Earth Observation - Science topic

Looking forward to the launch of this journal. 👍👍👍

Good day Ljubomir,

I do appreciate vey much your inputs... I will still uploading some analysis about this relevant issue because of three main reasons and standpoints, say:

(1) I do think, and I am trying to raise the alert that the 2030 SGS agenda is, already, compromised. That "failure" is a risky one as the global breakdowns (Climate and Earth's ecology) keep a speeding-up trends while getting more overlapping and interconnected (non-linear feedback among them).

(2) Global community (scientists and no-scientific people: lawyers, politicians, political scientists, government members, economists, and journalists) speak a lot about "Sustainable Development" (SD) in the millions of already published papers, speeches, policy papers, white papers reports , and articles (press) an it seems they can not find any difference between the "weak" SD version form the hard one. The "weak" one was adopted in the 1990s becoming the ultimate paradigm without carrying out ant strong analytical analysis of what would mean to adopt such a "weak" notion. I also think that many scientists that are part of this community (Research Gate) still can not make a strong exercise (by mathematical means only not just simple words) to make the case what they refer to when speaking about "Sustainable Development". In fact, that concept got meaningless several years ago. I urge scientists to make a case on this and explain in their papers what version are using and how their conclusions should be taking into account (validate them or not). In other words, we have to provide a range of validity to our conclusions depending if we use the "weak" or the "hard" version.

(3) I attend to several webinars and read a lot of papers on the 2023 SDGs agenda... For many , many people the methodology does not seem to care so much to them... Several reports that have been published do not provide any clue of what methodology they use and what are the range of validity of those conclusions that were obtained from "measuring the rate of progress" in any particular goal. It is being obvious to me that we (humans) will have to prioritize some of the seventeen (17) goals... This is my suggestion: No. 4, No. 6, No. 12, No. 13, No. 14 and No. 15.

I am willing to participate in hard-thinking workshops, publish joint analytical (with policy-making implications) papers and/or developing sound (also with policy-implications) research projects dealing with the urgent need to adopt the "hard" approach to SD and put humanity in the equation and the size of the human population as a key variable inside that reformulated scheme...

You can fin me here for further and farther discussions and exchanges of ideas, while building prospective joint operational cooperation opportunities.

Regards,

Hernán L. Villagran

Thank you IIan for your answer and your paper.

I do hope we can keep talking.

Have a nice weekend,

Hernan L.

Dear Napoleon Eduardo Cornejo, what about african institutes that are interested in the project,

Regards,

Fils César

Hello, in an atomic clock, frequency drift is generally a nonlinear variation. Atomic clocks are designed to minimize the effects of environmental factors and oscillator aging, but some drift remains inevitable. The drift can be caused by several factors, such as temperature variations, magnetic field fluctuations, and changes in the oscillator's properties over time. These factors can interact in complex ways, leading to a nonlinear drift pattern.

The extent and nature of the drift can vary depending on the specific atomic clock design and the quality of the components used. For example, some atomic clocks use temperature compensation techniques or magnetic shielding to minimize the impact of environmental factors, leading to a smaller drift. Moreover, higher-quality oscillators may exhibit less drift due to aging. However, even with these measures in place, atomic clocks still exhibit some level of frequency drift that is typically nonlinear.

It is important to note that atomic clock manufacturers perform extensive testing and calibration to determine the drift characteristics of their clocks. By understanding the nature of the drift, users can apply correction factors to improve the accuracy of their timekeeping systems.

To further elaborate on the calculation of frequency drift, let's consider the example of a typical cesium atomic clock. In a cesium atomic clock, the frequency of the clock is determined by the resonance frequency of cesium atoms when they transition between two energy levels. This frequency is extremely stable and provides a highly accurate reference for timekeeping.

However, even in a highly stable atomic clock like this, there can still be frequency drift due to various environmental factors. Let's assume that the clock has a frequency stability of 1 part in 10^14 over one day, meaning that the clock's frequency will drift by no more than 1 part in 10^14 over a 24-hour period.

If we express this drift as a frequency value, we can calculate it as follows: Frequency drift = 1 part in 10^14 x clock frequency Assuming the clock frequency is 10^10 Hz, the frequency drift will be: Frequency drift = 1 part in 10^14 x 10^10 Hz = 0.1 Hz per day

This means that over a period of one day, the clock's frequency will drift by no more than 0.1 Hz. While this might seem like a small amount, it can still impact the clock's accuracy over long periods, which is why atomic clocks employ sophisticated algorithms to correct for drift and maintain high accuracy over time.

Good evening,

Clear skies to observe,

you can check Ocean Data View websites and Copernicus marine resources (https://resources.marine.copernicus.eu/products).

Good for sharing, this was an interesting program.

I would also prefer u use PEPS(CNES) to download the sentinel 2 images then u use Maja corrections for all the images u want. Am also working on time series from 1984 till now combining Landsat 5 TM and landsat 8 Oli togeda. So from 2015 till now I used Sentinel images from PEPs to validate some of the OLI images from 2015 to 2020 which I found Maja corrections to more working good.

Hi

Since you don't have any sample points for calculating Vulnerability for Coastal areas either you can use the MCDM methods or AHP/Fuzzy AHP methods.

Just shine infra-red light on vessel and temp will rise faster the higher the CO2 conc. This is basically the mechanism for warming in the atmosphere and is so well understood that one way we measure atmospheric CO2 concs is using absorption of infra-red light

Anas Sharafeldin Mohamed Osman GEOCOL and GEOGRID for gross-error detection.

Good luck

Hola Napoleón, actualmente la UCA desarrolla una investigación interdisciplinaria con biólogos, expertos SIG en teledetección y expertos en ciencias de la computación, en un proyecto de monitoreo del crecimiento de algas y toxinas en el embalse del Cerrón Grande, puedes contactar con Metzi Aguilar que coordina la parte de teledetección maguilar@uca.edu.sv y Luis Cierra biólogo jsierra@uca.edu.sv , creo que pueden tener buena sinergia!

Saludos

Forecasting climatic, geological, natural and other processes that may occur in the next several dozen years is burdened with a very large scope of research error. Despite the progress made in the field of predictive analytics, the impact of the development of civilization on the climate and the biosphere of the planet Earth is still large and growing. Therefore, forecasting the development of climatic, geological and natural processes that may appear in the next several hundred years may border on the proverbial "fortune-telling on tea grounds".

Best regards,

Dariusz Prokopowicz

Dear Andres Calabia, at TU Delft we created a website with neutral mass density from CHAMP, GRACE, GOCE (slightly out of the range you mentioned), and Swarm missions: http://thermosphere.tudelft.nl/

These densities are obtained with satellite high fidelity geometries and updated assumptions on Gas-Surface Interactions.

Maybe it's not entirely what you need, but I hope can be useful somehow!

When they first sent up GPS satellites, they programmed them with what they believed to be the correct relativistic changes that would be needed which would affect GPS location determinations. They also installed a programmable system within these satellites that can be reprogrammed from the ground, and they could upload any changes when the satellite was overhead.

What they found was that the relativity program the system was given was not accurate enough for GPS system calculations. They tried to correct these equations and related program for a period of time but finally gave up. Instead they uploaded a programmed algorithm that they believed could make the necessary corrections based upon information from the ground coming from one or more additional GPS satellites. The new system worked. So now all relativity corrections of GPS satellites are now based upon a self-correcting algorithm using new information from the ground and other GPS satellites on an ongoing basis.

Yes, there is, available through NASA EarthExplorer portal:

See https://www.usgs.gov/centers/eros/science/usgs-eros-archive-declassified-data-declassified-satellite-imagery-3?qt-science_center_objects=0#qt-science_center_objects

And more is not available online, you can request scans of those.

That being said, these are not trivial to deal with - it requires an understanding of the entire technical context of the systems.

It's useful to familiarize your self with the histories and technical details of the platforms ( https://www.nro.gov/History-and-Studies/Center-for-the-Study-of-National-Reconnaissance/The-GAMBIT-and-HEXAGON-Programs/ ).

Some are outstanding ( 2 to 4 foot resolution, caveat: the metadata may be completely misleading in the catalog).

These are military reconnaissance imagery which were scanned from photographic film, returned from orbit, and those orbits were not the nice ones of modern remote sensing platforms ( some are taken at times of day to accentuate features of interest, rather than repeatability. The film / mirror mechanism can make georeferencing these 'interesting', some have a waviness from side to side along the track, and the scene parameters may be variable from one end of the image to the other depending on the orbit. I usually start with an oblique Mercator which has an axis perpendicular to the orbital plane, and go from there.

Since Norilsk was foundational to supplying the Soviet military industrial complex, there seems to be a lot imagery.

It was amazing what they did with the technology at the time.

Wow, thank you. I'm looking for understanding their high-frequency repeated interferometry for ground deformation monitoring. Will data be open or should they be bought?

There are standard methods for testing soils to consider. Usually soil samples are taken and put into a container of water so the soil is saturated from the bottom, about 24 hours. Unless there is a water table to saturate soils in the field, ability in reaching saturation may vary with the conditions. I remember we had a dry period and wildfire, then a substantial rain, about 3-5 inches. Water was pounded on the surface the next day, but the soil scientist found the soil totally dry about 12-18 inches below the surface. I also learned that an upper clay layer needs to be saturated before the lower sandy layer can effectively gain water and this is related to the tension with which clay holds water as compared to sands. Some soil layers have difficulty becoming saturated, such as well developed forest soils seldom exhibit runoff due to high infiltration and macropores from roots, etc. other soils as hydric soils are saturated frequent enough to exhibit hydric soil indicators due to frequency of high water table. Sampling the soil depth or horizon is probably the most reliable using standard methods to saturate and measure.

I would not use just the high reading in 6 month period, unless at least I reviewed nearby stream gauging stations and the rainfall during the 6 month produced at least a bankfull streamflow event, which occurs generally about every year, and the stream channel is at a level where flooding begins for stable channels that have not aggraded or degraded. I would want to be pretty sure there was a reasonable likelihood of saturation based on evidence like the streams were near or at flooding. The other option might be to set up so the area with the data sensors can be irrigated for long enough so the detectors stabilize after reaching their approximate maximum. Saturating from the surface can apparently leave some air spaces unfilled, but unless the area periodically has a high water table, the air spaces may not be filled easily to saturation by rainfall anyway.

Please go to this link:

https://www.timetech.de/products/new-trends-in-two-way-time-and-frequency-transfer-viasatellite/

Simply because Earth is the only planet inhabited by human beings.

Dear Maged,

thanks for this answer. We will try discuss questions of EO for food security on next GEO meeting in Canberra.

In the Lagrangian frame of reference, the residence time of a given quantity of chemical species is determined by the location of the emission, the flow path, the lateral mixing, the vertical diffusion, the deposition (wet and dry), chemical reactions and the transfer between the gas phase and particles. The solution of the mass conservation equation for a given trajectory leads directly to the estimation of a residence time.

Considering the fact that all the parameters of the considered system exhibit a significant variability, we should look at the problem of the residence time more in the sense of the distribution of the residence times evaluated for the ensemble of the trajectories. Calculations can be accomplished using a Lagrangian model that includes all the relevant processes indicated in the first sentence

In the Eulerian frame of reference, we can use a method suggested by Professor Bolin and his coworkers the article: "Residence time of atmospheric pollutants as dependent on source characteristics, atmospheric diffusion processes and sink mechanisms"

When considering the modifications of the method suggested in this paper, it is advisable to use a three-dimensional transport model with a realistic Planetary Boundary Layer scheme, good schemes for dry deposition coupled to a cloud model. In my opinion, we are still trying to master three-dimensional transport models with the properties required to make realistic estimates of residence time.

The remaining question is: which method is better?

In my opinion, the most accurate and realistic methods are based on the ensemble of trajectories because of their ability to estimate the distribution of the residence times.

Dear Colleagues, In 2008 I have made an algorithm for prediction… With it was written the tides of Moon, Venus, and Mercury, described in my article: ‘Tényekkel igazolható a gravítáció valós oka’,… The algorithm was not published, for research and business purposes…: Venus tidal effect: ‘116 days 17 hours 45 minutes (the data is on a terrestrial scale) Mercury tidal effect: 175 Days, 23 Hours, 20 Minutes (Data on Earth Scale)’ The Vyecheslav prediction method is very good but seems he did not present all in his paper a very important phenomenon is not mentioned, perhaps similar reasons like in case of mine…

Regards, Laszlo, P.S. Today I was in Church… Exactly they read gospel was connected to the researchers who do promote such a person like Vjacheslav Nagorny Luke 10:25-37 English Standard Version (ESV) ‘The Parable of the Good Samaritan 25 And behold, a lawyer stood up to put him to the test, saying, “Teacher, what shall I do to inherit eternal life?” 26 He said to him, “What is written in the Law? How do you read it?” 27 And he answered, “You shall love the Lord your God with all your heart and with all your soul and with all your strength and with all your mind, and your neighbor as yourself.” 28 And he said to him, “You have answered correctly; do this, and you will live.” 29 But he, desiring to justify himself, said to Jesus, “And who is my neighbor?” 30 Jesus replied, “A man was going down from Jerusalem to Jericho, and he fell among robbers, who stripped him and beat him and departed, leaving him half dead. 31 Now by chance a priest was going down that road, and when he saw him he passed by on the other side. 32 So likewise a Levite, when he came to the place and saw him, passed by on the other side. 33 But a Samaritan, as he journeyed, came to where he was, and when he saw him, he had compassion. 34 He went to him and bound up his wounds, pouring on oil and wine. Then he set him on his own animal and brought him to an inn and took care of him. 35 And the next day he took out two denarii[a] and gave them to the innkeeper, saying, ‘Take care of him, and whatever more you spend, I will repay you when I come back.’ 36 Which of these three, do you think, proved to be a neighbor to the man who fell among the robbers?” 37 He said, “The one who showed him mercy.” And Jesus said to him, “You go, and do likewise.”’

Hi Mannan,

Are the qanat's track superficial or underground? if they are superficial, you can use thermal bands. If the qanat are underground, I think you can use Ground Penetrating Radar for this purpose.

Good Luck,

Reda

It really depends on the type of imaging. If, as mentioned before, there is spectrally resolved information, one can make use of the fact that the contribution functions of some channels peak at different altitudes and apply some kind of minimization technique (e.g. see our algorithm for IR sounders https://bit.ly/2VAG2NZ).

However, if the image was obtained only at one wavelength or the contribution functions are too broad or they do not "scan" the whole altitude range then it's more difficult. One can train neural network using this kind of images and a combination of active and passive sounders (e.g. AIRS + CALIOP) serving as a reference, but it's easier to say than to make :)

The greenhouse effect has long been recognized by research centers and in the past, from the age of 60. Since the 1990s, many international researchers have come together under the auspices of the United Nations to work on this rapid and global climate change and its evolution. Regarding the cycle I totally agree with my colleague Kenneth M Towe

Dear Abd-Elrahim , I suggest reading this book:

https://www.springer.com/gp/book/9783540853282.

Best regards

Dear Abd-Elrahim Ruby,

I am also new in GMT, but I have found a good manual "Getting Sterted with GMT: An Introduction to Seismologists" by Matthew R. Agius, you can request a full-text, hope it will help you.

Best regards,

Katerina

In Mathcad writing

Elevation ==

Sum, for n = 1 to 2190,

for m = 1 to n, of

Coefficient C.m Cosine Lamda + Coefficient S.m Sine Lamda

is so easy, since the proper notation is available, but in Mathcad, we can *write*

d^n / d cos^n (theta) inline but it won't expand, even with Live Symbolics *on*.

So what is needed is to write it out of line, with Live Symbolics on, Evaluate Symbolically, and Paste the result. However, even at order 5, the result may be what Mathcad calls Huge. Too big. So we shall see.

You may find the link below:

Hi,

what you mean by synonymous? As you pointet out they have a different spatial resolution. Are you asking what is the source of original data? It's the famous SRTM mission.

Best.

If you are looking for a freeware for Sentinel-2 time series data managements, I would suggest using SNAP, the Sentinel toolbox and there are some tutorials about that.

Joan, just wanted to share an update. All the Landsat 8 night time scenes that were acquired in the past are back online available for download on the Earthexplorer database. Out of the 17607 night time scenes acquired with Landsat 8 globally, there are around 5 scenes covering Barcelona for the time period July-Sept. 2015.

Cheers!

I write this scrip long ago to read Modis Aqua files in matlab, i hope someone else could help.

Dear, Dr. Basem

Many thanks for helping me and also for this answer

Can you help me to get more details about these three missions in a gravity field?

Regards

Abdel Rahim

Recollection

Imagine the 3 gravity fields:

(1) the gravity field of the Earth, described in the rotating Earth-fixed coordinate

system, under the assumption that Moon and Sun do not exist ==> tide free gravity field model

(2) the gravity field of the Earth, described in the rotating Earth-fixed coordinate

system, plus the gravity fields of the Moon and the Sun averaged over a long time (which is called the permanent tidal effect), plus the effect of Earth's deformation, caused by Moon and Sun, on the gravity field (also averaged over time)   ==> mean tide gravity field model

(3) the gravity field of the Earth, described in the rotating Earth-fixed coordinate

system, without the gravity fields of the Moon and the Sun, but, with the indirect effect of Earth's deformation ==> zero tide gravity field model

The differences of these three gravity fields can be described (as the gravity

field itself) in terms of spherical harmonics. Strictly, these spherical harmonic series go up to infinity.

But, because the permanent (i.e. averaged over time) tidal effects change the gravity field only very globally, it turned out that it is sufficient to change only the coefficient C20, which describes the flattening of the equipotential surfaces of the gravity field.

This answer I received from Dr. Franz Barthelmes (Section 1.2: Global Geomonitoring and Gravity Field, Department 1: Geodesy and e-mail:    bar@gfz-potsdam.de)

There is no specific number save at a specific time, as the magnetic pole is constantly moving relative to the rotational pole, as discussed below, and any number that is correct now will be incorrect within a year or two.

A century or so ago the north magnetic pole was twenty degrees from the rotational pole, in northern Canada; but it has been moving more or less steadily northward and is currently less than 4 degrees from the North Pole. This change in the position of the magnetic pole causes the direction that compasses point to change by a significant amount in as little as a few years (the error is called the magnetic declination). Also, the south magnetic pole is normally not directly opposite the north magnetic pole, so its angle with the South Pole of the rotational axis is not the same as the angle between the north rotational and magnetic poles.

Over very long periods of time the magnetic pole moves more or less randomly relative to the rotational pole, sometimes being (as now) very close to it, and at other times being (as a century or so ago) twenty or more degrees from the pole, though on the "average" it seems to be a good indicator of the position of the continents relative to each other and the Pole, so the average position of the magnetic pole in any given million-year period appears to be nearer the rotational pole than far from it. The speed at which the magnetic pole's position changes ranges from nearly zero (over a century ago) to more than 30 miles per year (over most of the last decade or two).

Dear Ricardo

Thanks for clarification

I found another solution where:the function "polyxpoly" used for Intersection points for lines or polygon edges

Many Thank

Regards

Abdel Rahim

Dear Sumanta, you are asking a complex question for which there might be no exact answer today. While in economic literature the statement about temperature growth in the last 100 years by only anthropogenic influence dominates, physicists are still in doubt; see for example https://www.newscientist.com/article/dn11639-climate-myths-the-cooling-after-1940-shows-co2-does-not-cause-warming/ . There exists also a hypothesis about global cooling, but on much larger time scale; see http://isthereglobalcooling.com/ . The graph about global temperature and CO2 dynamics is the last 450,000 years is attached (from this source). If this graph is correct, we have an interesting observation: warming went faster (about 10,000 years) than cooling (100,000 years). It is unclear whether triggering is caused by some shocks or some mechanism of dynamic equilibrium on the Earth. But is is also clear that we observe anthropogenic contribution to this process for the 1st time.

The problem however is that we cannot wait too long without making actions to combat global warming. Less carbon emissions would indeed work towards speed reduction of this process, but it might happen that other greenhouse gases (like methane) will continue working in its favor while increase of volcanic activity (observed in the last 10 years) will work towards reduction.

As for the rise of ocean level, the effect is still small today, and is caused not so much by ice melting but more by change of water density with temperature and salinity. However, melting of all Antarctic ice will cause a catastrophic rise of the ocean level by 60 meters; see https://nsidc.org/cryosphere/quickfacts/icesheets.html . Melting of Greenland will have much lower effect (6 meters), but here we observe catastrophic melting in the last years.

Dear Abdel Rahim, methods of estimating the geoid geopotential value W0, in addition to the previous and recent estimates have been described in some details in our paper "A conventional value for the geoid reference potential W0, doi:10.1007/s00190-016-0913-x" . This paper would satisfy your question. Nadim

what do you want now?

Dear Rhorom,

Please check the following link and pdf attachment for said query

Good Luck

Please have a look at the discussion following Mustak Ali's question "Can we automatically classify only the rivers from satellite images using any programming or different methods?", last year on Research Gate.

Open water you can map with both optical and radar remote sensing, but flooded forest you see much better on radar imagery. In optical imagery you may  see some texture differences between the flooded and the non-flooded forest, which is more related to forest type and its location in the floodplain, but with radar you can map the actual extent of water below the canopy at that particular time. Have a look of at the use of SAR imagery over the Amazon forest to find examples. You can add "varzea" or "flooded forest" or "floodplain" to limit the results.

Success,

Wietske

In general, quantaties Y that vary in time t, are called variables. They are usually dependent variables Y(t) = cX1 + ...  A parameter, here e.g. "c" is a constant. Both, variables and constants can be the result of measurements. Parameters can be also empirical or physically based.

In climate modeling, you often have a "parameter table" which defines typically used constants used in simulations. These parameters are sometimes changed for different experiments but are constant during the given simulation. In some cases however, you may use also a variable/timeseries e.g. for the solar constant or the orbital forcing etc to simulate changes in these forcings over time. In that case, although they vary in time, they are not dependent variables (at least not in the context of Earth, so external parameters). So I would still tend to say that these are parameters of the climate system which vary in time. I hope that helps.

Your question is difficult because there is no accepted definition of what a "clear-sky day" actually is in terms of sunshine or global irradiation. There is even possible confusion between the terms "clear sky" and "cloudless sky". The latter is precise (0% cloud fraction), whereas the former is not because some authors consider cloudless but hazy conditions as "not clear". Then of course you need to define the threshold of what hazy conditions are in practice _for you_, which introduces variance. This question of haze is important here if you talk about conditions over UAE, for instance, because the aerosol optical depth (AOD) may be very high at times, e.g. during dust storms. This greatly complicates the issue.

In any case, there are two possible avenues: Use either (1) GHI or (2) sunshine data.

1. You can calculate GHIc (GHI during an ideal cloudless day) using a clear-sky radiation model. Selecting the best one for your application is not trivial, since you have to trade off accuracy vs. ease of use. This recent study should give you the background info you need about this: http://www.sciencedirect.com/science/article/pii/S0038092X11004221

Usually, the best radiation models require many inputs that describe the atmospheric conditions (such as AOD) for each specific day. One rarely has such information with the required accuracy and spatio-temporal resolution, which forces us to rely on interpolation, extrapolation, approximation, etc., resulting in errors in the calculated GHIc for any specific day. Therefore, you need to accept some uncertainty limits. For example, you can decide that a clear-sky day has a daily global irradiation of at least 90% (or 95%, etc.) of the ideal GHIc you calculated for that day. This margin of error must be evluated empirically for your location/model combination, and will account for actual hazy situations, etc., that are not reflected in your input assumptions, and for any model shortcoming.

2. Similarly, you can calculate the ideal daylength and compare it to the measured sunshine duration for each day. But here again, this idealization can be a source of error. The WMO convention is to consider a threshold of 120 W/m2 to define daylength as sensed by Campbell-Stokes sunshine recorders. This is ideal, since this value can change drastically over time or location for a variety of reasons. See discussion and method of effective daylength calculation for various theresholds in http://www.sciencedirect.com/science/article/pii/0038092X9390075Y

In practice, you can define a clear day as having an observed sunshine duration equal to the calculated effective daylength within some "reasonable" margin of error. The latter is to take the effects of actual instrumental threshold variance and haze into consideration.

Dear Muhammad Munir;

The answers of your specific questions as:

1. These coefficients are dependent on the site of where they calculated, for example: if you calculate them in Algeria, and apply them in UAE, the models with these coefficients will give you lower performances than in Algeria.

2. Not always, just in very specific cases.

3.This depend on the correlation used, in some cases it is the first, and in others it is the second. Therefore you need to check the equation.

I hope this can help

Give a lool to the Copernicus Marine Environmental Monitoring Service (CMEMS), formerly MyOcean.

There are various bio-physical parameters - including chlorophyll -  obtained both from Earth Observation data and models which are provided at different resolutions. In some cases the data series start from earlier than 2000.

Normally, you should have Ground Truth Data. But if you don't, you should try to find known points from other sources (e.g. Google Earth) or better from G.P.S. points taken by you. You can take a look at:

Congalton, R. G. and Green, K. 2009. Assessing the accuracy of remotely sensed data: Principles and Practices. 2nd Edition. CRC Press, Taylor&Francis Group, Boca Raton.

Hi, all

 i have excel program sheet based on Gravity definitions & anomaly computations (NGA document)

Download  file :- Gravity definitions & anomaly computations (NGA document)

and the latest version of World Geodetic System (WGS) 1984

see attached for excel sheet

Please testing the excel program sheet... when there are errors Contact with me

 Regards

Abdel Rahim

Dear Eng, Ahmed

Thanks for adding your answer

this paper Failure during download

It can be sent to email (abd_roby87@yahoo.com)

Thanks

Abdel Rahim

Dear Fatima,

Further to your question and your subsequent email, and to summarize the information collected so far, here is some additional input for you to consider:

1. Classification:

1.1 Earth Observation (EO) satellites can be classified according to their orbits, as hinted by V. K. Srivastava. In this case, the relevant criteria are

- orbit altitude (and eccentricity, perigee and apogee, if the orbit is not nearly circular), and

- orbit inclination.

Two main categories emerge in this case: Polar-Orbiting and Geostationary satellites. Read this page https://en.wikipedia.org/wiki/Orbit for a general introduction, and then search the Internet with those specific terms to find links to further information.

1.2 Satellite instruments are typically classified in two major categories:

- passive instruments measure radiation quantities emitted by other sources

- active instruments emit their own radiation, and measure how much of that emitted radiation returns to the instrument, and when.

1.3 Satellite instruments can also be classified according to the remote sensing technology used to retrieve information on the planet, and in particular the range of electromagnetic radiation in which they operate. As already pointed out by Jianjiang Wang, you can distinguish between

- instruments operating in the visible and near-infrared spectral range, which take advantage of the Sun as a source of light and therefore measure

- instruments operating in the thermal spectral range, typically to measure the temperature of targets of interest, or

- instruments operating in the microwave spectral domain, such as synthetic aperture radars.

Hyperspectral instruments feature more spectral bands than typical multispectral instruments, but otherwise operate similarly.

2. As far as construction phases are concerned, I suggest you study the materials prepared by NASA's Jet Propulsion Laboratory (JPL) and available here:

This web site is specifically addressing the design of planetary or outer space missions, but most of the content is also applicable to EO missions.

3. Satellite and instrument dimensions vary widely, from 10 cm and 1 kg for a CubeSat, to 120 m and 450 tonnes for the International Space Station. See https://en.wikipedia.org/wiki/CubeSat and http://www.nasa.gov/mission_pages/station/main/onthestation/facts_and_figures.html for more details on these extremes.

Best regards, Michel.

Dear Miguel,

The OCN should soon come now.

The L2 processing is slightly more complex to organise as it requires exteranl information (ECMWD WIND, WW3, Ice information). It took us a bit of time to have these properly used at PDGS level.

The WV mode acquistion is being currently made global. We will soon have enough gobal data to fine tune the MTF for swell retrieval.

I would say that first OCN should available this summer

I have no idea what these words stand for.  They sound like names of some computer programs.  Possibly the people above are more knowledgeable, so you should listen to them.

Cheers,

PV

You should check the International Earth Rotation Service web page:

The yearly values (since 1623) are available here:

If you want daily solutions (since 1962) for UT1-TAI and delta LOD (together with the EOP), go to:

I hope this helps.

Cheers,

Demián

Dear Mohamed,

There is a vast literature on your topic of interest. You may want to start by browsing journals that specialize on arid and semi-arid regions, such as

- Journal of Arid Environments [Elsevier]

(http://www.journals.elsevier.com/journal-of-arid-environments/)

- Land Degradation & Development [Wiley]

(http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291099-145X)

In particular, you may be interested in the following special issues:

* Land Degradation & Development [Wiley]

Special Issue on Understanding Dryland Degradation Trends

March/April 2011, Volume 22, Issue 2, Pages 145–312.

(http://onlinelibrary.wiley.com/doi/10.1002/ldr.v22.2/issuetoc)

* Global and Planetary Change [Elsevier]

Special Issue on Climate Change and Desertification

December 2008, Volume 64, Issue 3-4, Pages 105-252.

(http://www.sciencedirect.com/science/journal/09218181/64)

To characterize vegetation dynamics, you may want to use accurate, quantitative remote sensing products such as the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), which is much more reliable than traditional (and obsolete) vegetation indices. To get started on that, have a look at the following web site:

it provides plenty of documentation and papers, as well as data products.

I hope this will help you. Cheers, Michel.

the second  program are Harmonic_Synth_v02 (Version 05/01/2006)

                       Simon A. Holmes and Nikolaos K. Pavlis

                                                       Harmonic_Synth

HARMONIC_SYNTH is a FORTRAN 77 program for very-high-degree harmonic synthesis.

HARMONIC_SYNTH combines and expands the capabilities of three related programs that existed in Richard H. Rapp's software library at the Department of Geodetic Science and Surveying of The Ohio State University. These three programs and their functions are:

 

(a) F477: harmonic synthesis of point values over arbitrarily scattered locations (Rapp, 1982).

(b) F388: harmonic synthesis over regular grids of regional or global extent, using Chris Rizos' (1979) algorithms.

(c) F431: harmonic synthesis of point or area-mean values over grids of global extent, using Oscar Colombo's (1981) algorithms.

For more information, please visit the following website

But I need to help run this program .... who wants to help, please send me a message

Atmospheric interference creates artifacts in observational data. Ground targets useful for minimizing the impact of atmosphere or to minimize the error extent.

Dear friend Reda

Where the answer to question?

Dear N. Dkhar,

Whenever two or more individuals or organizations decide to tackle the same task, it is actually very likely that their results will exhibit mismatches and discrepancies. Difference in methodology, instrumentation, recording practices, quality control, human errors, archiving, maintenance, documentation, and many other factors will inevitably lead to different outcomes or interpretations. This is even more the case with potentially "controversial data", e.g., involving issues linked to national or commercial interests.

Remember also that different inventories may have been assembled for different purposes, with different accuracy requirements, and--most importantly--with possibly quite different levels of financial support. In any case, each inventory or data source should be independently evaluated with regards to the quality and performance of the measurement protocols, the methodologies followed, the practices, etc. That should give you a first indication of their intrinsic value.

If you have access to multiple independent sources, you may trust one that is closer to the average, but even that type of selection is potentially subject to biases: it all depends on whether they are truly independent, and there is always the possibility that the "outlier" stands out because it is actually better designed... So the next step is to conduct direct comparisons, as well as benchmarks against well-know cases that have been amply documented in situ.

By the way, you may also want to look at the world glacier monitoring service, hosted in Switzerland:

Last but not least, noticing differences between inventories is important to remain aware of the intrinsic limitations of each, but you need to be clear also about your own objectives, your expected outcomes, the acceptable degree of uncertainty, etc. Perhaps some or most of the differences you witness are not critical for your application?

I hope this helps. Cheers, Michel.

Another paper I think that could be of some relevance is

"A spatially explicit methodology to quantify soil nutrient balances and their uncertainties at the national level" by J. P. Lesschen et al

Nutr Cycl Agroecosyst (2007) 78:111–131

DOI 10.1007/s10705-006-9078-y

hi Xiaolu,

the spinup time depends whether you start from a coldstart or from a realistic initial conditions

from a cold start you need about 2000 year of accelerated spinup plus 2000 without acceleration in order to fill the carbon and nitrogen pools

in both cases you need to run a transient simulation using the observed forcings till 1998

the restart produced at the end of 1997 will be the initial condition for your simulation

Matteo