Hamed Alemohammad

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Verified

Hamed verified their affiliation via an institutional email.

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• PhD
• Associate Professor at Clark University

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H,...

This technical report presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2M global time series samples from NASA's Harmonized Landsat and Sentinel-2 data archive at 30m resolution, the new 300M and 600M parameter models incorporate temporal and location...

Approximately 20% of Africa's population suffered from undernourishment, and 868 million people experienced moderate to severe food insecurity in 2022. Land-use and land-cover maps provide crucial insights for addressing food insecurity, e.g., by mapping croplands. The development of global land-cover maps has been facilitated by the increasing ava...

Synthetic Aperture Radar (SAR) observations, valued for their consistent acquisition schedule and not being affected by cloud cover and variations between day and night, have become extensively utilized in a range of agricultural applications. The advent of deep learning allows for the capture of salient features from SAR observations. This is acco...

Mapping agricultural fields using high-resolution satellite imagery and deep learning (DL) models has advanced significantly, even in regions with small, irregularly shaped fields. However, effective DL models often require large, expensive labeled datasets, which are typically limited to specific years or regions. This restricts the ability to cre...

An approach for estimating vertically continuous soil moisture profiles under varying vegetation covers by combining remote sensing with soil (hydrological) modeling is proposed. The approach uses decomposed soil scattering components, after the removal of the vegetation scattering components from fully polarimetric P-band SAR observations. By comp...

Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote sensing. Foundation models are pre-trained on large unlabeled datasets through self-supervision, and then fine-tuned for various downstream tasks with small labeled dataset...

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks. Such models, recently coined foundation models, have been transformational to the field of natural language processing. Variants have also been proposed f...

Abstract Tropical cyclones (TCs) cause significant disruptions to infrastructure and livelihood. The scale of loss due to TCs may be mitigated by prompt and accurate advisories about TC wind speed. Current advisories are consensus based and have a time delay of about 6 hr between each new update. As part of efforts to increase the frequency of wind...

Soil moisture is a key hydrological variable with great influence on various processes of land-atmosphere interactions, like infiltration or subsurface flow [1]. Depending on the frequency, microwave remote sensing can be used to estimate soil moisture near the surface (~0-5 cm, L-band) [2] or at deeper layers (~20-30 cm, P-band) [3]. However, typi...

A P-band SAR moisture estimation method is introduced for complex soil permittivityand penetration depth estimation using fully polarimetric P-band SAR signals. This methodcombines eigen- and model-based decomposition techniques for separation of the totalbackscattering signal into three scattering components (soil, dihedral, and volume). Theincorp...

Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decad...

Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks. Such models, recently coined as foundation models, have been transformational to the field of natural language processing. While similar models have also been...

A method for estimating complex soil permittivity (or moisture) and penetration depth based on SAR decomposition is presented. By combining model- and eigenbased decomposition techniques in a non-iterative way, SAR observations are separated into single scattering components (soil, vegetation). The proposed method incorporates a multilayer rough su...

Multi-spectral satellite imagery provides valuable data at global scale for many environmental and socio-economic applications. Building supervised machine learning models based on these imagery, however, may require ground reference labels which are not available at global scale. Here, we propose a generative model to produce multi-resolution mult...

Regularly updated and accurate land cover maps are essential for monitoring 14 of the 17 Sustainable Development Goals. Multispectral satellite imagery provide high-quality and valuable information at global scale that can be used to develop land cover classification models. However, such a global application requires a geographically diverse train...

Semantic segmentation of satellite imagery is a common approach to identify patterns and detect changes around the planet. Most of the state-of-the-art semantic segmentation models are trained in a fully supervised way using Convolutional Neural Network (CNN). The generalization property of CNN is poor for satellite imagery because the data can be...

Tackling data challenges and incorporating physics into machine learning models will help unlock the potential of artificial intelligence to answer Earth science questions.

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. T...

The continental tropics play a leading role in the terrestrial energy, water, and carbon cycles. Land–atmosphere interactions are integral in the regulation of these fluxes across multiple spatial and temporal scales over tropical continents. We review here some of the important characteristics of tropical continental climates and how land–atmosphe...

The role of remote sensing in understanding earth systems is growing rapidly, in part due to advances in new machine learning (ML) techniques. These approaches typically rely on large, spatially extensive training datasets to predict categories or continuous quantities. These training data are typically collected by digitizing polygons from high sp...

Active microwave-based retrieval of soil moisture in vegetated areas has uncertainties due to the sensitivity of the signal to both soil (dielectric constant and roughness) and vegetation (dielectric constant and structure) properties. A multi-frequency acquisition system would increase the number of observations that may constrain soil and/or vege...

Advances in computer vision are improving the ability to accurately extract structured information from frequent and high-resolution satellite imagery, shedding light on global challenges and furthering Sustainable Development Goals. While these advances, along with increased availability of high capacity computational resources, result in improved...

Understanding the climatic and direct human drivers of abrupt drying of lakes in many parts of the world is a high research priority, particularly for water resources management and restoration. Lake Urmia, a shallow endemic lake in north-west Iran and one of the major saltwater bodies on earth, has undergone a dramatic decline in its water level (...

The continental tropics play a leading role in the terrestrial water and carbon cycles. Land–atmosphere interactions are integral in the regulation of surface energy, water and carbon fluxes across multiple spatial and temporal scales over tropical continents. We review here some of the important characteristics of tropical continental climates and...

Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvan...

Lake Urmia—a shallow endemic hypersaline lake in northwest Iran—has undergone a dramatic decline in its water level (WL), by about 8 m, since 1995. The primary cause of the WL decline in Lake Urmia has been debated in the scientific literature, regarding whether it has been predominantly driven by atmospheric climate change or by human activities i...

Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality land cover classifications, publ...

Characterizing soil moisture at spatiotemporal scales relevant to land surface processes (i.e., of the order of 1 km) is necessary in order to quantify its role in regional feedbacks between the land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture information...

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inc...

Satellite-retrieved Solar Induced Chlorophyll Fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inc...

Climate change is altering the dynamics, structure and function of the Amazon, a biome deeply connected to the Earth’s carbon cycle. Climate factors that control the spatial and temporal variations in forest photosynthesis have been well studied, but the influence of forest height and age on this controlling effect has rarely been considered. Here,...

Climate change is altering the dynamics, structure and function of the Amazon, a biome deeply connected to the Earth’s carbon cycle. Climate factors that control the spatial and temporal variations in forest photosynthesis have been well studied, but the influence of forest height and age on this controlling effect has rarely been considered. Here,...

Understanding the scattering mechanisms from the ground surface in the presence of different vegetation densities is necessary for the interpretation of P-band Synthetic Aperture Radar (SAR) observations and for the design of geophysical retrieval algorithms. In this study, a quantitative analysis of vegetation and soil scattering mechanisms estima...

Supporting Information S1

Solar‐induced fluorescence (SIF) observations from space have resulted in major advancements in estimating gross primary productivity (GPP). However, current SIF observations remain spatially coarse, infrequent, and noisy. Here we develop a machine learning approach using surface reflectances from Moderate Resolution Imaging Spectroradiometer (MODI...

Characterizing soil moisture at spatio-temporal scales relevant to land surface processes (i.e. of the order of a kilometer) is necessary in order to quantify its role in regional feedbacks between land surface and the atmospheric boundary layer. Moreover, several applications such as agricultural management can benefit from soil moisture informati...

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling...

A neural network (NN) soil moisture retrieval product computed from the synergy of AMSR-E brightness temperature and ASCAT backscatter observations is evaluated against in situ soil moisture observations from the International Soil Moisture Network (ISMN). The skill of the NN retrieval is compared to that of the ESA-CCI soil moisture retrieval as w...

The terrestrial biosphere and atmosphere interact through a series of feedback loops. Variability in terrestrial vegetation growth and phenology can modulate fluxes of water and energy to the atmosphere, and thus affect the climatic conditions that in turn regulate vegetation dynamics. Here we analyse satellite observations of solar-induced fluores...

Surface soil moisture has a direct impact on food security, human health and ecosystem function. It also plays a key role in the climate system, and the development and persistence of extreme weather events such as droughts, floods and heatwaves. However, sparse and uneven observations have made it difficult to quantify the global distribution and...

A new global estimate of surface turbulent fluxes, including latent heat flux (LE), sensible heat flux (H), and gross primary production (GPP) is developed using remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. The approach uses an artificial neural network (ANN) with a Bayesian perspective to learn...

Understanding the scattering mechanisms from the ground surface in the presence of different vegetation densities is necessary for the interpretation of P-band Synthetic Aperture Radar (SAR) observations and for the design of geophysical retrieval algorithms. In this study, a quantitative analysis of vegetation and soil scattering mechanisms estima...

This study aims to integrate environmental data for drought monitoring to reduce uncertainty in urban drought characterization as part of the smart city framework. Currently, drought monitoring in urban areas is a challenge. This is due, in part, to a lack of knowledge on the subject of urban droughts and urban drought vulnerability. A critical par...

This study aims to integrate environmental data for drought monitoring to reduce uncertainty in urban drought characterization as part of the smart city framework. Currently, drought monitoring in urban areas is a challenge. This is due, in part, to a lack of knowledge on the subject of urban droughts and urban drought vulnerability. A critical par...

Validation of precipitation estimates from various products is a challenging problem, since the true precipitation is unknown. However, with the increased availability of precipitation estimates from a wide range of instruments (satellite, ground-based radar, and gauge), it is now possible to apply the triple collocation (TC) technique to character...

Ensemble-based data assimilation techniques are often applied to land surface models in order to estimate components of terrestrial water and energy balance. Precipitation forcing uncertainty is the principal source of spread among the ensembles which is required for utilizing information in observations to correct model priors. Precipitation field...

On July 02 and 03, 2015 around 40 academics, students, government officials and concerned individuals gathered at Tufts University and then at MIT, under the auspices of the Tufts Institute of the Environment and its Water Diplomacy Program, and with the support of MIT and its Iranian Studies Group, along with a half dozen scientific sponsors, to a...

The NASA Soil Moisture Active Passive (SMAP) mission is designed to produce high-resolution (9 km) global mapping of surface soil moisture based on L-band radar and radiometer measurements. The multi-scale measurements are combined using time-series of active passive microwave data to retrieve the statistical regression parameters (α, β) from succe...

Validation of precipitation estimates from various products is a challenging problem, since the true precipitation is unknown. However, with the increased availability of precipitation estimates from a wide range of instruments (satellite, ground-based radar, and gauge), it is now possible to apply the Triple Collocation (TC) technique to character...

The record of global precipitation mapping using the Special Sensor Microwave/Imager (SSM/I) instrument measurements now extends over two decades. Similar measurements, albeit with different retrieval algorithms, are to be used in the Global Precipitation Measurement (GPM) mission as part of a constellation to map global precipitation with more fre...

Satellite-derived retrievals of precipitation have increased in availability and improved in quality over the last decade. There are now several satellites in orbit with instruments capable of precipitation retrieval with various degrees of accuracy, spatial resolution and temporal sampling. These retrievals have the advantage of almost full global...

This paper considers the characterization of uncertain spatial features that cannot be observed directly but must be inferred from noisy measurements. Examples of interest in environmental applications include rainfall patterns, solute plumes, and geological features. We formulate the characterization process as a Bayesian sampling problem and solv...

Predicting future probable values of model parameters, is an essential pre-requisite for assessing model decision reliability in an uncertain environment. Scenario Analysis is a methodology for modelling uncertainty in water resources management modelling. Uncertainty if not considered appropriately in decision making will decrease reliability of d...

Coastal regions have a high social, economical and environmental importance. Due to this importance the sea level fluctuations can have many bad consequences. In this research the correlation between the increasing trend of temperature in coastal stations due to Global Warming and the Caspian Sea level has been established. The Caspian Sea level da...

Several passive microwave satellites orbit the Earth and measure rainfall. These measurements have the advantage of almost full global coverage when compared to surface rain gauges. However, these satellites have low temporal revisit and missing data over some regions. Image fusion is a useful technique to fill in the gaps of one image (one satelli...

Satellite-derived retrievals of rainfall have increased in availability and improved in quality over the last decade. However, there has not been a reasonable assessment of the uncertainties associated with these retrievals. An elegant way to express these uncertainties is to generate a realistic ensemble of rainfall replicates that each replicate...

Locating in arid and semi-arid region of Asia, Iran is a dry country from the geographic and meteorological point of view and has been always facing water shortage problems. Existence of ancient dams, irrigations canals and Qanats shows the long lasting struggle of people to fight against drought condition. This water shortage has resulted in a spe...

According to the geographic and climatic situation of Iran, water has always had a strategic importance. Due to this importance, our ancestors in different times and by considering temporal and spatial needs have approved different rules and established organizations for appropriate and correct management. Water needs, due to increasing population,...

Iran is located in one of the arid and semi-arid regions of the world and due to a dry weather, has faced the problem of water shortage for long times. This long lasting problem has forced people from old times to be prepared for drought. Today there are many institutions and organizations which are involved in supplying water in Iran. Water supply...