Soil moisture is a determinant factor in most of complex environmental processes and has an important role in agricultural drought occurrence. Methods based on remote sensing are preferred to point measurements due to their better spatial and temporal precision. Hence, in this research AMSR-E surface soil moisture data of Aqua satellite have been evaluated and their correlation to five years (2003-2007) precipitation data of two selected synoptic stations in Esfahan province (Esfahan, Natanz) has been studied in 8-day and monthly temporal resolutions. Results indicate of significant correlation between AMSR-E surface soil moisture data and in-situ precipitation data in 90% and 95% confidence levels.

Aim The aim of this study was to investigate the spatio-temporal changes of snow cover and snow water equivalent in Karun, Karkheh and Dez basins and the effect of changes in the discharge of these basins. Methodology In order to extract snow cover and measure snow water equivalent and their correlation with the discharge of Karun, Karkheh and Daz basins, from the thresholding method on reflective and thermal bands, Man Kendal method, Spearman correlation and MODIS measurement data (2000-2020), available images of AMSR-2/AMSR-E sensor (2003-2020) and monthly discharge at the same time with the mentioned satellite images have been used. Findings The results of time series analysis in all three studied basins show a decreasing trend of snow cover area and volume of the snow water equivalent in most months And The most decreasing changes in snow cover area were observed in Dez basins and March with a value of-3. 26 and the most decreasing changes of snow water equivalent were observed in Karun basin and February with a value of-3. 86. The highest correlation in all three basins was related to Dez basin in June with a value of 0. 775 (p<0. 01) and the lowest was related to Karkheh basin in February with a value of 0. 183. Examination of the relationship between discharge and snow water equivalent AMSR-E/AMSR-2 images also showed that the highest correlation was related to Karun Basin in January with a value of 0. 721 (p<0. 01). Conclusion Generally, in the observed years, the area of snow cover and snow water equivalent in all months has decreased and is more severe in Karun and Dez basins. In addition, in most months, there is no significant relationship between snow cover area and basin discharge.

Variation of snow cover area (SCA) in small to large scale catchment can bestudied using MODIS snow products on daily to monthly time step since theyear 2000. However, one of the major problems in applying the MODIS snowproducts is cloud obscuration which limits the utilization of these products. Inthe current study, variation of SCA was investigated in Karoun basin, westernpart of Iran, using MODIS 8-day snow cover product (MOD10A2). More overin order to overcome the cloud barrier in application of snow cover products, asimultaneous employment of the images from both MODIS optical sensor andAMSR-E microwave sensor was recommended. Meeting our target, thecombination of MODIS and AMSR-E daily images was exercised to accomplishsnow cover area in daily interval and afterwards, a comparison was madebetween the result and those which had been obtained by the sole utilization ofeither of them while the weather had been either cloudy and not been overcast. Validation of snow cover gained by combined images was additionallycompared with the discharge of one of the catchments existing in Karoun basin. The results demonstrate that regardless of the fact that microwave data, featuringa coarse spatial resolution, can penetrate the cloud cover, on average, AMSR-Eimages approximately show 16% more snow cover in comparison to MODISimages. The results also illustrate that the correlation existing between snowcover rate of AMSR-E and MODIS images during cloudless days, the differenceof average snow cover area decreases from 16% to 5%. Moreover, the upshot ofvalidation by the exercise of daily discharge data indicates that by possessing acorrelation coefficient of 0. 66, the correlation of snow cover and discharge incombined images features a higher accuracy in comparison to MODIS imageswith a correlation coefficient of 0. 55.

Snow cover, as an important component of land cover, is one of the most active natural and plays an important role in hydrological processes and climate. Variability in snow covered area has a significant influence on water and energy cycles, as well as socioeconomic and environmental repercussions. Frequent and long-term snow observation, accurate snow cover (SC) mapping and snow water equivalent (SWE) estimation are crucial for operational flood control, water delay planning, and resource management in snowmelt-dominated basins. Today, satellite-derived snow products obtained from visible and infrared imagery, as well as passive microwaves are available on the Internet, with few recently availability in near-real time. Optical sensors (e. g., Landsat, Advanced Very High Resolution Radiometer-AVHRR, Moderate Resolution Imaging Spectroradiometer-MODIS, Systeme Probatoire d’ Observation de la Tarre-SPOT) have been well developed to provide snow information with good temporal and/or spatial resolution. But, cloud cover is a major factor in optical remote sensing that limits our capability to map the Earth’ s surface. It is often not an easy task to collect a time-series cloud-free images for a particular area of interest, using optical remote sensing, which limits their wider applications for SC monitoring. Space-borne passive-microwave radiometers (e. g., Scanning Multichannel Microwave Radiometer-SMMR, Special Sensor Microwave/Imager-SSM/I, Advanced Microwave Scanning Radiometer-Earth Observing System-AMSR-E), can penetrate cloud to detect microwave energy emitted by snow and ice and provide information on SC and SWE. These passive microwave data are well suited to snow cover monitoring because of the characteristics such as all-weather imaging, large swath width with frequent overpass times. But, the coarse spatial resolution (e. g., 25 km of AMSR-E daily SWE product) hinders their applications in operational hydrological modeling. It seems that the combination of MODIS and AMSR-E can take advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data. In this study, daily cloud-free SC and SWE maps at the 500-m resolution were produced for Karun watershed, Ahwaz, Iran (January 25– 32, 2004). The daily MODIS-Terra, MODIS-Aqua, and AMSR-E snow data products were used via a fusion-disaggregation algorithm. The developed SC and SWE maps were evaluated, using total accuracy of snow mapping in clear-sky (Os) and all-sky (Oa) conditions, underestimation (UEc) and Overestimation (OEc) of snow covered area in clear-sky condition, snow accuracy in clear-sky (Sc) or in all-sky (Sa) conditions, and no snow accuracy in clear-sky (NSc) or in all-sky (NSa) conditions. The results of this study showed that the combination of MODIS-Terra and MODIS-Aqua considerably reduced the cloud coverage in such high resolution optical data. Although MAC-SC and MAC-SWE products have been developed to have 500 m spatial resolution, the massive and continuous cloud cover (larger than 25 km in size) in the MODIS-Terra and MODIS-Aqua and, hence, TAC products were simply replaced by the coarse AMSR-E pixels. In this case, although those cloud coverages were removed in the MAC-SC and MAC-SWE products, the actual resolution of the snow or no-snow pixels kept 25 km and such pixels had the false spatial resolution of 500 m. The SWE redistribution of AMSR-E based on MAC-SC products enhanced (to some extent) the spatial resolution of the AMSR-E SWE products. However, there was no measured data to evaluate the accuracy of the enhanced SWE products. It can be concluded that for pixels with scattered cloud cover (less than 25 km in size) in the TAC products, the MAC-SC and MAC-SWE products indeed improve the spatial resolution of those pixels to 500 m, while for massive cloud cover (larger than 25 km in size), the actual resolution of those pixels in the MAC-SC and MAC-SWE products are 25 km, even in 500 m pixel size. Despite of these limitations, the MAC-SC and MAC-SWE maps are suitable for hydrological, meteorological modeling on a daily basis in the study area.

Background: Cutaneous leishmaniasis (CL) is a dermal and parasitic disease. . The aim of this study was to determine the effect of environmental and climate factors on spatial distribution of CL in northeastern Iran by utilizing remote sensing from 20 March 2016 to 19 March 2017. Methods: In this ecological study, the data were divided into two parts: The descriptive data on human CL cases were gathered from Communicable Diseases center of Iran. The remote sensing techniques and satellite imagery data (TRMM, MODIS-Aqua, MODIS-Terra and AMSR-2 with spatial resolution 0. 25° , 0. 05° , 5600m and 10km) of environmental and climate factors were used to determine the spatial pattern changes of cutaneous leishmaniasis incidence. Results: The incidence of CL in North Khorasan, Razavi Khorasan, and South Khorasan was 35. 80 per 100, 000 people (309/863092), 34. 14 per 100, 000 people (2197/6, 434, 501) and 7. 67 per 100, 000 people (59/768, 898), respectively. The incidence of CL had the highest correlation with soil moisture and evapotranspiration. Moreover, the incidence of disease was significantly correlated with Normalized Difference Vegetation Index (NDVI) and air humidity while it had the lowest correlation with rainfall. Furthermore, the CL incidence had an indirect correlation relation with the air temperature meaning that with an increase in the temperature, the incidence of disease decreased. Conclusion: As such, the incidence of disease was also higher in the northern regions; most areas of North Khorasan and northern regions of Razavi Khorasan; where the rainfall, vegetation, specific humidity, evapotranspiration, and soil moisture was higher than the southern areas.

Low-light images often suffer from low brightness and contrast, which makes some scene details hard to see. This can affect the performance of many computer vision tasks, such as object recognition, tracking, scene understanding, and occlusion detection. Therefore, it is important and useful to enhance low-light images. One technique to enhance low-light images is based on the Retinex theory, which decomposes images into two components: reflection and illumination. Several mathematical models have been recently developed to estimate the illumination map using this theory. These methods first compute an initial illumination map and then refine it by solving a mathematical model. This paper introduces a novel method based on the Retinex theory to estimate the illumination map. The proposed method employs a new mathematical model with a differentiable objective function, unlike other similar models. This allows us to use more diverse methods to solve the proposed model, as classical optimization methods such as Newton, Gradient, and Trust-Region methods need the objective function to be differentiable. The proposed model also has linear constraints and is convex, which are desirable properties for optimization. We use the CPLEX solver to solve the proposed model, as it performs well and exploits the features of the model. Finally, we improve the illumination map obtained from the mathematical model using a simple linear transformation. This paper introduces a new method based on the Retinex theory for enhancing low-light images. The proposed method improves the illumination and the visibility of the scene details. We compare the performance of our method with six existing methods: AMSR, NPE, SRIE, DONG, MF, and LIME. We use four common metrics to evaluate the visual quality of the enhanced images: AMBE, LOE, SSIM, and NIQE. The results demonstrate that our method is competitive with many of the state-of-the-art methods for low-light image enhancement.