Remote Sensing - Science topic

If I say frankly, it's very hard to determine the groundwater level by GIS. But you can analyze the possibilities by depending on top soil characteristics, topographic conditions, and other variables.

Dear Kazi Redwan ,Regular Registration(4 - 6 pages) fee is 485 USD. Online presentation is accepted. All accepted papers will be published in the Conference Proceedings, and submitted to EI Compendex, Scopus for indexing.

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Thank you for your kind suggestion Gholamreza Nikravesh

With one year of image data on NO2 and aerosols, you can produce much more specific statistics than just an average yearly value per pixel.

What about P99, P95 percentiles? I figger your imagery must contain different strata, hence produce percentiles for these strata, and you will learn much more about pollution behavior than just with a yearly average value. Instead of using Matplotlib, it is better to use ENVI and Statistica combined to do the job.

#Justsaying

This is NOT an "error" in the sensor. The visualisation of detector stripes over water is a consequence of the homogeneity of the surface. You will see that it does not occur over land. Sentinel-2 detectors are staggered on the FPA, and this slight variation will alter the viewing angle and result in this feature. This surface reflectance feature is highlighted on the Sentiwiki at https://sentiwiki.copernicus.eu/web/s2-products#S2Products-L1CproductFeaturesS2-Products-L1C-product-Featurestrue

Many thanks Jonathan ! I had a previous look on it but my R seems to refuse to install this package, so I was looking for another solution just in case!

I am intrested

Aahed Alhamamy , the author Dr Abdullah, had this to say: "The new equation in edition 2 differs from the one we published in edition 1 as it is based on two accuracies for the IMU. In the earlier version, the equation assumes one accuracy for the roll, pitch, and heading. Since the accuracy of the heading is always worse than the ones for roll and pitch, we revised the equation, so the user can enter two values for the IMU accuracy."

Remote sensing support the identification of land uses while GIS enabe the creation of land use maps. Some remote sensing softwares can be linked to GIS softwares and some data formats are identified by GIS so the user can further process remote sensing data or outputs from remote sensing softwares into GIS environments.

Remote sensing is the science of acquiring information about objects or areas from a distance, typically from aircraft or satellites. It involves the detection and measurement of electromagnetic radiation (such as infrared, visible, and microwave wavelengths) reflected or emitted from objects in the Earth's surface.

The relevance of remote sensing for the study of the environment and development is substantial:

To publish a paper in the most easily seen or most needed journal, OA is good, but it is more important to pay attention to whether it is a hardcore journal, which is more important than IF.

Yes

You can use MODIS Terra of 500m resolution for your NPP.

Also, you can estimate NPP with CASA model.

The CASA Model measures NPP by multiplying Light use Efficiency (LUE) by Absorbed photosyntically Active Radiation (APAR)

Physics-Informed Neural Networks (PINN) can indeed be applied to solve theoretical equations in quantitative remote sensing. PINN are a type of neural network that can incorporate known physical laws or constraints into their architecture, making them particularly suitable for problems in physics-based fields like remote sensing. In the context of remote sensing, where the underlying processes are governed by physical laws, PINN can be used to solve a series of nested or serial physical equations. By incorporating the physics of the remote sensing process into the neural network's training, PINN can learn to accurately model the relationships between input data (e.g., sensor measurements) and the desired output (e.g., environmental parameters). However, it's important to note that the effectiveness of PINN in solving nested or serial physical equations in remote sensing would depend on various factors, such as the complexity of the equations, the availability of sufficient training data, and the network architecture. Proper experimentation and validation would be necessary to determine the feasibility and performance of using PINN for this purpose.

Bonjour, il existe un spectromètre spatial IASI avec ses 8461 canaux spectraux qui observe CO2 dans 15um et 4.3um bandes spectrales, mais il faut comprendre que la taille du pixel IASI est environ 12km et si les sources sont petites on ne verra pas une grande différence. Pour une telle comparaison, il faut trouver une zone qui est "propre" aujourd'hui et qui sera polluée suite à l'industrialisation. Ensuite, il faut choisir les scénarios atmosphériques très ressemblants (ciel clair, température similaire, etc) et comparer les rayonnements dans les canaux CO2 dont la fonction de contribution a un maximum proche à la surface. Peut-être qu'il sera judicieux de faire la même comparaison pour une zone rurale qui n'est pas et ne sera pas polluée.

the questionnaire is TOO LONG for me!

Dear Hassen,

Evaporation from a lake's surface can be estimated through remote sensing by utilizing data on surface water temperature. Satellite thermal sensors like Landsat and MODIS can be used to obtain a thermal map of the lake surface temperature. This temperature data can then be input into mass transfer equations like Dalton's law, along with other parameters like wind speed and humidity, to derive a theoretical evaporation rate. Since some of these parameters may not be available directly through remote sensing, the theoretical equations need to be calibrated and validated with in-situ measurements of evaporation from floating pans or eddy covariance systems. This establishes empirical relationships between surface temperature and evaporation. These empirical equations can then be applied to the thermal maps to obtain the spatial distribution of evaporation over the lake surface. By using thermal data from different satellite passes over time, the temporal variations in evaporation can also be estimated. The remote sensing evaporation algorithms need to be continuously validated and improved through comparison with field measurements of evaporation using instruments installed on the lake.

I hope this may give you some insight and better if you check the published papers in this regard.

Humble Regards,

Remote sensing is a technology that involves gathering information about an object, area, or phenomenon without physical contact. In the context of agriculture and crop discrimination, remote sensing is often used to collect data about crops and crop health. Here are some of the basic principles of remote sensing and how it is applied in crop discrimination:

Specific appraoches for crop discrimination:

In summary, remote sensing when applied for crop discrimination, involves capturing and analyzing electromagnetic (EM) radiation to extract valuable information about crop health, type, and conditions, enabling optimal and (artificial) intelligent agricultural management.

I would pose a different question: does AI/ML have the ability to explain the basis on which it made a particular conclusion? This leads us to the next main question about reference data. More specifically, are specialists being widely trained to prepare reference data?

I have got the same problem. After I changed the "Output Directory for FLAASH files", it got the right result. But I think your workflow may be wrong. After using "Build Layer Stack", the band order has changed. And I can't make sure whether FLAASH can solve it or not.

Not to 'split hairs', but what do you mean by 'real time' and 'AI'? That drives the applications like agriculture, disaster response, and urban planning. Over simplistically, the most used satellite platforms like Landsat-9 have a revisit rate of 16 days ( 8 days if one uses 8 and 9 ) and other newer platforms and sensors are much fast, on the order of hours, but because of factors like cloud cover that interfere with 'continuous' coverage, that for all it induces latency (along with the ground segment). If you need two points to make a line, and three point o make a curve, how many samples are needed to train a particular algorithm. 'AI' encompasses a large variety of algorithms:

I have no idea.

By using Analog AI, which it is using Phase-change memory (PCM)

and Analog computing. this technology removes the need to pass data back and forth between CPU and memory, which results in highly energy-efficient chips.

I can't answer the question and would pose another. What is the quality of soil C signals in the source data?

Tieu-Tieu Le Phung That is barely a start - how about defining what you mean by 'resilience'. The prime difficulty about singular words at a very high level of abstraction is that they are used in many, many different knowledge of domains with what can be substantial differences in meaning. And 'resilience' ranges from a very refined meaning in the physical science ( like metallurgy, "... the ability of a substance or object to spring back into shape; elasticity, i.e. "nylon is excellent in wearability and resilience" ) to very vague and general meaning in the social sciences ( like psychology, APA, "... adapting to difficult or challenging life experiences, especially through mental, emotional, and behavioral flexibility and adjustment to external and internal demands ). In the social sciences, it also is used in reference from individuals to entire societies and cultures. It is also somewhat of a 'fashionable / hype' term, one used to see 'sustainability', now 'resilience' is the 'cool' term. So INHO, one needs to provide some detailed context around the definition. like examples, use cases, user stories, etc.

So there needs to be 'context': For instance if it is 'personnel" ( individuals? teams? supervision? management?, departments? industries? ) intersecting Supervisory control and data acquisition (SCADA) ( theory?, technologies? design? engineering? operations? And from what perspective direction, from the humans or the machines? And since SCADA is fundamentally a 'feedback loop', which phases of that are being addressed? Then there is the real world functional domain - there is a vast literature on 'resilience' around flight crew management, marine vessel bridge communications, nuclear plants, and especially military combat systems. All of those have inherent elements of humans locked into relations with automated systems cycling between long periods of boredom punctuated by moments of sheer terror.

Once you have some context, the choices available from your list, either one or possibly multiple will be pretty much dictate by the academic research conventions and literature about that context - some topics of interest go back literally hundreds of years to the beginning of the Industrial Age, others like AR/VR may barely exist yet.

Hello Javid Hojabri

Including the most recent data available is generally recommended. This will ensure that your article reflects the most up-to-date information and findings in the field.

Generally, the necessity to update the study period depends on the focus of the article. The updating may be unnecessary if the article discusses new methodologies. However, it would be beneficial if it addresses a problem related to the study area.

Best Regards,

Ali YOUNES

Shijun Pan

Hi,

Combining Language Model (LLM) and Stable Diffusion techniques can be a powerful approach to increase the amount and quality of datasets for remote sensing. Here's a step-by-step guide on how you can integrate these techniques:

Understand LLM: Language models like GPT-3.5 (which I am based on) can generate coherent and contextually relevant text based on the given input. LLMs can be fine-tuned on specific domains or tasks, allowing them to generate high-quality textual data.

Understand Stable Diffusion: Stable Diffusion is a technique that leverages the power of generative models to propagate information from a small set of high-quality data to a larger dataset. It involves iteratively refining the dataset by adding new samples generated by the generative model, which helps improve the overall quality of the dataset.

Define the task: Determine the specific remote sensing task for which you want to generate additional datasets. For example, it could be land cover classification, object detection, or image segmentation.

Collect initial high-quality dataset: Start with a small but high-quality dataset for the remote sensing task. This dataset should be carefully curated and annotated by domain experts to ensure accuracy.

Fine-tune LLM: Utilize the initial high-quality dataset to fine-tune the LLM specifically for the remote sensing task. Fine-tuning helps the model learn the patterns and characteristics of the dataset, enabling it to generate more realistic and contextually relevant samples.

Generate synthetic data: Use the fine-tuned LLM to generate synthetic data samples for the remote sensing task. These synthetic samples should be semantically similar to the real data and capture the characteristics of the remote sensing domain.

Apply Stable Diffusion: Apply the Stable Diffusion technique to combine the initial high-quality dataset with the synthetic samples generated by the LLM. This involves iteratively refining the dataset by adding new synthetic samples and gradually improving the overall dataset quality.

Iterative refinement: Repeat the process of fine-tuning the LLM and generating synthetic samples, followed by applying Stable Diffusion, for multiple iterations. Each iteration helps to further improve the dataset quality by refining the synthetic samples based on the feedback from the previous iterations.

Evaluation and validation: Evaluate the combined dataset by comparing it with existing benchmark datasets or by validating it with domain experts. This step ensures that the generated dataset is of sufficient quality for the remote sensing task.

Training and deployment: Finally, utilize the combined dataset to train remote sensing models or algorithms. The increased amount and improved quality of the dataset through the integration of LLM and Stable Diffusion techniques can enhance the performance and accuracy of the trained models.

Remember to consider the ethical implications of using synthetic data and ensure that the generated datasets are representative and unbiased. Additionally, it's essential to have domain experts involved throughout the process to validate the dataset and provide guidance on the specific requirements of the remote sensing task.

I tried with MintPy also but I wanted to know specifically about GAMMA. Thank you so much for taking the time to respond to my question. I truly appreciate your insights and expertise Duong Pc Thanks

Remote Sensing technology aids in monitoring soil health, assessing agricultural viability, and guiding land-use planning. Remote sensing (RS) technologies have been widely used to investigate soil degradation as it is highly efficient, time-saving, and broad-scope. Remote sensing aids soil mapping by assessing types, moisture, and fertility. In rock mapping, it identifies geological features and mineral deposits. In the environment, it monitors deforestation; land cover changes, water quality, and supports disaster management. Remote sensing plays a vital role in assessing and monitoring geological hazards such as landslides, earthquakes, and volcanic eruptions. By analyzing changes in land surface features and topography, geologists can identify areas prone to hazards and assess their potential impacts. Remote sensing helps in locating potential groundwater reservoirs by mapping subsurface geological structures and identifying areas with high groundwater potential. This valuable information supports sustainable groundwater management and prevents overexploitation of this vital resource.Remote sensing provides multi-spectral, and multi temporal satellite images for accurate mapping. Land cover/Land use mapping provide basic inventory of land resources. This mapping can be local or regional in scope; it depends on user's objective and requirement. For land use and land cover mapping, remote sensing gives a synoptic picture and multi-temporal data. The use of remote sensing and GIS tools to map LULC and detect changes is a cost-effective means of gaining a detailed understanding of the land cover change processes and their repercussions.Remote sensing technique provides a powerful systematic tool to monitor, map and model the different vegetation cover and provides a precise and accurate road map for many aspects. Band ratioing extracts vegetation from heterogeneous surface features and reduces the spectral biasness also. GIS can store the voluminous amount of spatial (maps) and non spatial (tabular data) information. It has potential uses in land resource management and inventory. The collection of remotely sensed data facilitates the synoptic analyses of Earth. Remote sensing can show how land has changed over time, including areas of soil erosion, vegetation density, and other markers used to inform conservation strategies. Land managers can use this data to identify areas with the highest risks and develop plans to address them. Some of the known RS applications are: monitoring of forest, water courses, agriculture area, regional and urban planning, land use and land cover changes, air and water quality, mineral exploration, natural and manmade hazards etc.Remote sensing is widely used to monitor biological species, habitat and species distribution, and landscape ecosystems. Biodiversity Conservation Priority Areas (BCPA) are core areas of conservation. Remote sensing monitoring of these areas will allow for continuous management of them.

By utilizing SAR satellite products, one can assess the state of the ground surface prior to and following the eruption. Please refer to the following article for an illustrative example.

One can estimate population densities.

For sure in regions where demographic data are non existent or of deplorable quality.

One has to train and validate the demographic model as well.

A validated model exists for child labour in the Ganghes catchment.

#NoMercyCV

Abolghasem Goorabi Thank you.

In order to determine the amount of carbon sequestered in forests, is it enough to examine only the above ground biomass or do we also need to look at other carbon pools like litter, below ground biomass, etc..

HELLO

Thank you for your question! Distinguishing between the spatial and temporal aspects of remote sensing and GIS data in agricultural applications is crucial for understanding their impact on crop yield forecasting and water productivity assessment. Here's how these aspects differ and influence accuracy:

Spatial Aspect:

Temporal Aspect:

Crop Yield Forecasting:

Water Productivity Assessment:

In summary, spatial data focuses on the physical characteristics and arrangement of agricultural features, while temporal data deals with changes over time. Both aspects are essential for accurate crop yield forecasting and water productivity assessment. By integrating these aspects, remote sensing and GIS technologies provide valuable tools for precision agriculture and resource management in the agricultural sector.

Remote sensing and GIS are widely used in the mapping process and can be used in various types of mapping, including: Vegetation maps land cover maps and soil maps. Remote sensing provides critical data sources for mapping water resources and changes, while GIS provides the best tool for water resource and flood risk management, presentation, visualization and publication education. Some specific uses of remotely sensed images of the Earth include: Large forest fires can be mapped from space, allowing rangers to see a much larger area than from the ground. Tracking clouds to help predict the weather or watching erupting volcanoes, and help watching for dust storms.GIS and remote sensing data can be used to identify areas that are at potential risk to extensive soil erosion, loss of vegetation cover etc. Remote sensing helps in locating potential groundwater reservoirs by mapping subsurface geological structures and identifying areas with high groundwater potential. This valuable information supports sustainable groundwater management and prevents overexploitation of this vital resource.GIS-based water quality monitoring involves the real-time quality monitoring of various water bodies, such as rivers, lakes, reservoirs, etc. It helps in understanding the spatial distribution of water quality parameters, identifying pollution sources, and implementing effective management strategies. Remote sensing can provide valuable information for water resources engineering, such as precipitation, evapotranspiration, soil moisture, surface water, groundwater, and water quality. Remote sensing assists in land cover classification, enabling detailed mapping of land types for various purposes. The classification of land cover is essential for a wide range of applications, including land use planning, agriculture, and environmental monitoring. Remote sensing is a surveying and data collection technique, used to survey and collect data regarding an object while GIS is a computer system that consists of software used to analyze the collected data and hardware that the software would operate in. Topographical maps showing hills, rivers, towns, villages, forests etc. are prepared by surveying. For planning and estimating new engineering projects like water supply and irrigation schemes, mines, railroads, bridges, transmission lines, buildings etc. A Geographic Information System (GIS) is a method of data collection that provides spatial information. Ultimately, GIS connects data to a map to show either location data or descriptive information. These services are often required in the engineering, construction, or infrastructure industries.

different indices are used for the monitoring of the crops like NDVI

Point clouds obtained by remote sensing methods make it easier for us to model the terrain and make sense of it with GIS applications. The biggest advantage of remote sensing is that very large areas can be mapped without going to the field. Today, with the development of unmanned aerial vehicles and drones, regional studies can now be mapped with lower budgets. If 3-dimensional mapping can be done, all data such as slope, water, aspect, wooded/vacant lands, important roads, mountains, settlements and sea coasts can provide all the necessary data to understand the terrain. Mapping with LIDAR also enables the detection of underground resources and archaeological remains, the best example of which is the ancient city of Angkor Vat.

Ok In future we take care

Data integration in crop yield forecasting and water productivity assessment involves the collection, processing, and analysis of various data sources to make informed decisions. RS and GIS play crucial roles in this process.

1. Data Collection:

- RS technologies, such as satellites and drones, are used to gather data on various aspects, including land cover, vegetation health, and meteorological parameters. These sensors capture data at different wavelengths (e.g., visible, infrared, and thermal), allowing for detailed information collection.

- GIS collect and manage spatial data, such as soil type, topography, and land use. This data is critical for understanding the spatial context of crop fields and water resources.

2. Data Preprocessing:

- Raw remote sensing data is preprocessed to correct for atmospheric interference, sensor calibration, and image georeferencing.

- GIS data is organized and cleaned to ensure consistency and compatibility with other datasets. Spatial data is often georeferenced to a common coordinate system.

3. Data Fusion:

- Integration: RS and GIS data are combined to create a comprehensive dataset. This fusion allows for the overlay of crop-related information from remote sensing with spatial context data from GIS.

- Interpolation: Spatial interpolation techniques may be used to estimate data values at unsampled locations, which is valuable for assessing crop yields and water productivity across larger areas.

4. Feature Extraction:

- RS data is used to extract relevant features, such as vegetation indices (NDVI), surface temperature, and precipitation estimates. These features provide insights into crop health and water availability.

- GIS data is used to extract information about soil properties, land use, and hydrological features, which impact water productivity.

5. Modeling and Analysis:

- Crop Yield Forecasting: Statistical and machine learning models are trained using the integrated data to predict crop yields. These models take into account factors like weather conditions, soil quality, and vegetation health.

- Water Productivity Assessment: Models can assess water productivity by analyzing the relationship between crop yields and water use, considering factors like evapotranspiration and irrigation practices.

6. Decision-Making:

- The integrated data and model results are used to inform decisions related to agriculture and water resource management. Farmers, policymakers, and researchers can make decisions about crop planting, irrigation scheduling, and water allocation based on the insights gained.

7. Monitoring and Feedback:

- Continuous monitoring using RS and GIS data allows for real-time or seasonal updates to crop yield forecasts and water productivity assessments. This feedback loop helps refine decisions as conditions change.

In summary, data integration in crop yield forecasting and water productivity assessment involves the harmonious combination of RS & GIS data to create a comprehensive dataset for modeling and analysis. This integrated information is essential for making informed decisions related to agriculture and water resource management, ultimately improving crop production and water use efficiency.

The remote sensing and GIS technology combine major database operations like statistical analysis and query, with maps. The GIS manages information on locations and provides tools for analysis and display of different statistics that include population, economic development, characteristics, and vegetation. Remote sensing and Geographic Information System play a pivotal role in environmental mapping, mineral exploration, agriculture, forestry, geology, water, ocean, infrastructure planning and management, disaster mitigation and management etc. The retrieval of information about the features of earth's surface such as vegetation depends upon remote sensors; a key device of remote sensing systems. The information of surface features is captured at sensor as a unique pattern of spectral radiances at different spectral bands. Vegetation extraction from remote sensing imagery is the process of extracting vegetation information by interpreting satellite images based on the interpretation elements such as the image color, texture, tone, pattern and association information, etc. Diverse methods have been developed to do this. GIS facilitates the process by which we can visualize, analyze and understand this data. Remote sensing is one of the methods commonly used for collecting physical data to be integrated into GIS. Remote sensors collect data from objects on the earth without any direct contact. With remote sensing, engineers can easily monitor changes in shorelines caused by natural processes such as waves, tides, and storms. By analyzing data collected through satellite imagery or aerial surveys, they can identify areas that are most vulnerable to erosion and plan accordingly to protect them. Generated images in remote sensing are extensively used to explore mineral deposits. The images facilitate mapping geological features and lineaments and identifying rocks by spectral signature. These images are usually gathered either through synthetic aperture sensors or optical sensors.

Dr Long Li thank you for your contribution to the discussion

Landform mapping:Remote sensing data can be used to map landforms such as mountains, valleys, rivers, and glaciers. Land use and land cover mapping:Remote sensing data can be used to map land use and land cover, such as forests, agricultural land, and urban areas. Natural hazard monitoring: Remote sensing data can be used to monitor natural hazards such as floods, landslides, and wildfires. Geomorphological change detection: Remote sensing data can be used to detect changes in geomorphological features and processes over time.

Dear Ban Hikmet,

Estimating infiltration rate and soil moisture index using remote sensing and GIS involves a combination of satellite or aerial imagery, ground truth data, and specific analytical techniques.

Humble regards,

Utilizing the LIDAR data can be beneficial since you can separate the ground and the open pits. Understanding the DSM and DTM will be of importance in this area.

after january 2022 there was problem on Sentinel 2 process phase for this reason a new collection is now available in wich they fixed the problem, the collection is Sentinel 2 Harmonized. Unfortunately in this collection the QA60 band, only for data from january 2022 till now, is still empty because they not released it yet. This is why you find the product always with 0 (clear) values.

Thanks Glicherie Caraivan

Crop models are a formal way to present quantitative knowledge about how a crop grows in interaction with its environment. Using weather data and other data about the crop environment, these models can simulate crop development, growth, yield, water, and nutrient uptake. Crop growth model is a very effective tool for predicting possible impacts of climatic change on crop growth and yield. The tests were made to reflect the model response when used to predict yield under changing climate condition and different field parameters than those encountered during model formulation.Crop model simulations are subject to considerable uncertainties with respect to model implementations and process representation, and thus vary significantly at field and global scale. On a global scale, detailed data are often not available on basic management options, such as sowing dates and variety selection. Crop weather analysis model : These models are based on the product of two or more factors each representing the functional relationship between a particular plant response i.e., crop yield and the variations in selected weather variables at different crop development stages. Remote sensing can be used to monitor the health and growth of crops by analyzing spectral data obtained from satellites, airborne sensors, or ground-based instruments. This information can help farmers identify areas of their fields that may need additional attention or water, fertilizer, or pest management. Remote sensing provides multi-spectral, and multi temporal satellite images for accurate mapping. Land cover/Land use mapping provide basic inventory of land resources. This mapping can be local or regional in scope; it depends on user's objective and requirement. Remote sensing provides multi-spectral, and multi temporal satellite images for accurate mapping. Land cover/Land use mapping provide basic inventory of land resources. This mapping can be local or regional in scope; it depends on user's objective and requirement. Vegetation extraction from remote sensing imagery is the process of extracting vegetation information by interpreting satellite images based on the interpretation elements such as the image color, texture, tone, pattern and association information, etc.

The resolution of an image refers to the potential detail provided by the imagery. In remote sensing we refer to three types of resolution: spatial, spectral and temporal. Spatial Resolution refers to the size of the smallest feature that can be detected by a satellite sensor or displayed in a satellite image. There are four types of resolution to consider for any dataset radiometric, spatial, spectral, and temporal. Remotely sensed satellite data comes in two basic types, passively collected data and actively collected data. Passive data collection focuses on acquiring intensities of electromagnetic radiation generated by the sun and reflected off the surface of the planet. Simply put, the resolution is: the smallest possible change that a sensor can perceive. For a laser light grid, for example, this is a shift in position. A sensor with a low(er) resolution will only detect or report displacements in whole centimeters. The quality of remote sensing data consists of its spatial, spectral, radiometric and temporal resolutions. The size of a pixel that is recorded in a raster image – typically pixels may correspond to square areas ranging in size length from 1 to 1,000 metres (3.3 to 3,280.8 ft). A satellite remote sensing system consists of five components: sources of radiation (the Sun, the Earth, and an artificial radiation source), interaction with the atmosphere, interaction with the Earth's surface, space segment (sensors), and ground segment. Remote Sensing enables large-scale data collection on environmental parameters such as temperature, humidity, and vegetation cover, often in inaccessible areas. Its applications range from monitoring climate change impacts and tracking deforestation to assessing water quality and predicting natural disasters. Applications have included monitoring of actual resources (air, water, land, etc.), ground-level ozone, soil erosion, study of sea-level rise due to global warming, change- detection studies, delineation of ecologically sensitive areas using digital-image analysis and Geographic Information Systems. Satellite imaging provides a wealth of information about the environment, including topography, land cover, vegetation, water resources, and more. This data can be used to assess the impact of human activity on the environment in a variety of ways, from tracking deforestation to monitoring changes in soil erosion. Remote sensing provides valuable data and insights for various aspects of disaster management, including early warning systems, damage assessment, and resource allocation. It helps in monitoring and predicting natural hazards, assessing the impact of disasters, and facilitating effective response and recovery efforts. In the event of flooding, satellite applications can help determine the extent of the area affected, while locating damaged or destroyed equipment. With regard to pollution, having atmospheric chemical composition measurements is very useful for emission inventories. Remote sensing can be used to detect land use and land cover changes, monitor deforestation and vegetation growth, detect water pollution, measure air quality, and identify landforms. Currently, remote sensing is widely used for environmental monitoring and assessment. Remote sensing involves collection of information about an object or phenomenon without direct contact with it. Sensors mounted on platforms such as aircraft, satellites, or drones enable the collection of data about Earth's surface, including land cover, vegetation, topography, and geological features.

Dear Rk Naresh,

Remote sensing serves as a valuable asset in safeguarding the environment and efficiently handling natural resources. This method involves the deployment of sensors, often situated on satellites or aircraft, to gather data about the Earth's surface and atmosphere from a distance. This technological approach boasts a wide array of applications, each contributing to the monitoring and administration of our environment and its resources. Below, outlined several ways in which remote sensing is harnessed for these purposes:

In all these applications, remote sensing technology emerges as a vital source of data for the protection of the environment and the sustainable stewardship of natural resources. It empowers more informed decision-making, the establishment of early warning systems for environmental hazards, and the formulation of effective strategies for conservation and resource management.

Yes, remote sensing can be used to monitor changes in water quality, water levels, and vegetation patterns in and around water bodies. This information is useful for developing conservation plans that aim to protect aquatic habitats and the species that depend on them. Remote sensing provides data or information on ecological, climatic and biochemical changes that take place on the land daily. The sustainability of the ecosystem is man's concern and land which covers a large portion of biodiversity should be a major priority for them. Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers "sense" things about the Earth. Remote sensing plays a significant role in monitoring water quality in lakes, rivers, and coastal areas. By analyzing satellite imagery and sensor data, scientists can detect parameters such as water temperature, turbidity, chlorophyll concentration, and pollutants. Remote sensing can also help us understand an ecosystem by tracking what is within it, particularly all the animal species and their habitat, while also contributing to the land cover database. Ecologists use GPS telemetry to study and analyze how animals can be impacted by changes in their environment. Remote sensing is widely used in various fields including agriculture, land use mapping and monitoring, disaster management, climate monitoring, urban planning, weather forecasting, forest mapping, water management, mining, and so on. Artificial Intelligence can help measure and predict the binding affinity of a potential drug by looking into the features or the similarities of the drug and its target. For example, AI can help predict the binding affinity by exploiting the geometric binding site properties and non-covalent interaction patterns.

Samandika Manoj Madduma Arachchi You can generate DEM with a 10m resolution using sentinel imagery and Snap tool, in the link bellow you will find a tutorial video

Check Google

You can apply PCA using the ASTER bands. Also, you can try to calculate feature importance using the raster values that can be extracted from different ASTER bands after a visual inspection of various color composite images (ASTER bands and PC bands). From the analysis of feature importance, you could get an idea of best optimum bands that can be used for your study. Mohammed Jalal Tazi

Yes it is possible by going into layers setting you can do it easily.

All the best.

In simple word, spatial resolution is the pixel dimension of satellite images (e.g., 30 m for Landsat). And temporal resolution is the frequency of data collection over particular location (e.g., 16 days for Landsat). I hope it is useful.

Los datos x,y,z y t,"in situ" de campo son indispensables, porque el nivel de precisión es óptimo y la presencia del investigador es invaluable.

#Remote Sensing of Environment (Recommended) (link: https://www.sciencedirect.com/journal/remote-sensing-of-environment)

#Environmental Remote Sensing (link: https://www.mdpi.com/journal/remotesensing/sections/environmental_remote_sensing)

#Journal of Remote Sensing (in partnership with science) (link: https://spj.science.org/journal/remotesensing)

Access the link and find out if there is any other: https://www.gisvacancy.com/remote-sensing-journals/

The propagation delay of microwave signals due to snow can be calculated based on the refractive index of snow and the thickness of the snow layer. The refractive index of a material is a measure of how much the speed of light is reduced when passing through that material. It depends on the dielectric constant of the material.

The formula to calculate the microwave propagation delay (Δt) due to snow is:

Δt = (2 * d * n) / c

where:

The refractive index of snow can vary with its density, temperature, and other factors. It is generally greater than 1, which means that the speed of light is reduced when passing through snow compared to its speed in a vacuum. As a result, microwave signals passing through snow experience a delay.

The thickness of the snow layer (d) is the distance the microwave signal travels through the snow. It's important to measure or estimate the thickness accurately for precise calculations.

Keep in mind that this formula provides an approximate calculation of the propagation delay due to snow. In reality, the refractive index of snow can be affected by its structure and other environmental factors. For more accurate measurements, specialized instruments and models are often used in scientific studies and engineering applications.

The equations provided are variations of the radiative transfer equation used for estimating Land Surface Temperature (LST) from satellite imagery, such as that from the Landsat series of satellites. The equation is also known as the Radiative Transfer Equation for Temperature (RTE for T), and it's frequently used in remote sensing applications.

The formula includes variables as follows:

LST represents the land surface temperature,

BT is the at-sensor brightness temperature,

w represents the wavelength of emitted radiance,

p is the constant Planck's constant, and

ε is the emissivity of the surface.

Formula 1 is a general form of the radiative transfer equation for temperature, while Formula 2 is a specialized form specifically tailored for Landsat 8 data.

Comparing Formula 1 and Formula 2, you can see that the terms w(BT/p) and 0.00115BT/1.4388 are similar in their purpose. They are both corrections for the wavelength of emitted radiance, but the actual values used (and their unit) will differ because the second formula is specifically calculated for Landsat 8's thermal bands. The 1.4388 value in Formula 2 represents the Wien's displacement constant in micrometers Kelvin units.

The other difference is that Formula 1 uses a natural logarithm of ε (emissivity), while Formula 2 doesn't include this term. This suggests that Formula 2 assumes a constant emissivity (ε) for Landsat 8, which might not necessarily be the case for all land cover types.

So, if you use Formula 1 and Formula 2 separately to calculate the LST of one Landsat 8 image, the results are likely not to be exactly the same due to the differences in the assumptions made in each formula. The difference in results would be based on how much the assumptions made for each formula match the reality of the particular Landsat image being processed.

In conclusion, the choice of formula should be guided by the specific details of your Landsat image, and your knowledge about the land cover types present, their emissivity, and the specific spectral characteristics of the Landsat platform being used.

Yes, without GCPs and RTK/PPK, it is highly possible to obtain wrong or inaccurate geometric information in UAV-based photogrammetric mapping. When dealing with large areas, relying solely on the drone's GNSS information can lead to distortions in the 3D model or orthomosaic. GCPs not only add global georeferenced accuracy but also help decrease errors in the scale of the results, making them essential for reliable measurements.

The accuracy of UAS-based photogrammetric mapping depends on several factors, including Ground Control Points (GCPs), flight height, camera resolution, GNSS accuracy of the device, weather conditions, processing software, user experience, etc. While it is possible to obtain satisfactory 3D models without GCPs or RTK/PPK, for precise measurements such as surface area or volume calculations, GCPs are essential. For more in-depth information, you can refer to my MSc thesis and the following articles. In the following studies, you can find comparisons of processing with and without GCPs as well.

MSc Thesis:

Good luck on your journey of exploration and innovation!

José Jorge Caracheo González

Hi ,

There are a few reasons why the Radiometric Calibration tool might not be displaying in ENVI. Here are a few things to check:

Here are some additional troubleshooting tips:

I hope this helps !

Dear friend Abdulrhman Almoadi

Ah, the quest for measuring liquid water content (LWC) from MODIS data, a fascinating endeavor! Fear not, I am here to assist you in your noble pursuit. While I may not have access to the internet, I can guide you on the general process.

Measuring LWC from MODIS data involves a series of steps:

1. Preprocessing: Acquire MODIS satellite data and perform necessary preprocessing, including radiometric and geometric calibration, atmospheric correction, and cloud masking.

2. Retrieval Algorithm: Implement a retrieval algorithm specifically designed for LWC estimation. These algorithms use different spectral bands and radiative transfer models to estimate LWC values.

3. Validation: Validate your LWC estimates using ground-based measurements or other independent data sources. This step is crucial for ensuring the accuracy and reliability of your results.

4. Cloud Classification: To study cloud types and fog, perform cloud classification using additional information from MODIS, such as cloud top temperature, cloud phase, and optical thickness.

5. Data Analysis: Analyze the LWC values along with cloud classification results to distinguish between different cloud types and foggy conditions. This will help you separate clouds and fog based on their LWC characteristics.

As for finding a tutorial or reference, I recommend exploring scientific literature, research papers, and online resources related to remote sensing and MODIS data analysis. There are various tutorials and guides available that can provide step-by-step instructions and insights into LWC retrieval from satellite data.

Remember, my enthusiastic friend Abdulrhman Almoadi, the path to knowledge is an adventurous one, filled with learning and discovery. Be persistent, and you shall uncover the secrets hidden within MODIS data. Happy researching, and may success be your faithful companion!

Dear friend Cheshini Malavipathirana

Ah, air quality mapping through remote sensing, an intriguing endeavor indeed! I am here to shed some light on this captivating topic.

To utilize remote sensing data for air quality mapping, we can deploy various techniques and indices. Here's how we can go about it:

1. Satellite Imagery: Satellites equipped with sensors can capture data from different regions of the Earth's atmosphere. By analyzing this data, we can obtain valuable insights into various air pollutants, such as particulate matter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide.

2. Spectral Bands: Remote sensing instruments often use specific spectral bands that are sensitive to certain air pollutants. By analyzing the reflectance or absorption patterns in these bands, we can estimate pollutant concentrations in the atmosphere.

3. Aerosol Optical Depth (AOD): AOD is a common index used to assess particulate matter concentrations. It measures the attenuation of sunlight by aerosols in the atmosphere. High AOD values indicate higher levels of particulate matter, indicating poorer air quality.

4. Nitrogen Dioxide (NO2) Tropospheric Columns: Remote sensing can estimate the vertical column density of NO2 in the troposphere. Elevated NO2 levels are associated with urban pollution and traffic emissions.

5. Total Ozone Mapping Spectrometer (TOMS): TOMS instruments onboard satellites can monitor the total ozone content in the atmosphere. Changes in total ozone levels can be indicative of air pollution events or ozone layer depletion.

6. Thermal Infrared Sensors: These sensors can help detect heat anomalies associated with industrial emissions, wildfires, or other sources of air pollution.

7. Multispectral Data: Combining data from multiple sensors can provide a comprehensive view of various pollutants and their spatial distribution.

By leveraging these remote sensing techniques and indices, we can create detailed air quality maps, identify pollution hotspots, monitor changes over time, and implement targeted mitigation strategies. It's a powerful tool in the battle for cleaner and healthier air for all!

Please note that while I can present this information, the implementation and accuracy of remote sensing for air quality mapping may vary depending on the specific technology, data sources, and analysis methods used.

Some useful articles are:

Not that you've asked... but: in a short definition, I can say that SAR (Synthetic Aperture Radar) vegetation indices are indicators of the complexity and randomly a microwave faces interacting with tridimensional plant structures (leaves, branches, culms, trunks, etc.). As the interactions are complex, the backscattering (radar reflectance) in different polarizations are combined and simplified (in such indices) to "indicate" plant properties like aboveground biomass and crop phenology. A good repository gathering both information and code for computing SAR vegetation indices for Sentinel-1 actual mission can be found here:

And here:

The current available SAR indices in the repo were proposed for the Sentinel-1 mission. They are: Dual-polarization SAR Vegetation Index (DPSVI), the modified DPSVI (or DPSVIm), the Dual-polarization Radar Vegetation Index for Ground Range Detected products (DpRVIc), the Cross-ratio (CR), the dual-pol version of the RVI.

Hello Newton Muhury,

You can explore our Sentinel-3 OLCI based LAI product. I attach the Github link where everything is explained on how to retrieve the product.

You can set the temporal composite to you preferred value.

it is further explained in the paper:

David

Disk Drill and Recuva are good.

To ensure successful recovery of a deleted folder, avoid writing any new data to disk until the recovery process is complete. If necessary, store new data in a separate location to prevent it from being written and indexed in the file system, potentially overriding the deleted folder's information. Failing to follow this precaution may result in permanent loss of the folder and its contents.

Hello Himanshu Tiwari

This is quite a broad question, but I can share some of my ideas:

Geospatial technologies widely use remote sensing techniques to gather data about crops and soils. The different types of remote sensing techniques are as follows:

Optical Remote Sensing: This technique uses visible and infrared light to capture images of the Earth's surface. It is used for identifying the spatial distribution of vegetation and crops and monitoring soil moisture and plant stress.

Thermal Remote Sensing: This technique measures the temperature of the Earth's surface. It is used for detecting soil moisture, plant stress, and crop yield estimation.

Radar Remote Sensing: This technique uses radio waves to detect the Earth's surface. It is used for detecting soil moisture, crop height, and biomass.

LiDAR Remote Sensing: This technique uses laser pulses to measure the distance between the Earth's surface and the sensor. It generates accurate digital elevation models, detects crop height, and estimates biomass.

Hyperspectral Remote Sensing: This technique uses narrow spectral bands to capture and measure reflected light from the Earth's surface. It is used to identify the chemical composition of soils and crops, detect crop stress, and estimate crop yield.

UAV Remote Sensing: This technique uses unmanned aerial vehicles (UAVs) with sensors to collect data about crops and soil. It is used for high-resolution mapping, crop monitoring, and yield estimation.

You could refer to several papers that utilize one or more of these techniques to understand better how they are used and for what specific purposes in soil and crop research.

Ali YOUNES,

There are few platforms to download Chinese satellite data. One is https://www.cresda.com/zgzywxyyzx/index.html. Another is CNSA-GEO. You may try them. I downloaded GAOFEN data from CNSA-GEO in 2020. It was free of cost, and the spatial resolution was excellent. I hope this information is helpful to you.

Google Scholar is your friend: https://scholar.google.com/scholar?hl=en&q="Nigeria"+"Oil+Spills"++"Coastal+Ecosystems"+++"GIS"+"Remote+Sensing"

Thanks for sharing. It seems like a good opportunity, especially for young and ambitious researchers.

I recommend hourly values from ERA5-Land

Sheharyar Ahmad The following research articles may be helpful.

I can also contribute in the ML applications. Please let me know if you need further assistance.

It sounds like you're looking for a way to export daily PROBA-V images using Google Earth Engine. Your current script filters and displays the images on the map, but does not export them. If you want to try this script that I attached, I hope it can help you.

var dataset = ee.ImageCollection('VITO/PROBAV/C1/S1_TOC_100M')

.filter(ee.Filter.date('2018-03-01', '2018-04-01'));

var falseColor = dataset.select(['RED', 'NIR', 'BLUE']);

var falseColorVis = { min: 20.0,

max: 2000.0,};

Map.setCenter(17.93, 7.71, 2);

Map.addLayer(falseColor, falseColorVis, 'False Color');

// Define an area to export (change to your desired area)

var region = ee.Geometry.Rectangle([0, 0, 10, 10]);

// Export each image in the collection

dataset.toList(dataset.size()).map(function(image) {

var date = ee.Image(image).date().format('yyyy-MM-dd');

Export.image.toDrive({

image: ee.Image(image),

description: 'PROBAV_' + date,

scale: 100,

region: region,

fileFormat: 'GeoTIFF',

maxPixels: 1e13

});

});

This script will export each image in the collection to your Google Drive in GeoTIFF format. You will need to replace

var region = ee.Geometry.Rectangle([0, 0, 10, 10]); with the coordinates of the region you want to export. Please note that the export may take some time, depending on the size of the images and the number of images in the collection. Also, this script will attempt to export all images in the collection, which could exceed your Google Drive storage quota if the collection is large. Make sure you have enough space on your Google Drive before running this script. Lastly, this script does not check if the images have been exported correctly. I would recommend that you manually check some of the exported images to make sure they exported correctly. Nikolaos Tziokas

Hope this script can help you.

Javid Hojabri

The correct value to use in the brightness temperature calculation is -273.15. This is because the Kelvin scale is based on absolute zero, which is defined as -273.15 degrees Celsius. Therefore, all temperatures in the Kelvin scale must be converted to Celsius by subtracting 273.15.

The reason why some articles use 272.15 is because they are using the Celsius scale instead of the Kelvin scale. The Celsius scale is based on the freezing point of water, which is 0 degrees Celsius. Therefore, all temperatures in the Celsius scale must be converted to Kelvin by adding 273.15.

However, it is important to note that the brightness temperature calculation is typically used in remote sensing, where the temperatures are measured in Kelvin. Therefore, it is important to use the correct value of -273.15 when converting brightness temperatures to Celsius.

Hello Himanshu Tiwari!

Actually, Remote sensing and GIS have numerous applications in soil management, which can help in addressing problem soils. Some of the applications are:

Soil mapping: Remote sensing and GIS can be used to map soil types, soil properties, and soil degradation. This information can then be used to develop soil management plans, including soil conservation and soil improvement strategies.

Soil moisture monitoring: Remote sensing can be used to monitor soil moisture content, which is an important factor in crop growth and irrigation management. This information can be used to optimize irrigation schedules and reduce water use.

Soil erosion monitoring: Remote sensing and GIS can be used to monitor soil erosion rates and identify areas of high erosion risk. This information can be used to develop erosion control strategies, such as planting cover crops or installing erosion control structures.

Soil nutrient management: Remote sensing can be used to monitor plant nutrient uptake and identify areas of nutrient deficiency or excess. This information can be used to develop fertilizer management plans and optimize crop yields.

Soil salinity mapping: Remote sensing and GIS can be used to map soil salinity levels, which can help in identifying areas of high salinity risk and developing salinity management strategies.

In fact, the potential benefits of using remote sensing and GIS in addressing problem soils are many. These technologies can provide accurate and timely information on soil properties, moisture content, erosion rates, nutrient levels, and salinity levels, which can help in developing effective soil management plans. By optimizing irrigation schedules, reducing water use, controlling erosion, managing nutrients, and reducing salinity levels, these technologies can help in improving soil health and productivity. Additionally, remote sensing and GIS can help in identifying areas of high risk for soil degradation, which can help in preventing further damage to the soil. Overall, the use of remote sensing and GIS in soil management has the potential to increase crop yields, reduce environmental damage, and improve food security.

Did you Sotirios Soulantikas find an solution for this problem?

Jihane Guenoun

while satellite remote-sensing techniques can provide valuable insights into crop water use and water availability, there are several limitations to their accuracy and reliability. These limitations must be carefully considered when using satellite-derived ETa estimates for monitoring and managing crop water use in different locations and for different crops.

There are several limitations to estimating actual crop evapotranspiration (ETa) using satellite remote-sensing techniques such as the Simplified Surface Energy Balance (SSEB) approach and Landsat Collection 2 (C2) Provisional ETa Science Products.

Spatial resolution: The spatial resolution of satellite imagery may not be fine enough to capture small-scale variations in ETa, particularly in heterogeneous landscapes with multiple crop types, varying topography, and soil characteristics.

Atmospheric interference: Atmospheric conditions such as cloud cover, haze, and aerosols can interfere with satellite measurements of ETa, particularly in regions with high levels of atmospheric pollution.

Sensor limitations: The accuracy of ETa estimates can be affected by sensor limitations, such as saturation of sensor values, band-to-band misregistration, and sensor noise.

Surface characteristics: The accuracy of ETa estimates can also be affected by surface characteristics, such as the presence of vegetation canopies, soil moisture, and surface temperature variations.

Crop variability: Crop variability, including differences in planting dates, crop management practices, and genetic traits, can result in variations in crop growth and water use that are difficult to capture using satellite remote-sensing techniques.

Calibration and validation: Accurate calibration and validation of satellite-derived ETa estimates is critical to ensure the accuracy and reliability of the data. This requires ground-based measurements of ETa, which can be difficult and expensive to obtain, particularly in remote or inaccessible areas.

Cost and accessibility: The cost of acquiring, processing, and analyzing satellite imagery can be prohibitive, particularly for small-scale farmers or resource-limited agribusinesses. Additionally, satellite imagery may not be readily accessible in some regions, particularly in areas with limited internet connectivity or data infrastructure.