A synergistic use of AMSR2 and MODIS images to detect saline soils (Study Area: Iran)

Soil salinity is a critical environmental problem especially in arid and semiarid regions. Then, the objective of this study is to detect saline soils by synergistic use of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) images. In this regard, the Total Precipitable Water (TPW) Vapor parameter obtained from AMSR2 and MODIS, the Microwave Polarization Difference Index (MPDI), and a vertical to horizontal brightness temperature ratio ($TB_{v}/TB_{h}$) in the 6 GHz channel of AMSR2 were used in two procedures. In procedure 1, the thresholding on the TPW and MPDI, and in procedure 2, the thresholding on the TPW and the $TB_{v}/TB_{h}$ in the 6 GHz channel were investigated. The overall accuracy and Kappa coefficient of the produced saline soil map by the procedure 1 were acquired as 0.865 and 0.715, and for the procedure 2 were 0.809 and 0.607, respectively

A synergistic use of AMSR2 and MODIS images to detect saline soils (Study Area: Iran)

Soil salinity is a critical environmental problem especially in arid and semiarid regions. Then, the objective of this study is to detect saline soils by synergistic use of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) images. In this regard, the Total Precipitable Water (TPW) Vapor parameter obtained from AMSR2 and MODIS, the Microwave Polarization Difference Index (MPDI), and a vertical to horizontal brightness temperature ratio ($TB_{v}/TB_{h}$) in the 6 GHz channel of AMSR2 were used in two procedures. In procedure 1, the thresholding on the TPW and MPDI, and in procedure 2, the thresholding on the TPW and the $TB_{v}/TB_{h}$ in the 6 GHz channel were investigated. The overall accuracy and Kappa coefficient of the produced saline soil map by the procedure 1 were acquired as 0.865 and 0.715, and for the procedure 2 were 0.809 and 0.607, respectively

Improving a comprehensive remote sensing drought index (CRSDI) in the Western part of Iran

Numerous remote sensing indices have been introduced to investigate the drought. Hence, the purpose of this study was to develop a Comprehensive Remote Sensing Drought Index (CRSDI) using the strengths of the previous Remote Sensing Drought Indices (RSDIs). Eleven mostly used RSDIs were used to develop CRSDI. Aras and Karkheh basins were studied as representative of Western Iran. Vegetation indices and surface temperature were derived from the products of the Moderate Resolution Imaging Spectroradiometer (MODIS). Gravity Recovery and Climate Experiment (GRACE (and Global Land Data Assimilation System (GLDAS) data were used to extract groundwater information. The performances of RSDIs were evaluated using the Standardized Precipitation Index (SPI) acquired from 13 synoptic stations. The correlation coefficients between CRSDI against six-month SPI and nine-month SPI were obtained as 0.66 and 0.56, respectively. Then, the results proved the proper efficiency of the CRSDI to investigate the drought in the study area

Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images

Various spectral indices have been introduced to detect water extent from satellite images with different performances in various regions. The aim of this study is to provide an efficient index using particle swarm optimization (PSO) algorithm to detect water spread areas from satellite images with similar performance in different regions. This index is introduced for images containing water absorption bands from visible to middle infrared wavelengths. Eleven images were prepared from different satellites and water bodies with various environmental conditions. In addition, 40 pixels from water and 40 pixels from non-water regions were selected as training data for PSO algorithm. Results were evaluated using digitized polygons of water bodies on high-resolution images of Google Earth. The best results in PSO-based water index (PSOWI) were obtained by the combination of two bands (red and middle infrared). PSOWI represented proper performance in the selected various land covers and satellite images

Analysing the land-use change effects on soil erosion and sediment in the North of Iran; a case study: Talar watershed

This research aims at predicting the future land-use and examining the effects of the land-use changes on the present and future soil erosion and sediment delivery. Then, Land Change Modeler (LCM) and Geo-spatial Water Erosion Prediction Project (GeoWEPP) were applied. Annual suspended sediment derived from sediment rating curves in Talar Watershed, Iran was used to evaluate the GeoWEPP. The predicted sediment load values of 2016 by GeoWEPP in the likely zone of 0.5 for Shirgah-Talar and Valikbon hydrometric stations were achieved respectively as 756094 and 62082.8 ton. Moreover, the mean annual sediment delivery per unit area of 2000, 2010, 2016 and 2030 were calculated respectively as 37.5, 43, 49.4 and 53.2 (t/ha.yr). The reason for the acquired incremental trend of the soil loss and sediment delivery was the land-use change. The results proved the acceptable performance of LCM combined by GeoWEPP to predict the nearby land-use and related sediment delivery

Introducing a merged precipitation satellite model using satellite precipitation products, land surface temperature, and precipitable water vapor

Satellite precipitation have a lot of uncertainty and the spatial resolution is still quite coarse. Hence, the purpose of this study is to introduce a new approach based on multivariate linear modelling and Artificial Neural Networks (ANN) for the first time using four multisource satellite precipitation products and some other satellite data. Four different satellite precipitation products with different algorithms, along with Precipitable Water Vapor (PWV) and Land Surface Temperature (LST) data on a daily scale were used (to benefit interactions of atmospheric content). Five linear merge models, including five ANN models were introduced and calibrated using harmony search algorithm. The performance of the models was evaluated over Iran using observational rainfall data of 2014 to 2020. The findings of this study highlighted that the model based on ANN, which merged four rainfall products together with LST and PWV has outperformed individual satellite products and other presented models

The effects of cyanobacterial blooms on MODIS-L2 data products in the southern Caspian Sea

Summary: MODIS satellite imageries with minimal cloud cover (<25%) were used to extract cyanobacteria index, floating algea index, fluorescence line height, chlorophyll-a and sea surface temperature products, for seven days concurrent with blooms. The results showed a positive correlation between cyanobacteria index and chlorophyll-a (R = 0.74, p ≤ 0.05 and R = 0.75, p ≤ 0.05 for 2005 and 2010 respectively), and a negative correlation between the cyanobacteria index and fluorescence line height (R = −0.74, p ≤ 0.05 and R = −0.93, p ≤ 0.005 for 2005 and 2010 respectively). Further analysis showed that considering Fluorescence Line Height is not sufficient to detect the cyanobacterial blooms in the offshore area. However, the results indicated a weak correlation between cyanobacteria index and floating algae index (R = −0.42, p = 0.34 and R = −0.47, p = 0.29 for 2005 and 2010 respectively). The results also indicated that the irregular increases in the cyanobacteria index and chlorophyll-a in the study region was an operational index for the incidence of cyanobacterial bloom, where the surface wind speed and temperature conditions were <4 m s−1 and ≥30°C, respectively. Finally, a linear model was defined for monitoring, which determines occurrence or non-occurrence of cyanobacteria bloom based on daily monitoring of the changes of products. In order to evaluate the proposed model, its efficiency was tested on datasets at different times and locations, and the results were consistent with field reports, as expected. Keywords: Remote sensing, Cyanobacterial index, Floating algae index, Chlorophyll-a, Fluorescence line heigh

Application of target detection algorithms to identification of iron oxides using ASTER images: a case study in the North of Semnan province, Iran

In recent years, satellite sensor images have been commonly used in mineral detection. These images, with different spectral and spatial resolutions, can be used in detecting minerals which have surface indicators. Due to numerous sources of uncertainties in using spectral processes for mineral explorations and atmospheric effects on the spectrum of the pixels, in this research, mineral target detection methods were selected and implemented. The implementation of this research was done using an ASTER scene from the northern area of Semnan province in Iran. There were seven iron mines in the region covered by this scene. Three of them were used as training data and four other ones were used as test data. Several target detection methods were implemented and the mean of the results of these algorithms, which embrace the results of all algorithms, was evaluated. The output of the algorithm is an image where the gray value of each pixel corresponds to the probability of similarity to the training data. Considering the fact that the probabilities' range is between 0 and 1, after implementing the algorithms, it was concluded that the spectral angle mapper (SAM) method has the best performance with mean probability value of 0.99 for the test mines. Based upon the fact that the mean value of the algorithms was 0.87, it was proved that these methods can be very practical thanks to their high accuracy in prospection of new exploration targets which may detect new equivalent iron potentials