empirical studies showed that drought is never the result of a single cause. It is the result of many causes, often synergistic in nature (Magana et al. 1997, Estrada, 2001, Contreras, 2003, Vicente-Serrano et al., 2012, Rita Man Sze Yu, 2013, Joan Ramon, 2013 and Vicente-Serrano et al., 2014). Climatological events over the past three decades have prompted many researchers to pay closer attention to drought across the arid and semi-arid regions such as Egypt. Climate variability leads to more intense and more frequent droughts in the arid regions especially in Egypt. Drought is a recurring extreme climate event over land characterized by below normal precipitation over a period of several months to several years or even a few decades. The main aim of this research is to monitor the drought trends and classification induced by climate variability over Egypt using Terra-MODIS/ NDVI satellite data in companion with Drought Indices. To achieve the aim of the study the climate variability over Egypt is described by the analysis of temperature and rainfall at annual and seasonal time-scale. The drought trends and classifications, which are approached by the application of the most widely, used drought indices Self-Calibrated of Palmer’s Drought Severity Index (Sc-PDSI) which improves the “original” PDSI, Standardized Precipitation Evapotranspiration Index (SPEI) which is based on the difference between the precipitation and the reference evapotranspiration, and Standardized Precipitation Index (SPI) which is based on precipitation data over Egypt 1940-2014. These climatic indices based on rainfall and temperature data, and it has the advantage of combining multi-scalar character with the capacity to include the effects of the temperature variability on drought monitoring. A trend analysis is carried out on drought time-series in order to understand the behavior of drought conditions. Furthermore, the research was depended on the analysis of temperature and rainfall variability from 1940 to 2014 at annual and seasonal time-scale. Moreover, the trends and their statistical significance are also analyzed. This analysis provides relevant information related to the behavior of the main climate variables of Egypt under the current climate change conditions. Remote sensing NDVI data provided by Moderate Resolution Imaging Spectroradiometer (MODIS) Sensor, and Terra-MODIS/NDVI data available from 2000 until 2016, it was used to monitor drought in Egypt. The results accentuated that SPEI drought index does not work properly in all stations of Egypt due to the lack of rainfall compared to really high potential evapotranspiration causes an optimal calibration of the index and, in consequence, SPEI will not be a good tool to monitor and analysis dry events in Egypt. In a country like Egypt where reliable data are difficult to obtain continuous, remote sensing data proved to be a significant tool in monitoring and detecting drought components.

Snow season variability in a boreal-Arctic transition area monitored by MODIS data

The duration and extent of snow cover is expected to change rapidly with climate change. Therefore, there is a need for improved monitoring of snow for the benefit of forecasting, impact assessments and the population at large. Remotely sensed techniques prove useful for remote areas where there are few field-based monitoring stations. This paper reports on a study of snow season using snow cover area fraction data from the two northernmost counties in Norway, Troms and Finnmark. The data are derived from the daily 500 m standard snow product (MOD10A1) from the NASA Terra MODerate Resolution Imaging Spectroradiometer (MODIS) sensor for the 2000–2010 period. This dataset has been processed with multi-temporal interpolation to eliminate clouds. The resulting cloud-free daily time series of snow cover fraction maps, have subsequently been used to derive the first and last snow-free day for the entire study area. In spring, the correlation between the first snow-free day mapped by MODIS data and snow data from 40 meteorological stations was highly significant (p < 0.05) for 36 of the stations, and with a of bias of less than 10 days for 34 of the stations. In autumn, 31 of the stations show highly significant (p < 0.05) correlation with MODIS data, and the bias was less than 10 days for 27 of the stations. However, in some areas and some years, the start and end of the snow season could not be detected due to long overcast periods. In spring 2002 and 2004 the first snow-free day was early, but arrived late in 2000, 2005 and 2008. In autumn 2009 snowfall arrived more than 7 days earlier in 50% of the study area as compared to the 2000–2010 average. MODIS-based snow season products will be applicable for a wide range of sectors including hydrology, nature-based industries, climate change studies and ecology. Therefore refinement and further testing of this method should be encouraged.