A5043147444- Soil Moisture and Remote Sensing
- Synthetic Aperture Radar (SAR) Applications and Techniques
- Remote Sensing in Agriculture
- Precipitation Measurement and Analysis
- Smart Agriculture and AI
- Remote Sensing and LiDAR Applications
- Hydrocarbon exploration and reservoir analysis
- Soil Geostatistics and Mapping
- Climate change and permafrost
- Cryospheric studies and observations
- Sunflower and Safflower Cultivation
- Leaf Properties and Growth Measurement
- Rice Cultivation and Yield Improvement
- Remote Sensing and Land Use
- Soil and Environmental Studies
- Plant Water Relations and Carbon Dynamics
- Agriculture and Biological Studies
- Agriculture and Rural Development Research
- NMR spectroscopy and applications
- Plant Ecology and Taxonomy Studies
- Hydraulic Fracturing and Reservoir Analysis
- Geophysical Methods and Applications
- Plant Growth Enhancement Techniques
- Plant tissue culture and regeneration
- Soil and Unsaturated Flow
University of Maryland, College Park
2020-2023
University of Tehran
2011-2021
Carleton University
2018-2021
Natural Resources Canada
2017-2019
Agriculture and Agri-Food Canada
2014-2018
Tarbiat Modares University
2004-2018
Université de Sherbrooke
2013-2014
The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need long-duration combined active passive L-band microwave observations. response this need, joint Canada-U.S. field experiment (SMAPVEX12) was conducted Manitoba (Canada) over six-week period 2012....
The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that WCM accurately LAI if is effectively calibrated. However, calibration this requires access field measures as well soil moisture. In contrast, machine learning (ML) algorithms trained satellite data, even moisture are not available. study, a support vector (SVM) was for corn, soybeans, rice, and...
Few countries are using space-based Synthetic Aperture Radar (SAR) to operationally produce national-scale maps of their agricultural landscapes. For the past ten years, Canada has integrated C-band SAR with optical satellite data map what crops grown in every field, for entire country. While advantages well understood, barriers its operational use include lack familiarity by end-user agencies and a 'blueprint' on how implement an SAR-based mapping system. This research reviewed order...
Accurate estimation of biomass and Leaf Area Index (LAI) requires appropriate models predictor variables. These biophysical parameters are indicative crop productivity, thus, interest in applications such as yield forecasting precision farming. This study evaluated the potential leveraging vegetation indices derived from multi-temporal RapidEye data using a machine learning approach to estimate LAI. Both near-infrared red-edge based were considered this study. In-situ measurements these two...
On 10 August 2020, a series of intense and fast-moving windstorms known as derecho caused widespread damage across Iowa’s (the top US corn-producing state) agricultural regions. This severe weather event bent flattened crops over approximately one-third the state. Immediate evaluation disaster’s impact on lands, including maps crop damage, was critical to enabling rapid response by government agencies, insurance companies, supply chain. Given very large area impacted disaster, satellite...
The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated this study. Soil field measurements MODIS relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced (EVI) Water (NDWI) are calculated from images. In addition, Land Surface Temperature (LST)...
Biomass has a direct relationship with agricultural production and may help to predict crop yield. Earth observation technology can contribute significantly monitoring given the availability of temporally frequent high-resolution radar or optical satellite data. Polarimetric Synthetic Aperture Radar (PolSAR) several advantages for operational that at these longer wavelengths atmospheric illumination conditions do not affect acquisitions considering sensitivity microwaves structural...
The soil moisture changes (Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sub xmlns:xlink="http://www.w3.org/1999/xlink"><b>v</b></sub> ) have a significant influence on forestry, hydrology, meteorology, agriculture, and climate change. Interferometric synthetic aperture radar (InSAR), as potential remote sensing tool for change detection, was relatively less investigated monitoring this parameter. DInSAR phase (φ) is sensitive to the in...
Cloudy conditions reduce the utility of optical imagery for crop monitoring. New constellations satellites – including RADARSAT Constellation Mission (RCM) and Sentinel-1A/B, both available under free open data policies can be used to create stacks dense seasonal C-band Synthetic Aperture Radar (SAR) data. Yet date, contribution SAR operational mapping is often limited that a gap-filler, compensating obscured by clouds. The Joint Experiment Crop Assessment Monitoring (JECAM) Inter-Comparison...
Iran, situated in Southwest Asia, showcases a diverse landscape, including three phytogeographical regions and two global biodiversity hotspots. This diversity is attributed to its intricate geology, mountainous terrain, wide altitudinal range, heterogeneous climate, fostering rich flora characterized by significant proportion of endemism. We present an updated version the Vegetation Database Iran (IranVeg) (GIVD ID AS-IR-001), comprising 13,411 plots spanning six major habitat types. These...
In this study, a cross-calibration approach was applied to combine RADARSAT-2 and RapidEye sensors for biomass monitoring over corn fields. First, were compared in terms of estimation. Then the estimated from cross-calibrated with respect RapidEye. Combination optical Synthetic Aperture Radar (SAR) derived proposed have higher temporal resolution maps. Vegetation indices including normalized difference vegetation index (NDVI), red-edge triangular (RTVI), simple ratio (SR) (SRre) used...
Crop biophysical parameters, such as Leaf Area Index (LAI) and biomass, are essential for estimating crop productivity, yield modeling, agronomic management. This study used several features extracted from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) spectral vegetation indices Sentinel-2 optical data to estimate LAI wet dry biomass. Various machine learning algorithms, including Random Forest Regression (RFR), Support Vector (SVR), Artificial Neural Network (ANN), were trained...
AbstractIn the past, different soil moisture estimation models were developed using remotely sensed data. Some of these are based on optical images (i.e., models), some synthetic aperture radar (SAR) SAR and a few integration hybrid models). In this study, three types compared. Results show that generally more accurate than models, by accuracies improve.Using calibrated Dubois model, root mean square surface roughness parameter was estimated for all in situ This then considered to correct...