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Saeid Hamzeh
Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran

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Research article
Published: 09 November 2020 in GIScience & Remote Sensing
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Surface soil moisture (SSM) is an important parameter for many applications. Soil Moisture Active Passive (SMAP) satellite mission provides an SSM map at global scale. But its spatial resolution (36 km) is a big restriction for agricultural and hydrological studies at the catchment scale. Therefore, the present study was conducted to disaggregate the passive SMAP soil moisture data using the retrieved Soil Evaporative Efficiency (SEE) at 1-km spatial and daily temporal resolution from Moderate Resolution Imaging Spectroradiometer (MODIS) data and to assess the effectiveness of the method for generating data for different land covers. For this purpose, SMAP data were disaggregated using the SEE retrieved from daily MODIS data located at the southwest part of the United States. The accuracy of spatial and temporal variability of the disaggregated SMAP data was evaluated against the recorded in-situ soil moisture data in 202 stations of the Soil Climate Analysis Network (SCAN) for a period of 1 year. Results indicate that the disaggregated SMAP data have a moderate correlation with in-situ soil moisture data, but it is strongly affected by land cover. The highest accuracy was observed in the pasture/hay land cover class with Correlation Coefficient (R) value of 0.683 and 0.632, Mean Difference (MD) of −0.004 and −0.001, Root-Mean Square Error (RMSE) of 0.049 and 0.056, and unbiased Root-Mean Square Error (ubRMSE) of 0.039 and 0.045 for the disaggregated and original SSM data with the unit of m 3 . m − 3 , respectively. The lowest accuracy was found in the barren land (rock/sand/clay) for the disaggregated and original SSM data with R of 0.0278 and 0.155, MD of −0:081 and −0.052, RMSE of 0.134 and 0.116, and ubRMSE of 0.106 and 0.103, respectively. Results indicate that in overall disaggregation of SMAP data using Disaggregation based on Physical And Theoretical scale Change (DisPATCh) algorithm and MODIS products has a good potential for generating high spatial and temporal resolution of SSM at the catchment scale. But it is strongly affected by the land cover class type, because the calculation of the SEE is based on the Normalized Difference Vegetation Index (NDVI). Therefore, it can be recommended to retrieve the SEE with the attention to land cover class type and employ the other vegetation indices or methods.

ACS Style

Morteza Khazaei; Saeid Hamzeh; Qihao Weng. Generating high spatial and temporal soil moisture data by disaggregation of SMAP product and its assessment in different land covers. GIScience & Remote Sensing 2020, 57, 1046 -1056.

AMA Style

Morteza Khazaei, Saeid Hamzeh, Qihao Weng. Generating high spatial and temporal soil moisture data by disaggregation of SMAP product and its assessment in different land covers. GIScience & Remote Sensing. 2020; 57 (8):1046-1056.

Chicago/Turabian Style

Morteza Khazaei; Saeid Hamzeh; Qihao Weng. 2020. "Generating high spatial and temporal soil moisture data by disaggregation of SMAP product and its assessment in different land covers." GIScience & Remote Sensing 57, no. 8: 1046-1056.

Journal article
Published: 14 September 2020 in Remote Sensing
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Due to the excessive use of natural resources in the contemporary world, the importance of ecological and environmental condition modeling has increased. Wetlands and cities represent the natural and artificial strategic areas that affect ecosystem conditions. Changes in the ecological conditions of these areas have a great impact on the conditions of the global ecosystem. Therefore, modeling spatiotemporal variations of the ecological conditions in these areas is critical. This study was aimed at comparing degrees of variation among surface ecological conditions due to natural and unnatural factors. Consequently, the surface ecological conditions of Gomishan city and Gomishan wetland in Iran were modeled for a period of 30 years, and the spatiotemporal variations were evaluated and compared with each other. To this end, 20 Landsat 5, 7, and 8, and 432 Moderate Resolution Imaging Spectroradiometer (MODIS), monthly land surface temperature (LST) (MOD11C3) and normalized difference vegetation index (NDVI) (MOD13C3) products were utilized. The surface ecological conditions were modeled according to the Remote Sensing-based Ecological Index (RSEI), and the spatiotemporal variation of the RSEI values in the study area (Gomishan city, Gomishan wetland) were evaluated and compared with each other. According to MODIS products, the mean of the LST and NDVI variance values for the study area (Gomishan city, Gomishan wetland) were obtained to be 6.5 °C (2.1, 12.1) and 0.009 (0.005, 0.013), respectively. The highest LST and NDVI temporal variations were found for Gomishan wetland near the Caspian Sea. According to Landsat images, Gomishan wetland and Gomishan city have the highest and lowest temporal variations in surface biophysical characteristics, respectively. The mean RSEI for the study area (Gomishan city, Gomishan wetland) was 0.43 (0.65, 0.29), respectively. Additionally, the mean Coefficient of Variation (CV) of RSEI for the study area (Gomishan city, Gomishan wetland) was 0.10 (0.88, 0.51), respectively. The surface ecological conditions of Gomishan city were worse than those of the Gomishan wetland at all dates. Temporal variations in the surface ecological conditions of Gomishan wetland were greater than those of the study area and Gomishan city. These results can provide useful and effective information for environmental planning and decision-making to improve ecological conditions, protect the environment, and support sustainable ecosystem development.

ACS Style

Salman Qureshi; Seyed Kazem Alavipanah; Maria Konyushkova; Naeim Mijani; Solmaz Fathololomi; Mohammad Karimi Firozjaei; Mehdi Homaee; Saeid Hamzeh; Ata Abdollahi Kakroodi. A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran. Remote Sensing 2020, 12, 2989 .

AMA Style

Salman Qureshi, Seyed Kazem Alavipanah, Maria Konyushkova, Naeim Mijani, Solmaz Fathololomi, Mohammad Karimi Firozjaei, Mehdi Homaee, Saeid Hamzeh, Ata Abdollahi Kakroodi. A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran. Remote Sensing. 2020; 12 (18):2989.

Chicago/Turabian Style

Salman Qureshi; Seyed Kazem Alavipanah; Maria Konyushkova; Naeim Mijani; Solmaz Fathololomi; Mohammad Karimi Firozjaei; Mehdi Homaee; Saeid Hamzeh; Ata Abdollahi Kakroodi. 2020. "A Remotely Sensed Assessment of Surface Ecological Change over the Gomishan Wetland, Iran." Remote Sensing 12, no. 18: 2989.

Journal article
Published: 28 May 2020 in Ecological Indicators
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A harmful effect of anthropogenic activities in urban environments is the increases of thermal discomfort and subsequently, a negative effect on humans’ mental and physical performance. Therefore, it is of high importance to detect, monitor, and predict thermal discomfort, especially its temporal and spatial patterns in cities. The objective of this study is to propose a new method for modeling outdoor thermal comfort based on remote sensing and climatic datasets. To do so, several datasets were utilized, including those from Landsat, Moderate Resolution Imaging Spectroradiometer (MODIS), Digital Elevation Model (DEM) from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and climatic datasets from local meteorological stations. The method was experimented in the city of Tehran, Iran. For modeling outdoor thermal comfort, the Least Squares Adjustment (LSA) model was presented based on the Principle Component Analysis (PCA). In this model, the Principle Components (PCs) of the environmental and surface biophysical parameters were considered as independent variables and Discomfort Index (DI) as dependent variable. Finally, by determining the optimal values of the adjustment coefficients for each independent variable, maps of outdoor thermal comfort at different timestamps were produced and analyzed. The results of the modeling showed that correlation coefficient and Root Mean Square Error (RMSE) between the modeled and observed outdoor thermal comfort values at the meteorological stations for the training data sets were 0.86 and 1.80, for the testing data set were 0.89 and 2.04, respectively, while it was 0.85 and 1.15 for the self-deployed devices. The average values of DI in warm season of year was 8.5 °C higher than the cold season of the year. Further, in both warm and cold seasons of year the mean value of DI for bare land was found higher than other land covers, whereas that of water bodies lower than others. Our findings suggest that efficiency can be achieved for modeling outdoor thermal comfort using LSA with remote sensing and climatic datasets.

ACS Style

Naeim Mijani; Seyed Kazem Alavipanah; Mohammad Karimi Firozjaei; Jamal Jokar Arsanjani; Saeid Hamzeh; Qihao Weng. Modeling outdoor thermal comfort using satellite imagery: A principle component analysis-based approach. Ecological Indicators 2020, 117, 106555 .

AMA Style

Naeim Mijani, Seyed Kazem Alavipanah, Mohammad Karimi Firozjaei, Jamal Jokar Arsanjani, Saeid Hamzeh, Qihao Weng. Modeling outdoor thermal comfort using satellite imagery: A principle component analysis-based approach. Ecological Indicators. 2020; 117 ():106555.

Chicago/Turabian Style

Naeim Mijani; Seyed Kazem Alavipanah; Mohammad Karimi Firozjaei; Jamal Jokar Arsanjani; Saeid Hamzeh; Qihao Weng. 2020. "Modeling outdoor thermal comfort using satellite imagery: A principle component analysis-based approach." Ecological Indicators 117, no. : 106555.

Journal article
Published: 03 May 2020 in Remote Sensing
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Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction.

ACS Style

Elahe Akbari; Ali Darvishi Boloorani; Najmeh Neysani Samany; Saeid Hamzeh; Saeid Soufizadeh; Stefano Pignatti. Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2. Remote Sensing 2020, 12, 1449 .

AMA Style

Elahe Akbari, Ali Darvishi Boloorani, Najmeh Neysani Samany, Saeid Hamzeh, Saeid Soufizadeh, Stefano Pignatti. Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2. Remote Sensing. 2020; 12 (9):1449.

Chicago/Turabian Style

Elahe Akbari; Ali Darvishi Boloorani; Najmeh Neysani Samany; Saeid Hamzeh; Saeid Soufizadeh; Stefano Pignatti. 2020. "Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2." Remote Sensing 12, no. 9: 1449.

Journal article
Published: 17 May 2019 in Estuarine, Coastal and Shelf Science
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The Caspian Sea (CS) is the largest enclosed inland body of water and eminent for its rapid sea-level change. From 1929 to 1955, rapid cyclic changes with amplitudes of 3 m led to the formation of large submerged and emerged areas. The present study seeks to incorporate multi-source sensor data such as the Landsat time-series data (i.e., MSS, TM, ETM+ and OLI) alongside radar altimetry products (i.e., TOPEX, Jason-1, OSTM, Jason-3) as a means for extracting morphological features (Gorgan Bay and Gomishan lagoon) of the southeastern shorelines of the Caspian Sea, as well as to investigate changes in the shoreline for a given period of 42 years (1975-2016). We also employ Particle Swarm Optimization (PSO) algorithm as an automated method to extract shoreline change in shallow marine environments. Over the past century, the CS has experienced a lowstand in 1977 and a highstand in 1995. Despite an approximate 1.5 m drop in sea-level from 1995 to 2015, Gorgan Bay and Gomishan lagoon, with depths of 4.5 and 2.5 m, appear to have outlasted and emerged, respectively. PSO is a highly efficient method capable of defining shorelines and extracting water bodies. The surface area estimations using the PSO method are consistent with corresponding reference values, with an average error of 1.73% and a high coefficient of determination (R2 = 0.99). Differences between calculated and reference areas were mainly observed in muddy and swamp sectors of the study area. This study highlights the key role of satellite time-series in shoreline monitoring and management under rapid sea-level change conditions. Moreover, the study demonstrates the capabilities of the PSO algorithm as an automated and accurate method for shoreline detection.

ACS Style

Mehrdad Jeihouni; A.A. Kakroodi; Saeid Hamzeh. Monitoring shallow coastal environment using Landsat/altimetry data under rapid sea-level change. Estuarine, Coastal and Shelf Science 2019, 224, 260 -271.

AMA Style

Mehrdad Jeihouni, A.A. Kakroodi, Saeid Hamzeh. Monitoring shallow coastal environment using Landsat/altimetry data under rapid sea-level change. Estuarine, Coastal and Shelf Science. 2019; 224 ():260-271.

Chicago/Turabian Style

Mehrdad Jeihouni; A.A. Kakroodi; Saeid Hamzeh. 2019. "Monitoring shallow coastal environment using Landsat/altimetry data under rapid sea-level change." Estuarine, Coastal and Shelf Science 224, no. : 260-271.

Journal article
Published: 28 April 2019 in Ecological Indicators
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One of the most important signs of decreasing quality of life in urban environments is the reduction of thermal comfort. Heat discomfort has a negative impact on physical and mental performance of humans. Hence, it is of outmost importance to monitor thermal comfort patterns in cities and study its effect on people. The main objective of this study is to present a spatial multi-criteria decision analysis (MCDA) model for modeling thermal comfort for Tehran as a case study. For doing so, the reflectance and thermal information extracted from Landsat-8 satellite images, ASTER digital elevation model, MOD07 water vapor, and meteorological/climatic datasets were used. Several indicators including the downward shortwave radiation (SWD) and longwave radiation (LWD) to surface, upward longwave radiation (LWU) from the surface, brightness, greenness and wetness of the surface were derived. An Ordered Weighted Averaging (OWA) method was adapted considering different mental circumstances e.g., extremely pessimistic, pessimistic, neutral, optimistic and extremely optimistic. Our findings determine the geographical variation of thermal comfort across our study area e.g., the cold periods of the year are spread in the west and north-west side and the warm periods of the year on the west and north-west, while the central, northern, and eastern regions have a more favorable thermal comfort than other regions. The areal percentage of very suitable thermal comfort category for very pessimistic, pessimistic, neutral, optimistic, and very optimistic during the warm period of the year was 2.7, 5.1, 4.4, 13.4 and 1.18, respectively and in the cold period of the year was 9.1, 13.3, 18.3, 28.9 and 33.9, respectively. In both warm and cold periods with increasing degree of optimism, the area of favorable thermal comfort classes increases, while the area of unfavorable thermal comfort categories decreases. Our results and conclusions drawn from our proposed approach are useful for urban planners and public health researcher for monitoring quality of life in cities.

ACS Style

Naeim Mijani; Seyed Kazem Alavipanah; Saeid Hamzeh; Mohammad Karimi Firozjaei; Jamal Jokar Arsanjani. Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study. Ecological Indicators 2019, 104, 1 -12.

AMA Style

Naeim Mijani, Seyed Kazem Alavipanah, Saeid Hamzeh, Mohammad Karimi Firozjaei, Jamal Jokar Arsanjani. Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study. Ecological Indicators. 2019; 104 ():1-12.

Chicago/Turabian Style

Naeim Mijani; Seyed Kazem Alavipanah; Saeid Hamzeh; Mohammad Karimi Firozjaei; Jamal Jokar Arsanjani. 2019. "Modeling thermal comfort in different condition of mind using satellite images: An Ordered Weighted Averaging approach and a case study." Ecological Indicators 104, no. : 1-12.

Journal article
Published: 01 November 2018 in Journal of African Earth Sciences
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Groundwater quality and quantity are two major challenges in arid and semi-arid regions, due to their critical roles in sustainable agricultural development. Irrigated lands are spread all over Urmia Lake's surrounding plains in Iran. Due to the risk of saltwater intrusion as a result of over-exploitation from groundwater resources, it is important to monitor the groundwater quality and quantity through time and space. In this paper, the groundwater quantity was assessed over 11 years applying a novel groundwater balance estimation method based on water table data and 3D modeling; groundwater quality were monitored over 10 years using GIS and geostatistics; and the saltwater intrusion were investigated through generated quality maps and regression analysis. Results indicate that the groundwater balance was negative during the study period. Furthermore, the aquifers quality decreased over the study period, which was severe in west and southwest of the study area. The saltwater intrusion was proved and salty water was spread from west to other zones. The saltwater intrusion into aquifers increased electrical conductivity, chloride and sodium concentrations and will cause many ecological and agricultural problems. The novel and practicable approach utilized for groundwater balance quantitative assessment is suitable for countries lacking hydrological properties databases.

ACS Style

Mehrdad Jeihouni; Ara Toomanian; Seyed Kazem Alavipanah; Saeid Hamzeh; Petter Pilesjö. Long term groundwater balance and water quality monitoring in the eastern plains of Urmia Lake, Iran: A novel GIS based low cost approach. Journal of African Earth Sciences 2018, 147, 11 -19.

AMA Style

Mehrdad Jeihouni, Ara Toomanian, Seyed Kazem Alavipanah, Saeid Hamzeh, Petter Pilesjö. Long term groundwater balance and water quality monitoring in the eastern plains of Urmia Lake, Iran: A novel GIS based low cost approach. Journal of African Earth Sciences. 2018; 147 ():11-19.

Chicago/Turabian Style

Mehrdad Jeihouni; Ara Toomanian; Seyed Kazem Alavipanah; Saeid Hamzeh; Petter Pilesjö. 2018. "Long term groundwater balance and water quality monitoring in the eastern plains of Urmia Lake, Iran: A novel GIS based low cost approach." Journal of African Earth Sciences 147, no. : 11-19.

Journal article
Published: 01 November 2018 in Dokuchaev Soil Bulletin
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The study is focused on the pattern of soil salinity at the young loamy coastal plains of the Caspian Sea in Russia and Iran which were released from water less than 300 years ago. At two key sites of 45×30 m (Russia) and 25×20 m (Iran), the soil sampling with 1 to 5 m grid was performed to the depth of 1 m. The electrical conductivity (1 : 2.5) was measured in soil samples and soil sa-linity maps were compiled. Soils are represented by solonchaks with 2–3% of salts in the top layer or highly saline soils partly leached in the upper 5–10 cm. The ground water table is shallow (2–2.5 m). The studied sites are different in terms of climate, microtopography, and vegetation cover but spatial differentiation of soil salinity is quite similar what is evidenced from the similar distributions (mean values and variance) of electrical conductivity in almost all studied depths. The redistribution of salts is mainly observed in the upper 50 cm with the maximal manifestation in the upper 5 cm.

ACS Style

M. V. Konyushkova; S. Alavipanah; A. Abdollahi; S. Hamzeh; A. Heidari; M. P. Lebedeva; Yu. D. Nukhimovskaya; I. N. Semenkov; T. I. Chernov. THE SPATIAL DIFFERENTIATION OF SOIL SALINITY AT THE YOUNG SALINE COASTAL PLAIN OF THE CASPIAN REGION. Dokuchaev Soil Bulletin 2018, 41-57 -57.

AMA Style

M. V. Konyushkova, S. Alavipanah, A. Abdollahi, S. Hamzeh, A. Heidari, M. P. Lebedeva, Yu. D. Nukhimovskaya, I. N. Semenkov, T. I. Chernov. THE SPATIAL DIFFERENTIATION OF SOIL SALINITY AT THE YOUNG SALINE COASTAL PLAIN OF THE CASPIAN REGION. Dokuchaev Soil Bulletin. 2018; (95):41-57-57.

Chicago/Turabian Style

M. V. Konyushkova; S. Alavipanah; A. Abdollahi; S. Hamzeh; A. Heidari; M. P. Lebedeva; Yu. D. Nukhimovskaya; I. N. Semenkov; T. I. Chernov. 2018. "THE SPATIAL DIFFERENTIATION OF SOIL SALINITY AT THE YOUNG SALINE COASTAL PLAIN OF THE CASPIAN REGION." Dokuchaev Soil Bulletin , no. 95: 41-57-57.

Journal article
Published: 05 September 2018 in Science of The Total Environment
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Normalization of land surface temperature (LST) relative to environmental factors is of great importance in many scientific studies and applications. The purpose of this study was to develop physical models based on energy balance equations for normalization of satellite derived LST relative to environmental parameters. For this purpose, a set of remote sensing imagery, meteorological and climatic data recorded in synoptic stations, and soil temperatures measured by data loggers were used. For modeling and normalization of LST, a dual-source energy balance model (dual-EB), taking into account two fractions of vegetation and soil, and a triple -source energy balance model (triple-EB), taking into account three fractions of vegetation, soil and built-up land, were proposed with either regional or local optimization strategies. To evaluate and compare the accuracy of different modeling results, correlation coefficients and root mean square difference (RMSE) were computed between modeled LST and LST obtained from satellite imagery, as well as between modeled LST and soil temperature measured by data loggers. Further, the variance of normalized LST values was calculated and analyzed. The results suggested that the use of local optimization strategy increased the accuracy of the normalization of LST, compared to the regional optimization strategy. In addition, no matter the regional or local optimization strategy was employed, the triple-EB model out-performed consistently the dual-EB model for LST normalization. The results show the efficiency of the local triple-EB model to normalize LST relative to environmental parameters. The correlation coefficients were close to zero between all of the environmental parameters and the normalized LST. In other words, normalized LST was completely independent of the environmental parameters considered by this research.

ACS Style

Qihao Weng; Mohammad Karimi Firozjaei; Majid Kiavarz; Seyed Kazem Alavipanah; Saeid Hamzeh. Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Science of The Total Environment 2018, 650, 515 -529.

AMA Style

Qihao Weng, Mohammad Karimi Firozjaei, Majid Kiavarz, Seyed Kazem Alavipanah, Saeid Hamzeh. Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Science of The Total Environment. 2018; 650 ():515-529.

Chicago/Turabian Style

Qihao Weng; Mohammad Karimi Firozjaei; Majid Kiavarz; Seyed Kazem Alavipanah; Saeid Hamzeh. 2018. "Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate." Science of The Total Environment 650, no. : 515-529.

Original paper
Published: 14 August 2018 in Theoretical and Applied Climatology
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Study on air temperature is one of the most important topics in climate modeling. Modeling diurnal temperature cycle and its extracted parameters play an important role in environmental science. Previous methods for air temperature modeling are ambiguous in modeling and implementation. Thus, this research suggests a new method, based on two-part function, to model air temperature, and then compares it with previous ones. This procedure requires statistical approach to estimate optimal constant parameters for each station. The present research proposes a new statistical approach to estimate optimum air DTC constant. The proposed modeling was assessed via calculation of different values between observed and modeled air temperature. Then, a comparison was done with the results from other methods. Mean absolute error (MAE) and standard deviation of errors (STD) were measured for 93 synoptic stations over 365 daily measurements from 2013 to 2014. The mean of both MAE and STD for the suggested methods is 0.9 and 0.8 °C, respectively, showing acceptable results even more than other methods. The statistical 95% confidence interval for errors of the desired method was between − 1.8 and 1.7 °C, which indicated fewer range of error. Also, kurtosis and skewness of histogram errors for sampling observation time showed the model’s efficiency.

ACS Style

Mehdi Gholamnia; Seyed Kazem Alavipanah; Ali Darvishi Boloorani; Saeid Hamzeh; Majid Kiavarz. A new method to model diurnal air temperature cycle. Theoretical and Applied Climatology 2018, 137, 229 -238.

AMA Style

Mehdi Gholamnia, Seyed Kazem Alavipanah, Ali Darvishi Boloorani, Saeid Hamzeh, Majid Kiavarz. A new method to model diurnal air temperature cycle. Theoretical and Applied Climatology. 2018; 137 (1-2):229-238.

Chicago/Turabian Style

Mehdi Gholamnia; Seyed Kazem Alavipanah; Ali Darvishi Boloorani; Saeid Hamzeh; Majid Kiavarz. 2018. "A new method to model diurnal air temperature cycle." Theoretical and Applied Climatology 137, no. 1-2: 229-238.

Conference paper
Published: 01 July 2018 in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Shallow groundwater has a decisive impact on land surface temperature (LST) and soil moisture (SM). In the present paper relationship between shallow groundwater, SM and LST was studied. For this purpose, the groundwater level and soil moisture were measured in 59 and 39 locations respectively in the southwest of Iran, during June 2016, Simultaneous with the overpass of a Landsat 8 satellite from the study site. After necessary image processing the LST was retrieved from the Landsat image using the split window algorithm. Then relationship between retrieved LST and different field observation were studied. Results show that there is a significant relationship between the groundwater depth and SM with LST. These results indicate that shallow groundwater depth and soil moisture content could be estimated and mapped using the retrieved LST from the satellite imagery.

ACS Style

Saeid Hamzeh; Mohammad Mehrabi; Seyed Kazem Alavipanah; Majid Kiavar Moghadam. Investigating the Relationship Between Shallow Groundwater, Soil Moisture and Land Surface Temperature Using Remotely Sensed Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 7789 -7792.

AMA Style

Saeid Hamzeh, Mohammad Mehrabi, Seyed Kazem Alavipanah, Majid Kiavar Moghadam. Investigating the Relationship Between Shallow Groundwater, Soil Moisture and Land Surface Temperature Using Remotely Sensed Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():7789-7792.

Chicago/Turabian Style

Saeid Hamzeh; Mohammad Mehrabi; Seyed Kazem Alavipanah; Majid Kiavar Moghadam. 2018. "Investigating the Relationship Between Shallow Groundwater, Soil Moisture and Land Surface Temperature Using Remotely Sensed Data." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 7789-7792.

Journal article
Published: 01 May 2018 in International Journal of Applied Earth Observation and Geoinformation
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This study was carried out to evaluate possible improvements of the soil moisture (SM) retrievals from the SMAP observations, based on the synergy between SMAP and SMOS. We assessed the impacts of the vegetation and soil roughness parameters on SM retrievals from SMAP observations. To do so, the effects of three key input parameters including the vegetation optical depth (VOD), effective scattering albedo (ω) and soil roughness (HR) parameters were assessed with the emphasis on the synergy with the VOD product derived from SMOS-IC, a new and simpler version of the SMOS algorithm, over two years of data (April 2015 to April 2017). First, a comprehensive comparison of seven SM retrieval algorithms was made to find the best one for SM retrievals from the SMAP observations. All results were evaluated against in situ measurements over 548 stations from the International Soil Moisture Network (ISMN) in terms of four statistical metrics: correlation coefficient (R), root mean square error (RMSE), bias and unbiased RMSE (UbRMSE). The comparison of seven SM retrieval algorithms showed that the dual channel algorithm based on the additional use of the SMOS-IC VOD product (selected algorithm) led to the best results of SM retrievals over 378, 399, 330 and 271 stations (out of a total of 548 stations) in terms of R, RMSE, UbRMSE and both R & UbRMSE, respectively. Moreover, comparing the measured and retrieved SM values showed that this synergy approach led to an increase in median R value from 0.6 to 0.65 and a decrease in median UbRMSE from 0.09 m3/m3 to 0.06 m3/m3. Second, using the algorithm selected in a first step and defined above, the ω and HR parameters were calibrated over 218 rather homogenous ISMN stations. 72 combinations of various values of ω and HR were used for the calibration over different land cover classes. In this calibration process, the optimal values of ω and HR were found for the different land cover classes. The obtained results indicated that the impact of the VOD parameter on SM retrievals is more considerable than the effects of HR and ω. Overall, the inclusion of the VOD parameter in the SMAP SM retrieval algorithm was found to be a very interesting approach and showed the large potential benefit of the synergy between SMAP and SMOS.

ACS Style

Mohsen Ebrahimi-Khusfi; Seyed Kazem Alavipanah; Saeid Hamzeh; Farshad Amiraslani; Najmeh Neysani Samany; Jean-Pierre Wigneron. Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC. International Journal of Applied Earth Observation and Geoinformation 2018, 67, 148 -160.

AMA Style

Mohsen Ebrahimi-Khusfi, Seyed Kazem Alavipanah, Saeid Hamzeh, Farshad Amiraslani, Najmeh Neysani Samany, Jean-Pierre Wigneron. Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC. International Journal of Applied Earth Observation and Geoinformation. 2018; 67 ():148-160.

Chicago/Turabian Style

Mohsen Ebrahimi-Khusfi; Seyed Kazem Alavipanah; Saeid Hamzeh; Farshad Amiraslani; Najmeh Neysani Samany; Jean-Pierre Wigneron. 2018. "Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC." International Journal of Applied Earth Observation and Geoinformation 67, no. : 148-160.

Environmental planning and management
Published: 01 March 2018 in Environmental Science and Pollution Research
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The PM2.5 as one of the main pollutants in Tehran city has a devastating effect on human health. Knowing the key parameters associated with PM2.5 concentration is essential to take effective actions to reduce the concentration of these particles. This study assesses the relationship between meteorological (humidity, pressure, temperature, precipitation, and wind speed) and environmental parameters (normalize difference vegetation index and land surface temperature of MODIS satellite data) on PM2.5 concentration in Tehran city. The Geographically Weighted Regression (GWR) was employed to assess the impact of key parameters on PM2.5 concentrations in winter and summer. For this purpose, first the seasonal average of meteorological data were extracted and synchronized to satellite data. Then, using the ordinary least square model, the important parameters related to PM2.5 concentration were determined and evaluated. Finally, using the GWR model, the relationships between parameters related to PM2.5 concentration were analyzed. The results of this study indicate that meteorological and environmental parameters in winter season (71%) have a much higher ability to explain PM2.5 concentration than summer season (40%). In winter, PM2.5 concentration has a negative correlation with vegetation at most parts of the study area, a negative correlation with LST in the western and a positive correlation in the eastern part of the study area, a positive correlation with temperature, and a negative correlation with wind speed in the northeastern part of the study area. Precipitation has a positive correlation with PM2.5 concentration in most parts of the study area in both seasons. But, it was investigated in case of higher precipitation (more than 2 mm), PM2.5 concentration decreases. But, there is no negative relationship in any of the dependent parameters with PM2.5 concentration in summer. In this season, the air temperature parameter showed a high correlation with PM2.5 concentration. Also, spatial variations of the local coefficients for all parameters are higher in winter than in summer.

ACS Style

Fakhreddin Hajiloo; Saeid Hamzeh; Mahsa Gheysari. Impact assessment of meteorological and environmental parameters on PM2.5 concentrations using remote sensing data and GWR analysis (case study of Tehran). Environmental Science and Pollution Research 2018, 26, 24331 -24345.

AMA Style

Fakhreddin Hajiloo, Saeid Hamzeh, Mahsa Gheysari. Impact assessment of meteorological and environmental parameters on PM2.5 concentrations using remote sensing data and GWR analysis (case study of Tehran). Environmental Science and Pollution Research. 2018; 26 (24):24331-24345.

Chicago/Turabian Style

Fakhreddin Hajiloo; Saeid Hamzeh; Mahsa Gheysari. 2018. "Impact assessment of meteorological and environmental parameters on PM2.5 concentrations using remote sensing data and GWR analysis (case study of Tehran)." Environmental Science and Pollution Research 26, no. 24: 24331-24345.

Article
Published: 24 February 2018 in Water Resources Management
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In the present study the WEAP-NSGA-II coupling model was developed in order to apply the hedging policy in a two-reservoir system, including Gavoshan and Shohada dams, located in the west of Iran. For this purpose after adjusting the input files of WEAP model, it was calibrated and verified for a statistical period of 4 and 2 years respectively (2008 till 2013). Then periods of water shortage were simulated for the next 20 years by defining a reference scenario and applying the operation policy based on the current situation. Finally, the water released from reservoirs was optimized based on the hedging policy and was compared with the reference scenario in coupled models. To ensure the superiority of the proposed method, its results was compared with the results of two well-known multi-objective algorithms called PESA-II and SPEA-II. Results show that NSGA-II algorithm is able to generate a better Pareto front in terms of minimizing the objective functions in compare with PESA-II and SPEA-II algorithms. An improvement of about 20% in the demand site coverage reliability of the optimum scenario was obtained in comparison with the reference scenario for the months with a higher water shortage. In addition, considering the hedging policy, the demand site coverage in the critical months increased about 35% in compared with the reference scenario.

ACS Style

Arash Azari; Saeid Hamzeh; Saba Naderi. Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy. Water Resources Management 2018, 32, 2061 -2078.

AMA Style

Arash Azari, Saeid Hamzeh, Saba Naderi. Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy. Water Resources Management. 2018; 32 (6):2061-2078.

Chicago/Turabian Style

Arash Azari; Saeid Hamzeh; Saba Naderi. 2018. "Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy." Water Resources Management 32, no. 6: 2061-2078.

Journal article
Published: 01 February 2018 in Journal of Hydrology
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The objective of this study was to exploit the synergy between SMOS and SMAP based on vegetation optical depth (VOD) to improve brightness temperature (TB) simulations and land surface soil moisture (SM) retrievals in arid regions of the world. In the current operational algorithm of SMAP (level 2), vegetation water content (VWC) is considered as a proxy to compute VOD which is calculated by an empirical conversion function of NDVI. SMOS avoids the empirical estimation of VOD and retrieves simultaneously SM and VOD from TB observations. The present study attempted to improve SMAP TB simulations and SM retrievals by benefiting from the advantages of the SMOS (L-MEB) algorithm. This was achieved by using a synergy method based on replacing the default value of SMAP VOD with the retrieved value of VOD from the SMOS multi angular and bi-polarization observations of TB. The insitu SM measurements, used as reference SM in this study, were obtained from the International Soil Moisture Network (ISMN) over 180 stations located in arid regions of the world. Furthermore, four stations were randomly selected to analyze the temporal variations in VOD and SM. Results of the synergy method showed that the accuracy of the TB simulations and SM retrievals was respectively improved at 144 and 124 stations (out of a total of 180 stations) in terms of coefficient of determination (R2) and unbiased root mean squared error (UbRMSE). Analyzing the temporal variations in VOD showed that the SMOS VOD, conversely to the SMAP VOD, can better illustrate the presence of herbaceous plants and may be a better indicator of the seasonal changes in the vegetation density and biomass over the year.

ACS Style

Mohsen Ebrahimi; Seyed Kazem Alavipanah; Saeid Hamzeh; Farshad Amiraslani; Najmeh Neysani Samany; Jean-Pierre Wigneron. Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions. Journal of Hydrology 2018, 557, 740 -752.

AMA Style

Mohsen Ebrahimi, Seyed Kazem Alavipanah, Saeid Hamzeh, Farshad Amiraslani, Najmeh Neysani Samany, Jean-Pierre Wigneron. Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions. Journal of Hydrology. 2018; 557 ():740-752.

Chicago/Turabian Style

Mohsen Ebrahimi; Seyed Kazem Alavipanah; Saeid Hamzeh; Farshad Amiraslani; Najmeh Neysani Samany; Jean-Pierre Wigneron. 2018. "Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions." Journal of Hydrology 557, no. : 740-752.

Article
Published: 18 October 2017 in Environmental Monitoring and Assessment
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Preserving aquatic ecosystems and water resources management is crucial in arid and semi-arid regions for anthropogenic reasons and climate change. In recent decades, the water level of the largest lake in Iran, Urmia Lake, has decreased sharply, which has become a major environmental concern in Iran and the region. The efforts to revive the lake concerns the amount of water required for restoration. This study monitored and assessed Urmia Lake status over a period of 30 years (1984 to 2014) using remotely sensed data. A novel method is proposed that generates a lakebed digital elevation model (LBDEM) for Urmia Lake based on time series images from Landsat satellites, water level field measurements, remote sensing techniques, GIS, and 3D modeling. The volume of water required to restore the Lake water level to that of previous years and the ecological water level was calculated based on LBDEM. The results indicate a marked change in the area and volume of the lake from its maximum water level in 1998 to its minimum level in 2014. During this period, 86% of the lake became a salt desert and the volume of the lake water in 2013 was just 0.83% of the 1998 volume. The volume of water required to restore Urmia Lake from benchmark status (in 2014) to ecological water level (1274.10 m) is 12.546 Bm3, excluding evaporation. The results and the proposed method can be used by national and international environmental organizations to monitor and assess the status of Urmia Lake and support them in decision-making.

ACS Style

Mehrdad Jeihouni; Ara Toomanian; Seyed Kazem Alavipanah; Saeid Hamzeh. Quantitative assessment of Urmia Lake water using spaceborne multisensor data and 3D modeling. Environmental Monitoring and Assessment 2017, 189, 572 .

AMA Style

Mehrdad Jeihouni, Ara Toomanian, Seyed Kazem Alavipanah, Saeid Hamzeh. Quantitative assessment of Urmia Lake water using spaceborne multisensor data and 3D modeling. Environmental Monitoring and Assessment. 2017; 189 (11):572.

Chicago/Turabian Style

Mehrdad Jeihouni; Ara Toomanian; Seyed Kazem Alavipanah; Saeid Hamzeh. 2017. "Quantitative assessment of Urmia Lake water using spaceborne multisensor data and 3D modeling." Environmental Monitoring and Assessment 189, no. 11: 572.

Journal article
Published: 01 September 2017 in Remote Sensing
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The air temperature is an essential variable in many applications related to Earth science. Sporadic spatial distribution of weather stations causes a low spatial resolution of measured air temperatures. This study focused on modeling the air diurnal temperature cycle (DTC) based on the land surface temperature (LST) DTC. The air DTC model parameters were estimated from LST DTC model parameters by a regression analysis. Here, the LST obtained from the INSAT-3D geostationary satellite and the air temperature extracted from weather stations were used within the time frame of 4 March 2015 to 22 May 2017 across Iran. Constant parameters of the air DTC model for each weather station were estimated based on an experimental approach over the time period. Results showed these parameters decrease as elevation increases. The mean absolute error (MAE) and the root mean square error (RMSE) for three hours sampling were calculated. The MAE and RMSE ranges were between [0.1, 4] °C and [0.1, 3.3] °C, respectively. Additionally, 95% of MAEs and RMSEs were less than 2.9 °C and 2.4 °C values, correspondingly. The range of the mean values of MAEs and RMSEs for a three-hour sampling time were [−0.29, 0.6] °C and [2, 2.11] °C. The DTC model results showed a meaningful statistical fitting in both air DTCs modeled from LST and weather station-based DTCs. The variability of mean error and RMSE in different land covers and elevation classes were also investigated. In spite of the complex behavior of the environmental variables in the study area, the model error bar did not show significantly biased estimations for various classes. Therefore, the developed model was less sensitive to variations of land covers and elevation changes. It can be conclude that the coefficients of regression between LST and air DTC could model properly the environmental factors.

ACS Style

Mehdi Gholamnia; Ali Darvishi Boloorani; Saeid Hamzeh; Majid Kiavarz; Seyed Kazem Alavipanah. Diurnal Air Temperature Modeling Based on the Land Surface Temperature. Remote Sensing 2017, 9, 915 .

AMA Style

Mehdi Gholamnia, Ali Darvishi Boloorani, Saeid Hamzeh, Majid Kiavarz, Seyed Kazem Alavipanah. Diurnal Air Temperature Modeling Based on the Land Surface Temperature. Remote Sensing. 2017; 9 (9):915.

Chicago/Turabian Style

Mehdi Gholamnia; Ali Darvishi Boloorani; Saeid Hamzeh; Majid Kiavarz; Seyed Kazem Alavipanah. 2017. "Diurnal Air Temperature Modeling Based on the Land Surface Temperature." Remote Sensing 9, no. 9: 915.

Journal article
Published: 01 July 2017 in Agricultural Water Management
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ACS Style

Shadman Veysi; Abd Ali Naseri; Saeid Hamzeh; Harm Bartholomeus. A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agricultural Water Management 2017, 189, 70 -86.

AMA Style

Shadman Veysi, Abd Ali Naseri, Saeid Hamzeh, Harm Bartholomeus. A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agricultural Water Management. 2017; 189 ():70-86.

Chicago/Turabian Style

Shadman Veysi; Abd Ali Naseri; Saeid Hamzeh; Harm Bartholomeus. 2017. "A satellite based crop water stress index for irrigation scheduling in sugarcane fields." Agricultural Water Management 189, no. : 70-86.

Journal article
Published: 10 October 2016 in Agriculture
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Land suitability classification is important in planning and managing sustainable land use. Most approaches to land suitability analysis combine a large number of land and soil parameters, and are time-consuming and costly. In this study, a potentially useful technique (combined feature selection and fuzzy-AHP method) to increase the efficiency of land suitability analysis was presented. To this end, three different feature selection algorithms—random search, best search and genetic methods—were used to determine the most effective parameters for land suitability classification for the cultivation of barely in the Shavur Plain, southwest Iran. Next, land suitability classes were calculated for all methods by using the fuzzy-AHP approach. Salinity (electrical conductivity (EC)), alkalinity (exchangeable sodium percentage (ESP)), wetness and soil texture were selected using the random search method. Gypsum, EC, ESP, and soil texture were selected using both the best search and genetic methods. The result shows a strong agreement between the standard fuzzy-AHP methods and methods presented in this study. The values of Kappa coefficients were 0.82, 0.79 and 0.79 for the random search, best search and genetic methods, respectively, compared with the standard fuzzy-AHP method. Our results indicate that EC, ESP, soil texture and wetness are the most effective features for evaluating land suitability classification for the cultivation of barely in the study area, and uses of these parameters, together with their appropriate weights as obtained from fuzzy-AHP, can perform good results for land suitability classification. So, the combined feature selection presented and the fuzzy-AHP approach has the potential to save time and money for land suitability classification.

ACS Style

Saeid Hamzeh; Marzieh Mokarram; Azadeh Haratian; Harm Bartholomeus; Arend Ligtenberg; Arnold K. Bregt. Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran). Agriculture 2016, 6, 52 .

AMA Style

Saeid Hamzeh, Marzieh Mokarram, Azadeh Haratian, Harm Bartholomeus, Arend Ligtenberg, Arnold K. Bregt. Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran). Agriculture. 2016; 6 (4):52.

Chicago/Turabian Style

Saeid Hamzeh; Marzieh Mokarram; Azadeh Haratian; Harm Bartholomeus; Arend Ligtenberg; Arnold K. Bregt. 2016. "Feature Selection as a Time and Cost-Saving Approach for Land Suitability Classification (Case Study of Shavur Plain, Iran)." Agriculture 6, no. 4: 52.

Journal article
Published: 01 October 2016 in International Journal of Applied Earth Observation and Geoinformation
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This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R2 of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R2 and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable.

ACS Style

Saeid Hamzeh; Abd Ali Naseri; Seyed Kazem AlaviPanah; Harm Bartholomeus; Martin Herold. Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields. International Journal of Applied Earth Observation and Geoinformation 2016, 52, 412 -421.

AMA Style

Saeid Hamzeh, Abd Ali Naseri, Seyed Kazem AlaviPanah, Harm Bartholomeus, Martin Herold. Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields. International Journal of Applied Earth Observation and Geoinformation. 2016; 52 ():412-421.

Chicago/Turabian Style

Saeid Hamzeh; Abd Ali Naseri; Seyed Kazem AlaviPanah; Harm Bartholomeus; Martin Herold. 2016. "Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and Quantitative mapping of salinity stress in sugarcane fields." International Journal of Applied Earth Observation and Geoinformation 52, no. : 412-421.