trmm

In order to carry out this research, the daily and three-hourly data of precipitation parameters and wind speed and direction were used for a period of 13 years (2009-2021) for 11 Ardabil synoptic stations. Percentile 95% free days were obtained for each station. For statistical analysis and consensus, the condition of choosing a representative day for each station was the simultaneous occurrence of rain and wind as a criterion, and for the sake of brevity, an event was analyzed at Meshkinshahr station. Using TRMM satellite NETCDF data, precipitation density was displayed in GIS software, and wind direction was shown using Golbad software, and synoptic maps were drawn using NCEP data. The statistical results for Meshkinshahr station showed the maximum wind frequency with 56% in the winter season. And the month of March occurred, which often entered the city from the south, but most of the rain was in spring with (41%) and May was the rainiest month. The results of synoptic analysis showed that the prevailing system over the studied area was low pressure over the Aral Lake and the rotating conditions of the air ascent followed. In the east of the Mediterranean Sea and the north of the Black Sea, high-pressure systems have formed, and with their outward movement, they have led the western currents towards the region. Also, the studied area is located in front of the descent axis in the west of Iran, and the positive rotation of the air has led to the strengthening of the divergence in the vorticosphere. The speed of the horizontal movement of the air has reached the speed of the wind and by containing the moisture of the Mediterranean Sea, it has injected 1.2 grams per kilogram of moisture into the atmosphere of the region.

Drought is a complex phenomenon caused by the breaking of water balance and it has always an impact on agricultural, ecological and socio-economic spheres. Although the drought indices deriving from remote sensing data have been used to monitor meteorological or agricultural drought, there are no indices that can suitably reflect the comprehensive information of drought from meteorological to agricultural aspects. In this study, the synthesized drought index (SDI) as a synthesized index from the vegetation condition index (VCI), temperature condition index (TCI) and precipitation condition index (PCI) were used for comprehensive drought monitoring in the Urmia Lake Basin (ULB) based on the Principal Component Analysis (PCA). For this purpose, MOD13A3, MOD11A2 and TRMM 3B43 data series were downloaded y for the period of 2001–2012. After initial processing, drought indicators were calculated using LST NDVI and TRMM data, and monthly drought severity maps were prepared. In order to validate SDI index, the Correlation relationship between SDI and SPI indices was obtained in the 3 month period during the growing season. As well as, SDI correlation relationships were investigated with wheat and barley crop yields. The results indicate that drought occurred in 2008 and 2001 in the ULB. The results of validation show that there is a correlation of 80% between the two SDI and SPI indicators. Also, the results of this study showed that the SDI index, as a comprehensive index of drought monitoring, reflects the effects of drought on agriculture.

Human activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential for taking measures and making decisions based on the goals of sustainable water and soil resources management. Over the past decade, many researchers around the world have been attracted to remote sensing and especially satellite remote sensing and used this technology to detect such changes over time. The present study has used Landsat (monitoring the area of water body), TRMM (monitoring rainfall), MODIS (monitoring vegetation and evapotranspiration), Grace (monitoring groundwater) satellite images available in Google Earth Engine to study last two decades changes (from 2000 to 2019) in Maharloo wetland, Goshnegan catchment and their surroundings. 

Maharloo wetland is located in Fars province and Goshnegan catchment (426 square kilometers). The present study has used Landsat 7 and 8 images to extract the area of water body, TRMM images to obtain precipitation values, MODIS products to calculate NDVI and evapotranspiration, and data received from Grace to extract changes in groundwater level. These satellite images were available in Google Earth Engine. Mann-Kendall test was also used to assess the overall trend of the aforementioned factors. 

The automated water extraction index was used in the present study to identify and estimate the area covered by ​​water bodies in the study area. The largest area belonged to 2006 (216.76 square kilometers) and the smallest belonged to 2018 (66 square kilometers). In 2000 (the beginning of the reference period), an area of ​​216.52 square kilometers was covered by this wetland which is close to what was observed in 2006. In 2018, this has reduced to 66 square kilometers. Thus, there is about 150.72 square kilometers (69.54 percent) difference between these two years. In 2009, the total area has reduced to 66.67 square kilometers. A numerical comparison between 2000 and 2019 also indicates a reduction of 91.17 square kilometers (42% decrease) in the total area covered by this wetland. Also, a 53.72 square kilometers (29.60%) difference was observed between the average area covered by the water body in the first and second ten years. Since calculated p-value value (< 0.00001) is less than the alpha level (0.05), so a significant trend was observed in the average annual data of the area covered by this wetland. Kendall's tau also indicated declining trend of the collected data. Groundwater level was calculated using data received from Grace Satellite to investigate the role of groundwater level in reducing the area covered by the ​​water body. Results indicated that since 2008, groundwater level ​​have always showed a negative value (a decreasing trend). For an instance, a groundwater level of -10.86 cm in 2019 indicates a decrease in the water level in the study area. As the calculated p-value (< 0.0001) is less than the alpha level (0.05), so a significant decreasing trend was observed in the groundwater level. Results of Mann-Kendall test (-0.6) also indicated that changes in water bodies, vegetation, rainfall and groundwater level had a decreasing, increasing, increasing and decreasing trend, respectively. No significant trend was observed in evapotranspiration. It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. 

Wetlands provide many ecological services including water treatment, natural hazard prevention, soil and water protection, and coastline management (Amani et al., 2019). Therefore, understanding the importance of wetlands and their management need to be seriously considered by relevant organizations in different countries of the world, and Iran is no exception. Satellite data and remote sensing methods and techniques are considered to be one of the most important and cost-effective methods of monitoring wetlands. The present study used satellite data collected by Landsat, MODIS, Grace, and TRMM to monitor water bodies, vegetation, groundwater level, and rainfall in Goshnegan catchment in which Maharloo wetland is located. The results of Mann-Kendall test showed a decreasing annual trend for changes in the average area of ​​this wetland. This decreasing trend is considered to be a serious threat to human settlements around the wetland which can intensify over time. It will also affect the thermal islands of Shiraz and Sarvestan in near future. Obviously, management of agricultural and forest land uses with the aim of stopping their increasing trend can improve water balance in catchment areas. A 132.2 ha (approximately 36.16%) difference was observed between the average vegetation cover in this catchment area over the first and second ten years (233.4 vs. 365.6 ha). It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. Due to the proximity of this wetland to the city of Shiraz and its importance as an ecological and tourist attraction, it is suggested that related authorities (Department of Environment and Water Organization) demarcate lake bed and riparian zone with the help of remote sensing researchers to improve the management of this wetland and prevent it from drying up. Also, it is suggested that the Organization of Agriculture Jihad review and improve water consumption methods and cultivation patterns in the areas surrounding this wetland.

Floods are a natural occurrence that causes casualties, livestock losses and damage to buildings, facilities, gardens, fields and natural resources every year. Therefore, rainfall estimates have long been considered by researchers in various fields, and along with the advancement of science and the emergence of new technologies, many advances have been made in the methods of rainfall estimation and evaluation and validation to achieve the best method. In the last twenty years, there has been a lot of progress in rainfall estimation methods. This advancement is due to the possibility of using a lot of information from different parts of the world, better understanding of atmospheric phenomena, exchanges and atmospheric rotations, improving the performance of models, progress in various surveillance tools such as radar and satellite and computer power. The methods used to estimate precipitation, especially in the short term, have shortcomings and are generally based on numerical forecasting models or the use of empirical analyzes, which are usually not very accurate for multi-hour intervals, so the use of satellite data It has been recommended as a supplement to address this problem, and doing so could greatly help increase the accuracy of numerical models for rainfall estimates.

The study used the physical properties of a cloud of five waves between 2011 and 2015. The data of the second generation of MSG meteorological satellite has good coverage on different regions of Iran. The satellite has 12 channels on the region and produces accurate products. Some of these products are in line with the physical properties of the cloud used in this study. These products are produced daily every 15 minutes and include cloud peak pressure (CTP), cloud peak temperature (CTT), cloud light depth (COT), thermodynamic cloud phase (CPH), and the volume of water in the cloud. Density (CWP) are the effective radius of cloud droplets (REFF) and cloud type (CT). Was obtained. The criterion for the accuracy of the calculations was the two MAE statistics Equation 1: Equation 2:

In this study, TRMM satellite data was considered as control data. After receiving TRMM images in MATLAB software environment, programming was performed and precipitation data were extracted from NETCDF files. After extracting TRMM satellite data, Meteosat satellite products were prepared through the CMSAF database and their data were extracted using MATLAB software code. In the study of waves, the coefficient of determination in the GPR model was 0.72 in the experimental section and 0.77 in the training section. In the TD model, the determination coefficient is calculated in the experimental section 0.64 and in the training section 0.87. However, in the neural network model, the coefficient of determination is 0.68 in the experimental section and 0.72 in the training section. The results show a good relationship between the components studied. Investigating the Effects of Cloud Physical Properties: One of the methods for determining the effectiveness of each of the physical properties of the cloud in estimating rainfall is the sensitivity analysis method. After calculating the coefficient of determination and the error coefficient, the sensitivity of each of the physical properties in estimating the precipitation was performed by the method of calculating the sensitivity analysis. Sensitivity analysis was calculated for all waves. Calculations show that the cloud type is most effective, followed by the effective radius of the cloud droplets and then the optical depth of the cloud in the second and third positions, respectively. Among the physical properties studied, the lowest effect is related to the cloud phase. To investigate the relationship between the physical characteristics of the cloud and the amount of precipitation, five waves of pervasive precipitation were selected between 2011 and 2015. Rainfall data from the region's stations were extracted. In order to validate the TRMM data, a comparison was made between the precipitation data of the selected stations and the precipitation of this satellite. Metoost satellite products were used to extract the physical properties of the cloud. After extracting the data, the physical properties of the cloud were matched to the time scale of the data and evaluated using TRMM satellite rain as a control.

The selection criteria were such that the waves lasted for at least two days and covered the entire area. On the day of the operation, the precipitation information of the meteorological stations of the region was obtained and also the precipitation information of TRMM satellite was extracted. In order to validate the data of TRMM satellite, the information of meteorological stations was compared with TRMM precipitation and obtained the necessary correlation. In order to get a better result, the matching of numbers was done in terms of time scale. In the next step, using the meteosat satellite products, the physical properties of the cloud were obtained for all waves. Data were extracted at all stages for each pixel. Then the data correlation matrix was performed with three models of GPR, TD and MLPBR, the results of which are given in Table One. Due to the use of different models as well as the study of 8 physical properties of the cloud, the results show a high relationship between the components of the study, so that the coefficient of determination in the GPR model for the experimental and training sections was 0.7 and 0.77, respectively. These coefficients for the TD model in the experimental and training sections are 0.64 and 0.87, respectively. In the artificial network model (MLPBR), the coefficients obtained in the experimental and training sections are 0.68 and 0.72, respectively. The numbers obtained indicate a relatively good relationship between the components. Sensitivity analysis was performed. Sensitivity analysis results show that the cloud type feature has the greatest effect on precipitation and then the effective radius of cloud droplets and then cloud light depth are in the second and third positions, respectively. Among the physical properties studied, the lowest effect is related to the cloud phase.

Drought is an important phenomenon which can be monitored based on weather data obtained from weather stations and remote sensing data. Remote sensing methods have offered significant relative advantages compared to the other methods for monitoring drought . Also , several drought indicators have provided in remote sensing for monitoring drought , but none of the common indicators in remote sensing did not have generalizability of time , climate and altitude and it is necessary the performance quality of these indexes 1) in climates, 2) in altitudinal zoning examined .This study also proved this hypothesis , to identify appropriate indicators in every altitudinal zone , and in every region the index considered the appropriate season to evaluate indexes . In this study , drought indices ,VCI ,VDI ,TCI and TVDI by LST parameter , NDVI and EVI have been evaluated. To evaluate climate and altitudinal indicators , first in the whole country and then in Hamadan province , climate and altitudinal zoning done and drought indexes for different climates and altitude was determined in two forms pixel-based and object-based (polygons) and compared to precipitation data TRMM sensors . The operation of drought indexes were analyzed to drought evaluation by taking account climate type , data acquisition season , altitude and area . The results of this research shows lack of generalizability of all indictors in terms of climate , altitude and time indicators and for example , in pixel evaluating of hot and dry climate , the highest correlation between VCI index and precipitation data was in June and the lowest correlation is in December.

The aim of this study was to evaluate the capability of remote sensing data for drought monitoring of Urmia Lake Basin (ULB). For this purpose, the MODIS NDVI/LST data and TRMM satellite rainfall data were downloaded on monthly scale from 2000 to 2014. After data processing, drought indices including Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Precipitation Condition Index (PCI), Composite indices include CI1, CI2, CI3 and Vegetation Health Index (VHI) were calculated for March to September (growing season). To validate the mentioned indicators, Standardized Precipitation Index (SPI) on different time scales (3, 6 and 9 months) was used. Results showed the VCI is the favorable satellite index for monitoring agricultural drought of the ULB during the growing season. SPI3, SPI6 and SPI9 have the highest correlation with the VCI and VHI in March, with PCI and CI1 in April, with TCI, CI3 and CI1 in May, with VCI in June and July, with TCI and VCI in August and September, respectively. Composite indicators presented better results than VCI and TCI in May and April. Therefore it can be concluded that composite indices can provide better results during the wet months (May-April). Also, Drought Severity classification results of the ULB based on VCI showed that highest drought with 93.85, 91.93 and 72 percent in 2000, 2008 and 2001, respectively, and the lowest drought with 1.21, 2.06 and 5.33 percent in 2010, 2007 and 2004 respectively has happened during the growing season (March to September) in the ULB. Using the optimum satellite indices presented in the present study, it is possible to predict and estimate the drought phenomena of Urmia Lake BASIN in different months of the growing season or during the growing season and obtain a better result.

Rainfall is one of the most important elements to determine the climate. Therefore, it is important to estimate its value accurately. The main purpose of this study is the evaluation of the TRMM (Tropical Rain Measurement Mission) 3B42 rainfall estimates, an exponential model and conceptual cloud model in Lake Urmia Basin. Therefore, this study focuses on the comparison of these methods to identify and select the most appropriate model for rainfall estimation in Lake Urmia Basin. The comparison are performed during the period 2007 to 2011 and the hourly rainfall, temperature, barometric pressure and dew point temperature, the three-hourly rainfall rate of TRMM 3B42-V6 at 0.25° resolution and thermal infrared images (TIR) of Meteosat 7 at six-hour intervals are used. The results indicated acceptable match of estimated rainfall with rain-gauge data. Comparison of three methods of rainfall estimation shows that exponential model has the determination coefficient (equal to 0.61). In addition to the high correlation, due to low levels of RMSE and MAE (respectively 1.58 and 1.01), has a good performance to estimate rainfall in this basin. Therefore, this model can introduced as the most appropriate model for estimating rainfall in Lake Urmia basin.

1. Precise estimates of rainfall in areas with complex geographical features in the field of
climatology, agricultural meteorology and hydrology is very important. TRMM satellite
is the first international effort to measure rainfall from space reliably (Smith, 2007).
Another set of data that has become available in recent years is the output of numerical
prediction models. Akter and Islam (2007) used MM5 model for weather prediction
especially for rainfall in Bangladesh. They compared MM5 outputs with 3B42RT
production of TRMM, rain gage and radar data and concluded that MM5 is reliable for
rainfall prediction. Ochoa et al. (2014) compared 3B42 product of TRMM with
simulated rainfall data by WRF model. Their results showed that TRMM data is more
applicable for presenting spatial distribution of annual rainfall. In addition to the
methods of statistical comparison, the similarity algorithm (Herzfeld & Merriam, 1990)
was also used in this study. This algorithm compares a large number of data
simultaneously, which can be in the form of maps or models output. In Iran, very few
studies have compared the output of numerical prediction models with TRMM products
of rainfall. The aim of this study was to evaluate and compare the rainfall data using
similarity algorithm for different locations and time periods in order to fill a gap in the
space-time data.
2. Material and The study area consisted of North Khorasan, Khorasan Razavi and South Khorasan
provinces in North East of Iran, which is geographically located between the longitudes
of 55 to 61 degrees and latitudes of 30 to 38 degrees. The climate of the area is arid and
semi arid. Total area is approximately 313000 square kilometers. In this study, three
types of data were used. Ground-based observations used from synoptic and rain-gauge
stations of Meteorology Organization. The seventh series products of TRMM 3B42 sensor containing three hours TRMM rainfall data with a spatial resolution of 0.25
degree were downloaded for free from the site of NASA. MM5 model outputs which
were in the form of images with a spatial resolution of 0.5× 0.5 degrees for the period of
2000-2010 were also obtained from NASA and NOAA .In this study, KED as a
geostatistical method was used to interpolate rainfall. For running geostatistics
algorithms, GS and ArcGIS software were used. Similarity algorithm was executed
for each grid point map and the similarity values were derived. After standardization by
calculating the similarity value for the entire study area, F network model for similar
map was created. In similarity algorithm, closest values to zero indicate a good
similarity between the input maps in a specific location and higher values indicate
weaker similarity. Standardization algorithms, similarity and analytical software
programming in MATLAB were performed for each grid point of the map.
3. RMSE values for MM5 model were higher in the warm months. The highest RMSE
values were obtained in late spring and early summer. This result proved that in the
summer, rainfall was predicted less accurately than in the cold months in winter. RMSE
values for TRMM showed a reverse pattern with MM5 model output. Maximum
amount of RMSE for TRMM was obtained in January with 14 mm per month. The
reason for this may be because microwave energy scattering from frozen ice on the
ground. The scattering from rain or frozen rain in the atmosphere is similar. Similarity
values in the area were scattered with uniform distribution that represents the least
significant inter-annual variation is cold seasons. For the warm seasons, in the south and
north of the area, similarity values vary from 1 to 2. Results showed that inter-annual
variations of rainfall in warm seasons and in central areas is high. One of the reasons for
these results can be errors in the observed data.
By examining the time series of TRMM images using similarity algorithm, we found
that in the cold season, the south zone of the study area had similarity values 0.05 to 0.1
with a uniform distribution of values. However, higher similarity values were obtained
for the northern and central areas where the distribution of similarity values was not
uniform.
Due to these facts, it can be concluded that rainfall production of TRMM data was
relatively good in the cold season in south and relatively week in north and central parts
of the region. In the warm season the least amount of similarity could be seen in the
northeast part of the study area. But generally, TRMM estimated rainfall fairly in the
warm season.
4. The validation results of MM5 model rainfall and TRMM monthly rainfall images
showed that the model predicted rainfall amounts in the cold months better than in the
warm months. However unlike the MM5 model, remote sensing images had the highest
error in cold months. The reason was the presence of snow and ice on the ground in the
cold months of winter. Considering inter-annual and seasonal changes, it became clear
that there is much difference between inter-annual remote sensing image changes and the actual amounts of rainfall (KED). Nevertheless the model inter-annual changes were
consistent with real data. Inter-annual changes of the model and the station data (KED)
were higher in cold season.
KED methods also retained spatial variability of rainfall as well as remote sensing data
and model output. The estimates, especially above 1500 meters in the central regions,
had low precision in the products. The results showed that in the absence of adequate
rain gages in the region, MM5 output model and TRMM data could be used to fill the
gaps.

Rainfall prediction at regional and global scales is mostly as principle component of hydro-meteorological studies in un-gauged regions. Ground-based measurements of precipitation are available with high accuracy in synoptic stations. Spatial distribution of operational stations is now as one of the biggest problems in the developing countries such as Iran, which the spatial distribution of stations are not enough. In recent decades, remote sensing data have been widely used by many researchers in the world for drought monitoring and management of water resources. The satellites data can be utilized as compensation for temporal and spatial distribution of rainfall. The satellite-based rainfall estimates provided by the Tropical Rainfall Measuring Mission (TRMM) satellite at global scale, are now available freely as only data source at regions without in-situ measurements. Most regions of Iran have arid and semi-arid climates. The evaluation and calibration of TRMM data in different regions of Iran at daily and monthly time scales is very important before those data are used by researchers, experts, climate scientist, hydrologist, etc. Therefore, a comprehensive evaluation and calibration of the TRMM 3B43 and 3B42 dataset at 87 synoptic stations in Iran including six climatic zones, is the main objective of present research.
This research was carried out in Iran. It is located between 44˚14’ to 63˚20 E longitude and 25˚03’ to 39˚47 N latitude, with an area of more than 1.6 million Km2. Alijani et al. (2008) classified Iran’s climate according to climatological parameters to six separate climatic classes: desert, semi desert, mountainous, semi- mountainous, coastal wet and coastal desert. This study aims to evaluate the accuracy of the Tropical Rainfall Measuring Mission (TRMM) satellite and its calibration on the daily, monthly, seasonal and annual scales at the synoptic stations located in climate zones of Iran. The daily TRMM-3B42 and monthly TRMM-3B43 collection data were downloaded from the NASA website and processed. After early processing, a comparative analysis was carried out for satellite data and observed rainfall data at 87 synoptic stations during a 12-year data period of 2009-1998. The Desert, semi desert, mountain, semi-mountain, coastal desert and coastal wet climate zones are included 22, 19, 19, 12, 8 and 7 stations, respectively. We utilized different error measures (R, ME, MAE and RMSE), and agreement indices (POD, FAR, CSI and TSS) for satellite data evaluation. Since there were noticeable errors, regional mean data were calibrated in the daily and monthly scales and finally two correction coefficients were introduced based on regression analysis.
Day-to-day rainfall comparisons showed that the TRMM rainfall estimates are very similar to the observed data values, even if a general overestimation in the satellite products must be highlighted. We found out a high similarity between two sources of rainfall data at 87 synoptic stations in most of climatic zones. Furthermore, The TRMM showed the highest error at Ramsar, Bandar Anzali, Rasht and Babolsar stations, and the lowest errors at Zahedan, Bam and Esfahan stations. In other words, the TRMM revealed the highest error in coastal wet zone and the lowest error in desert zone. The False Alarm ratio (FAR) indicator has the lowest amount in coastal wet zone that shows TRMM applicability to predict rainfall amount at these stations. The highest correlation coefficients at 0.01 significance level on monthly and daily scales, were 0.86 and 0.998 in the semi mountainous zone, respectively, while the lowest values as 0.49 and 0.78 were in the humid zone, respectively. After applying the calibration coefficients, The RMSE values were significantly reduced at monthly scale. This indicates that the calibrated TRMM data is mostly similar with observed rainfall data at different time scales and climatic zones.
In recent years, the accurate measurement of precipitation and its spatial and temporal distribution frequently at un-gauged regions have been addressed in the world. At present, the estimation of rainfall by the TRMM satellite is only data source, which is available freely at global scale. The main purpose of present study is to evaluate the TRMM rainfall data and to provide the correction coefficients in desert, semi-desert, mountainous, semi-mountainous, coastal wet and coastal desert climatic zones, on daily and monthly scale. The main advantage of this work is to apply various statistical error criteria and newly introduced agreement indicators, to evaluate TRMM data. The results reveal that the TRMM overestimates rainfall on daily and monthly scales at 68% of stations. In general, The TRMM could detect most of rainy days in the climate zone and Iran during 1998-2009 period. The calibrated data were very similar with the measured values. Therefore, our research findings showed that the calibration process could improve rainfall estimates at most of climatic zones, significantly.

Drought monitoring and assessment is usually done through either ground observation or remote sensing. Due to having some limitations, gathering and analyzing ground observations are a time-consuming and expensive way to approach a precise drought monitoring and assessment. In contrast, remote sensing represents a fast and economic way of monitoring, but an applicable approach needs to be developed. To this end, using satellite sensor data which are continuously available provides cost-effective data for a better understanding of the region. They can be used to detect the drought commencement, duration and magnitude. Tropical Rainfall Measuring Mission monthly data (TRMM-3B43) and Monthly Normalized Difference Vegetation Index (NDVI) data of the MODIS on Terra satellite are freely available for this objective. The main objectives of the present study, which was carried out in the Fars Province, Iran, were: 1. integrating the satellite data for mapping drought severity classes using the Standardized Precipitation Index (SPI) and the NDVI anomaly maps, 2. creating drought risk maps, 3. calculating the percentage of drought affected area by drought risk level, 4. showing the effectiveness of satellite derived drought indices as an indicator for drought assessment, and5. identifying the most drought vulnerable areas of the surveyed region. This research was carried out in Fars Province, Iran. It is located between 50􀃛30’ and 5 􀃛36 E longitude and from 27􀃛03’ to 31􀃛42 N latitude and cover an approximate area of 122661 km2. This study aimed to map drought risk area in the Fars Province, by integrating the Standard Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) Anomaly methods. As the first step, the growing season-based SPI (April- September) at 44 stations were calculated for 2000-2008 period using the standard normal distribution. The SPI raster layer (for each year), was created using the ordinary Kriging method. Then, all SPI maps were reclassified into five drought severity classes. As the second step, NDVI anomaly maps were created for the growing season based-NDVI anomaly of MODIS during the same period (9-year period). The NDVI anomaly map in each year was reclassified into five classes in a similar way. At the next part, for both methods, Boolean drought frequency map (presence or absence of drought) derived for each year. The derivation of final drought risk map was done by a simple weighted linear combination of the drought frequency maps. In this research, another drought risk map was created by integrating the NDVI anomaly and the TRMM-based SPI maps to introduce a new remote sensing method. The ground-based SPI method applied for the growing seasons showed that in 2000, 2001, 2005 and 2008, some severe droughts occurred whereas the NDVI anomaly resulted in 2000, 2001 and 2008. The drought severity maps of TRMM based on SPI method indicated some noticeable drought occurrences in the Fars Province in 2000, 2005, and 2008 as well. The comparison of drought risk maps created by the TRMM-based SPI and the ground-based SPI methods showed that the majority of the surveyed regions are highly prone to drought occurrence. The TRMM could predict the monthly rainfall at most of 44 rain-gauge stations. Comparing drought risk maps, the high and moderate risk classes in the first method contain % 59.58 and % 39.84, while in the TRMM based method, they cover %61.1 and %37.12 of the area, respectively. Before drought risk assessment, it is highly recommended to evaluate the TRMM data for future events. The risk maps can be compared with the actual decrease in agricultural products for a better understanding of the events and their verifications. The method applied in this study showed that almost whole the province is prone to drought occurrences. The northern and southern areas of the province were more susceptible to drought with different severities during the growing seasons in 2000-2008. It is notable to express that there are still some limitations to apply the satellite data for a long period. These might be data availability problem with moderate spatial resolution. The TRMM and the MODIS data have been available since 2000 and 1998, respectively. Furthermore, the TRMM data calibration and validation is required before creating the TRMM-based SPI maps. Despite their shortages, the application of remote sensing data for drought risk assessment can still be done as an acceptable method in ungauged regions.

It is important to achieve accurate monthly precipitation field for climatology and evaluation simulated result of regional models. Because of high temporal and spatial precipitation variability، as well as limited rain gauges، improvements in precipitation estimates using satellite images is critical. Some projects have initiated in this purpose and many products have been produced. Monthly 3B43 product of TRMM project is one of them that have been evaluated in this paper. Precision of monthly and annually 3B43 product have been evaluated during 2001-2003 using Weather Organization synoptic and climatology station data. Result showed that usually values less than average have been overestimated and values more than average have been underestimated. Also precision of values less than average is lower. Nevertheless there has seen no correlation between precision of this product with thermal and humidity characteristics of mounts. Besides، precision amount is various in different zones of Iran، so that in South of Alborz mountain range، central and somewhat Eastern regions of Iran it is incredible; while in Western and Southern regions it is credible. Because precision of 3B43 product is lower than precision of Kriging interpolation technique estimates، thus utilize of this product is not recommended.