Showing 8 results for Modis
B. Rayegani, S. J. Khajeddin, S. Soltani , S. Barati,
Volume 12, Issue 44 (7-2008)
Abstract
Snow is a huge water resource in most parts of the world. Snow water equivalent supplies 1/3 of the water requirement for farming and irrigation throughout the world. Water content estimation of a snow-cover or estimation of snowmelt runoff is necessary for Hydrologists. Several snowmelt-forecasting models have been suggested, most of which require continuous monitoring of snow-cover. Today monitoring snow-cover patches is done through satellites imagery and remote sensing methods. MODIS have smaller Spatial Resolution and more bands in comparison with Meteorology Satellite like NOAA. Therefore, in this research we used MODIS data for creating snow cover imagery. Existence of cloud in the study area is a major problem for snow cover monitoring. Therefore, in this research snow cover area changes were estimated without MODIS data period, but with DEM imagery and regressions between temperature, height and aspect. For this purpose, on 10 Esfand when the image was suitable we estimated the snow cover area. In comparison with real image, precision of the method was confirmed.
S. H. Sanaienejad, A. R. Shah Tahmasbi, R. Sadr Abadi Haghighi, K. Kelarestani,
Volume 12, Issue 45 (10-2008)
Abstract
Remote sensing science and satellite data are widely used by researchers for agricultural studies. Vegetation spectral reflections recorded by satellite sensors have been used extensively for identifying plant types, plant cover, health community of plants and predicting yield. The TERRA satellite, with 5 sensors, provides an opportunity to observe land, atmosphere and ocean characteristics. The Moderate Resolution Imaging Spectroradiometer (MODIS) is
on–board TERRA satellite. This sensor with 36 bands by 250m, 500m, and 1000m spatial resolution help us to study our environment. The MODIS vegetation indices are used to monitor photosynthetic activity radiation, change detection in plant communities, planted area estimation and plant health. A statistical analysis was done to analyze Near Infra Red (NIR) (841-876 nm) and Red (R) (620-670 nm) bands of MODIS images for a 16 day period. The images have been used for winter wheat in Mashhad (North East of IRAN) during agricultural season of 2004-05.Some image processing techniques were used to extract the related digital numbers (DN), showing the electromagnetic spectrum reflection for all of the pixels. The analysis shows a positive correlation between R and NIR spectrum (0.70 and 0.69) and decrease in NDVI (0.18 and 0.24) in the first and late wheat growth season. However, there is not such a good correlation in the middle of the season and NDVI increased very much. In spite of having wheat cover in the field, NIR reflection decreased very much in the late wheat growth season (0.5). Therefore the correlation relation between R and NIR band along with NDVI could be used effectively in precision agriculture management such as predicting of phonological stage, wheat yield estimation and wheat health condition.
Z. Mollaee, J. Zahiri, S. Jalili, M. R. Ansari, A. Taghizadeh,
Volume 22, Issue 2 (9-2018)
Abstract
Spectral Reflectance of suspended sediment concentration (SSC) remotely sensed by satellite images is an alternative and economically efficient method to measure SSC in inland waters such as rivers and lakes, coastal waters, and oceans. This paper retrieved SSC from satellite remote sensing imagery using radial basis function networks (RBF). In-situ measurement of SSC, water flow data, as well as MODIS band 1 and band ratio of band 2 to 1 were the inputs of the RBF. A multi-regression method was also used to make a relationship between the in-situ data and the water reflectance data retrieved from MODIS bands. The results showed that RBF had the best SSC prediction error (RMSE=0.19), as compared to the multi-regression and sediment rating curve methods, with the RMSE of 0.29 and 0.21, respectively.
R. Ziaee, M. Moghaddasi, S. Paimozd, M. H. Bagher,
Volume 22, Issue 4 (3-2019)
Abstract
Evaporation is one of the important components in water body’s management, leading to changes in the water level and water balance. Also, its accurate estimation is faced with certain difficulties and complexities. Because of the limitations of physical and empirical methods based on the meteorological data, remote sensing technology can be widely used for evaporation calculation due to its capabilities for spatial data estimation and minimization of the meteorological data application. Many models have been developed to estimate evapotranspiration using remote sensing technology. Regarding the use of these algorithms for estimating evaporation from water surface, a few studies have been done; however, there is yet no comparison between them to estimate evaporation from the water surface. For this purpose, in this study, the output from two models estimating spatially distributed evaporation of water surfaces from remotely sensed imagery is compared. In order to implement these models, Terra/MODIS Images for four months including June, July, August and September in of 2006, 2007, 2008 and 2009 were prepared. Comparisons were made using pan data from Urmia synoptic station. In general, there was a reasonable agreement between the evaporation outputs from both models versus a pan data observation. The statistical analysis also showed that the SEBS algorithm (by applying the salinity factor), despite being simple in its implementation, has higher accuracy than the SEBAL algorithm.
F. Hadian, R. Jafari, H. Bashari, M. Tarkesh,
Volume 23, Issue 4 (12-2019)
Abstract
Soil moisture is one of the most important factors that can affect productivity in ecosystems in arid and semiarid regions. The aim of this study was to investigate soil moisture and vegetation changes in the Isfahan province at the seasonal scale. For this purpose, MODIS Land Surface Temperature (LST) and NDVI data were used to calculate the TVDI index, and the rate of soil moisture content was also measured at several soil depths including 5, 10, 20, 30 cm. in the growing season. Seasonal changes of LST and NDVI indices were also studied in different climate regions ranging from humid to hyperarid. The results showed that the changes in NDVI and LST in this region were different, depending on the climate type and soil conditions; the LST and its changes mostly depended on the amount of vegetation cover NDVI changes based on the plant phenology in humid regions, which was were greater than that in arid and semi-arid climates. Soil moisture monitoring indicated that the relationships between TDVI and different soil depths varied based on the seasonal conditions. In the early growing season, the soil moisture at the depth of 0-5 cm had a higher correlation with TVDI, but in the middle of growing season, the deeper soil moisture (10-30 cm) showed the highest correlation. Therefore, the findings of this research indicated the importance of the growing season, soil conditions and vegetation percentage and types in the soil moisture studies by using satellite data.
M. Farokhi, H. Ansary, A. R. Faridhosseini,
Volume 24, Issue 1 (5-2020)
Abstract
Estimation of soil moisture at various temporal and spatial scales is a key to the strategic management of water resources. Satellite-based microwave observations have coarse spatial resolution despite widespread and continuous of the provision surface soil moisture (SSM). In this study, the SSM data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) 25km resolution were used and these products were downscaled by three parameters retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) to 1km resolution. In the next step, the integration of the SSM downscaling model with SMAR model was used to monitor the root zone soil moisture(RZSM) in the study area (Rafsanjan plain). In order to evaluate the performance of the proposed method, the SSM and the soil profile moisture were measured at 10 points in the Rafsanjan plain. Comparison of AMSR2 25k SSM and downscaled SSM with the field measurement data showed that the mean of total stations for the correlation coefficient(R) was increased from 0.540 to 0.739 and the mean absolute error(MAE) and the root mean square(RMSE) were reduced from 0.039 and 0.040 to 0.018 and 0.020, respectively. Moreover, the results obtained from the validation of the RZSM values showed that the proposed method could estimate the RZSM with high accuracy and indicate the variations.
M. Kaffash, H. Sanaei Nejad,
Volume 25, Issue 2 (9-2021)
Abstract
Land Surface Temperature (LST) is an important parameter in weather and climate systems. Satellite remote sensing is a unique way to estimate this important parameter. However, satellite products have either low spatial resolution or low temporal resolution that limits their potential use in various studies. In recent years, the use of Spatio-temporal fusion techniques to produce high resolution simultaneous spatial and temporal images has been extensively investigated. In this study, a Flexible Spatio-temporal Data Fusion (FSDAF) was used to produce Landsat-like LST images with Landsat spatial resolution and MODIS temporal resolution. The quantitative and qualitative validation of the images was performed by comparing them with the Actual Landsat LST images. The results showed that the FSDAF algorithm has high accuracy in estimating daily LST data both qualitatively and quantitatively. The RMSE and MAE parameters of the images produced compared to the actual Landsat images were 1.18 to 1.71 and 0.88 to 1.29°C, respectively. The correlation coefficient above 0.87 and bias between -0.6 to 1.45°C also confirms the high accuracy of the algorithm in estimating Landsat-like land surface temperature on a daily time scale.
S. Dehghan Farsi, R. Jafari, A.r. Mousavi,
Volume 26, Issue 2 (9-2022)
Abstract
The objective of the present study was to investigate the performance of some of the extracted information for mapping land degradation using remote sensing and field data in Fras province. Maps of vegetation cover, net primary production, land use, surface slope, water erosion, and surface runoff indicators were extracted from MOD13A3, MOD17A3, Landsat TM, SRTM, ICONA model, and SCS model, respectively. The rain use efficiency index was obtained from the net primary production and rainfall map, which was calculated from meteorological stations. The final land degradation map was prepared by integrating all the mentioned indicators using the weighted overlay method. According to the ICONA model, 5.1, 9, 47.21, 27.91, and 10.73 percent of the study area were classified as very low, low, moderate, severe, and very severe water erosion; respectively. Overlaying the ICONA map with other indicators showed that very high and high classes, moderate, and low and very low classes of land degradation covered 1.3, 18.7, 70, 0.9, and 9.1 percent of the study area, respectively. According to the results, integrating remote sensing with ICONA and SCS models increases the ability to identify land degradation.
