Evapotranspiration - Science topic

Thank you! Super! I understand :)

Hi,

One of the factors that affects water need is underground water pipeline leakage.

please take a look on my paper entitled "A Novel Technique for Detecting Underground Water Pipeline Leakage Using the Internet of Things" published in Journal of Universal Computer Science.

It is published online: https://lib.jucs.org/article/96377/list/18/

Hope this can help.

Regards,

Ahmad Abusukhon

This article might be helpful for you:

Morris La Crois Plant characteristics in your model includes mostly statistical or pre-measured data

1) Evaporation + transpiration= evapotranspiration rate.

2) Amount of water required by the crop for healthy growing circumstances (statistical data).

3) Stage of plant growth

4) Plant density

But, real water useage will depend more on plant leaf area, architecture of plants, physiological state of plants (stressed/healthy), and some other crop type/cultivar specific traits.

I share your deep interest in this field, as it holds the key to understanding and mitigating the widespread impact of corruption on our planet. The devastating consequences of corruption ripple through ecosystems, affecting not only our crops and plant life but also our animal life. By delving into the relationship between evapotranspiration fraction (ETf) and stress coefficient (Ks) in the context of water salinity, we gain valuable insights into how these corruption-driven disruptions manifest in the natural world. This knowledge equips us with the tools to safeguard our environment and work towards sustainable solutions that counteract the negative effects of corruption on our fragile ecosystems. Join the Dailyplanet.Club

En plantas herbáceas C4 el déficit hídrico y aplicaciones de fertilizantes químicos de mala calidad, falta de precipitaciones, entre otros pueden causar retrasos en los ciclos vegetativos, así como perdida de turgencia celular, lo cual deriva en problemas fisiológicos graves que merman la producción y ponen a los cultivos vulnerables a plagas y enfermedades

You can view this website

It contains the required dehydration equation

pl visit site https://dsp.imdpune.gov.in/

This is for supply of meteorological data .

or for more details call to help desk +91 (20) 25572-254, 255

from (Monday to Friday 10 AM to 5 PM )

Dear Tagele,

Negative values of evapotranspiration are not physically meaningful and suggest that there may be a problem with your calculation or input data. There are several possible reasons why you may be getting negative values, including:

1: Incorrect input data: Check that you have entered the correct values for temperature, humidity, wind speed, solar radiation, and other inputs. Make sure that the units are consistent and appropriate for the FAO PM method.

2: Errors in the code: Check your MATLAB code to ensure that there are no errors or typos in the equations or formulas. Check that you have used the correct constants and parameters for the FAO PM method.

3: Physical limitations: Negative values of evapotranspiration are not physically meaningful, as it represents a loss of water from the system. It may be that the FAO PM method is not appropriate for the conditions at your study site, or that there are physical limitations to the system that are not captured by the model.

4: Data quality: Check the quality of your input data to ensure that it is accurate and representative of the conditions at your study site. If your data is incomplete or unreliable, it may lead to negative values of evapotranspiration.

It is also worth noting that the FAO PM method may not be appropriate for all conditions and locations, and there may be other models or methods that are better suited for your study site. You may want to consider consulting with an expert in the field or seeking additional guidance from the literature to ensure that your evapotranspiration calculations are accurate and meaningful.

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.

Given the density of water, 1000 kg/m^3, the evapotranspiration (ETA) in the units of kg/m2/sec with 3 hours intervals, can be expressed in mm/month as

ETA [mm/month] = ETA [kg/m2/sec] x 10800 [sec/3hr] x 8 [3hr/day] * 30 [days]

Evapotranspiration (ET) is a key component of the water cycle, and its measurement and analysis are important for understanding hydrological processes. Statistical methods can be used to analyze evapotranspiration at the site scale, such as fluxnet data. Fluxnet data is a network of micrometeorological towers that measure energy and water exchange between the land surface and atmosphere. This data can be used to calculate ET rates at different timescales, from hourly to seasonal. Other statistical methods such as regression analysis can also be used to examine relationships between ET and other environmental variables such as temperature, humidity, wind speed, and precipitation. Additionally, remote sensing techniques such as satellite imagery can be used to estimate ET over large areas.

Dear Barry Croke & Takuya Iwanaga thank you so much for your suggestions. The modelling results have eventually increased after using satellite derived evapotranspiration data. I have also recalculated the catchments area for the study catchments. The calibration results produced more than 0.8 NSE values. I appreciate your support and time.

Dear William F. Hansen , Thank you for your answer.

I think your explanation makes a lot of sense, and it is common for PET to be less than P. However, this has caused me confusion in applying this equation. I chose this particular equation to calculate the index of land sensitivity to wind erosion, and this index can help me to continue analyzing other ecological issues (e.g., exploring the community characteristics of wind erosion sensitive areas), i.e., I may not need quantitative erosion values. Since I am not an expert in soil erosion, I wanted to find a suitable equation to simplify the effect of climate on wind erosion. If other conditions are consistent, the land will be sensitive to wind erosion in drier times and insensitive to wind erosion in wetter times, which is what this equation is trying to convey. I am not sure if this equation is applicable to my study area, and am also looking for a better solution.

Quite a late reply but still, been working on the same and I found that future projection simulation can be done using climate change scenario datasets. user needs to download the GCM datasets (suitable model) and downscale them using either software or programming languages like (Python, R, or MATLAB) then these downscaled datasets are used in HEC HMS to run the simulation (continuous simulations). And for just return periods (event-based simulations) one needs to use the Hypothetical storm method for precipitation where the user uses IDF curves, the precipitation distribution method to calculate return periods (10, 20 ... 100 or more). This is however based on empirical formulas used on peak rainfalls.

I request the scientific community to correct me if I am wrong as I am still new to this.

This might be helpful for you

The standard ET measurement method is the so called FAO 56-PM method. But different studies indicated the preference of other empirical methods according to the specific climatic conditions they studied, For example, based on the study of Gao et al. (2017) the Priestley-Taylor performed better in Dry climate, Hargreaves performed better in semi dry climate and Makkink methods performed best in humid climates. So it depends. It is also important to note that now a days ET could be predicted easily by machine learning algorithms.

F. Gao, G. Feng, Y. Ouyang, H. Wang, D. Fisher, A. Adeli, J. Jenkins Evaluation of reference evapotranspiration methods in arid, semiarid, and humid regions

JAWRA J. Am. Water Resour. Assoc., 53 (2017), pp. 791-808,

plz see, the procedure. May be helpful.

Regards,

How to Measure Evapotranspiration | CivilMint.Com

We tested the added value of gridded evaporation products in hydrological model calibration and found that they have a good potential to improve the model performance if they are used adequately (in fact, we tested various calibration settings).

The following papers will tell you more about our work:

Yes,

https://www.mdpi.com › htm

Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Yes, you may find it in AquaCrop model.

Yes, it can be done Using Satellite Data and Eddy Covariance Stations.

For those following this question - the solution was:

1 joining time series data

2. concatenating the stationary data.

// JOIN - Combine/join image collections for training

var joinA = GW_anomaly2.select('GW_anomaly');

var joinB = CHIRPS_resampleD.select('precipitation');

var joinC = MODIS_resampleD.select('ET','PET');

var filter2 = ee.Filter.equals({

leftField: 'system:time_start',

rightField: 'system:time_start'

});

var simpleJoinB = ee.Join.inner();

var innerJoinB = ee.ImageCollection(simpleJoinB.apply(joinA, joinB, filter2));

var joinedB = innerJoinB.map(function(feature) {

return ee.Image.cat(feature.get('primary'),feature.get('secondary'));

});

var simpleJoinC = ee.Join.inner();

var innerJoinC = ee.ImageCollection(simpleJoinC.apply(joinedB, joinC, filter2));

var joinedC = innerJoinC.map(function(feature) {

return ee.Image.cat(feature.get('primary'),feature.get('secondary'));

});

// Concatenate (combine into single collection with multiple bands) the given long term mean (single band)

function add_topo(image){

var concatenate2 = ee.Image.cat([DEM_resampleD]);

return image.addBands(concatenate2);

}

//Map function over entire time-series collection

var combined_input = joinedC.map(add_topo);

Thank you @ Mohsen Hoshan sir

Hi All and Prof. Gao,

I came across this discussion. I think this is really an interesting topic and a good question. First of all, I'd like to say that your point "these data-driven approaches require substantial data" is correct. To answer this question, I think we need to understand the background of the emergence and widespread application of AI and "big data" techniques, and we should think about why we need machine learning or other "big data" techniques in hydrology/earth system science? What are the pitfalls of our current process-based models at present? Can machine learning methods fill these gaps or improve the prediction?

In recent decades, we have vast amounts of spatiotemporal data from in-situ observations, remote sensing, reanalysis data, and model outputs. In my opinion, data-driven methods can help us gain innovative knowledge from these data, and their greatest advantage is that they do not need to rely on parametric assumptions, and thus can dynamically capture the effects of non-stationary surface processes. For the advantages of deep learning in Earth system science, I recommend reading the review paper "Deep learning and process understanding for data-driven Earth system science" by Reichstein et al. I think this paper discusses it very well.

Frankly, there are some challenges with distributed hydrological models, especially when tuning parameters. In the parameter calibration process, why do different parameters get the same effect? When we use the historical data to set the parameters, will the value of the parameters change in future?

In the future, we hope to be able to see the coupling between these approaches so that we can express the real world more realistically. Hope this information is helpful to you. Good luck!

Hello,

Please find the attached paper. It discusses designing a new PET product based on ERA5-land product over the period 1981-2020 with hourly and daily time scales in 0.1 degree horizontal resolution. Hope it can be useful for you.

Best Regards

Samy

Dear Soufiane Taia

Smaranika Mahapatra and Timothy O Randhir have provided I believe the best options for you. You start doing it, once any issues pertain to uncertainties arise we can discuss it further here.

Hello

I suggest comparing models such as Hargreaves method--Blaney-Criddle-Linacre and various temperature-based methods with the FAO Penman-Monteith method, and finally selecting the best model and using this method for future research. For my study area, the Blaney-Criddle method was very accurate.

Shruti Bansode i can add the answer like For example, air temperature, relative humidity and wind velocity are all taken into account in the penman monteith method, which is more accurate than the Thornthwaite method. The best method for estimating potential evapotranspiration is FAO 56 Penmann-Monteith.

Noha Abdelwarth The FAO's Land and Water Division developed the ETo calculator. FAO-standard reference evapotranspiration (ETo) is one of its primary functions. The ETo calculator uses the Penman-Monteith equation to estimate ETo from meteorological data.

Yahampath Marambe yes i do like to add the statement that It isn't required. Your analogy may hold true if reference ET is equal to potential evapotranspiration. The actual water consumption of a crop is represented by its evapotranspiration (ETa). Because the field is not under standard conditions, ETa is usually equal to or less than ETc.

You can estimate the solar ratiation.

I think, evapotranspiration (ET) is directly related to water body area, temperature, and wind speed. LULC can directly help to caculate water body area. LULC is one component, ET is one vector of many components. Thus, predicting ET with LULC is one issue of weak or partial correlation in statistics. You need to enhance the cost function of machine learning optimization or the set of statistics estimation criteria.

Hello Dear, please check these articles:

Hello Dear, these articles may be useful:

Hello Dear, these articles may help you:

In this case ET will 150 {120 +30 (irrigation and rainwater)} mm only. increase or decrease in weight in lysimeter by one kg will be equal to one mm in depth.

You can develop your own code using Python or R.

Many packages are available for multilinear regression such as "SCI-KIT Learn".

This packages efficiently work for regression of time-series data also.

I would suggest you to use Python and do the following steps:

1. Read raster file in Python using Rasterio package

2. Convert it into Numpy array by keeping dimension as your time steps

3. Fit the regression model in Numpy Array data

I hope this would solve your problem.

That is a good question.

Hi dear Gonzalo.

For you're answer question, I recomendation Earth Data. In the database you can downloading with different formats.

If no local crop coefficient (Kc) is available, the reference evaporation (ETo) can be used to determine the water requirement of the target crop.

Please see and enter this link to download my different papers (field, fruits Vegetables crops) using reference evaporation (ETo) to determine of water requirements and irrigation scheduling whit modern irrigation systems in Egypt

You can get evapotranspiration data (csv or excel files) from website of the weather station of meteorological department of each country.

Just like PET and ETo, there are several concepts, which may confuse you. For example, water use efficiency and water productivity; crop factor and crop coefficient; water foot print and virtual water; diffusion pressure deficit(DPD) and cell water potential; and so on. I have tried to discuss these kinds of issues in my latest book "Irrigation and Water Management" Published by Ane Books, New Delhi (2021)

Try the Water Erosion Prediction. (WEPP) model. We have been working for more than 20 years enhancing the hydrology in it for steep forested watersheds. The water file it generates has daily ET, surface runoff, shallow lateral flow, and deep seepage. Post processing the deep seepage can give the daily base flow. Base flow estimation is done internally for US, European and Australian watersheds on WEPP CLOUD but requires more effort with QGIS and QWEPP elsewhere in the world. Let me know if you have questions.

Yeah, I've tried those. I also have Fluxcom, Fluxnet datasets. I'm looking beyond these.

he or ha?

and consider the variations in the hydrogeologic environment

I don’t know if I could be of help. From what I gather, everything is generated by models. Typically at a minimum, rainfall and runoff are measured, and then the unknowns are estimated with models perhaps. Also, if you try to do the water balance by calendar year, one year may be very wet such as in January or December, and the other month or year may be dry, resulting in some estimation error. The USGS uses the water year approach, with September 30 and October 1 the end and beginning of a water year. But you might in SE USA imagine a hurricane toward end of a September, and much of the flow in a large river showing up in October, for example. Truthfully, your situation is quite unusual, suggesting some errors in assumptions or models. But if your project is in karst terrain, it was shown by using dye studies developed by hydrologist Tom Aley of Missouri, that there were instances of interbasin transfer of water underground. And one might imagine that a watershed with a large karst spring might have more runoff than rainfall. When I worked on the Big Turnaround and other associated wildfires in FL and GA, USA, air became reacquainted with the Suwannee River, which has instances of discharge losses of streamflow due to faults or karst terrain loses underground. Another potential source of error is the rainfall measurement, unless one has a grid of rain gauges. Measuring stream flow professionally even has some error, perhaps plus or minus 20 percent unless very carefully gauged. With the anomalies suggested in your data, I would agree there is something difficult to accept without clarification and perhaps more detailed study, including geology.

@Eliton Sancler Gomes Sales Thanks

Please note that the relationship between reference crop evapotranspiration (ETo) and pan evaporation (Ep) is well known and you can estimate ETo using a proper pan-crop coefficient.

ETo = Kpc x Ep.

Where, ETo- reference evapotranspiration (mm/day); Kpc- pan-crop coefficient, and Ep- pan evaporation (mm/day).

If you want to calculate actual crop evapotranspiration (ETc), you have to multiply ETo with another coefficient, Kc (crop coefficient).

ETc = Kpc X Ep X Kc

Analysis of data on ETc indicates a high degree of correlation between pan evaporation values and crop evapotranspiration. This indicates the possibility of using pan evaporation values directly to estimate crop evapotranspiration (ETc) employing a coefficient called crop factor (Cf). The crop factor accounts for both Kpc and Kc. Therefore, if you can estimate the values of “crop factors” locally, you are freed of the burden of looking for pan coefficients and crop coefficients. The crop factor (Cf) varies with crop type and crop growth stages. This method is also known as Hargreaves Class A Pan evaporation method (Hargreaves, 1968). The relationship between crop evapotranspiration (ETc) and pan evaporation (Ep) is usually expressed as:

ETc= Ep X Cf

Where, ETc - crop evapotranspiration (mm/day); Cf - crop factor for the period; and Ep- pan evaporation (mm/day).

Estimation of daily ETc relies on daily readings of pan evaporation from a suitably placed evaporation pan together with an appropriate set of crop factors. Approximate value of crop factors were given by Hargreaves(1968). However, for accuracy, you have to find local Cf values. For using this method, the user has to enter daily readings of pan evaporation to calculate the crop water use for the day.

Reference: Hargreaves, G.H. 1968. Consumptive use derived from evaporation pan data. J. Irrig. Drain. Div. (American Society of Civil Engineers) 94(1): 97-104.

Please note that the concept of potential evapotranspiration" (PET) is no longer used in irrigation, and it has been replaced by reference crop evapotranspiration” (ETo) because of certain reasons. I will explain these two concepts first, and then come to actual evapotranspiration.

The potential evapotranspiration concept was first introduced in the late 1940s. It was defined as “the amount of evapotranspiration in a given time by a large vegetation of short green crop, completely shading the ground, of uniform height and with adequate moisture at all the times in the soil”. The major objective in bringing this “potential” evapotranspiration concept was to remove the effects specific to crops in the evapotranspiration process.

The notion of PET was well accepted by the scientific fraternity, and it became the focal idea in estimating crop water use for a long time. After some time, however, certain limitations cropped up. In the definition of PET, the ET rate was not related to a specific crop. The main confusion with PET was that there were many types of crops that fitted into the description of a ‘short green crop’, and one might be confused as to which short crop should be selected! Having considered the ambiguities and limitations of PET, by the early 1980s, the concept of “reference crop evapotranspiration” (ETo) was introduced.

The major objective of introducing ETo was to assess the evapotranspiration demand of the atmosphere independently without considering the effects of crops, cultivar differences, crop development, and management practices.

In reference evapotranspiration, the reference surface closely resembles an extensive surface of green grass of uniform height, actively growing, completely shading the ground, and with adequate water. The FAO Expert Consultation on Revision of FAO Methodologies for Crop Water Requirements (1990) after specifying the reference surface explicitly, defines reference evapotranspiration as : “the rate of evapotranspiration from a hypothetical reference crop with an assumed crop height of 0.12m, a fixed surface resistance of 70 s/m and an albedo of 0.23, closely resembling the evapotranspiration from an extensive surface of green grass of uniform height, actively growing, well-watered, and completely shading the ground.”

"Actual crop evapotranspiration" (ETa) or simply "crop evapotranspiration" (ETc) refers to the evapotranspiration by a particular crop in a given period under the prevailing soil, water, and atmospheric conditions. Actual crop evapotranspiration can be defined “as the evapotranspiration from disease-free, well-fertilized crops grown in large fields under optimum soil water conditions, which achieve full production under the given climatic conditions”.

ETa can be determined by direct methods such as lysimeters or estimated from climatic data by integrating the effect of crop characteristics into ETo. The ratio of crop ET to reference ET (ETa/ETo), called crop coefficient (Kc), is used to relate them as given in the following relationship..

ETa = ETo x Kc

Where, ETa = Actual crop evapotranspiration (mm/day); ETo = Reference crop evapotranspiration (mm/day); and Kc = Crop coefficient

You need to divide the Le, which should be in an energy unit such as W/m-2 by the latent heat of evaporation (i.e. the amount of energy required to evaporate 1g or 1ml of water) which is 2257 J/g . For example, if you have a total LE of 500 W/m-2 for one hour this would be 1800 000 J of energy (with watts equal to jouls per second). Enough to evaporate 798 g of water per m-2 (1800000/2257). This is equal to 0.798 mm of evaporation (1 kg H20 per m-2 = 1 mm). Good luck with the conversion!

The last addition was incomplete. The Richards equation does not fully describe the measured phenomena so conceptual representations are equally valid in my experience. The real physics must include air and vapour flow.

I should also have given you my last "accepted" paper :-)

Hi Dong, In a greenhouse having nearly zero wind speed, movement of air by convection takes over. Leaves are warmed by photons. The air in contact with the leaves warms and then expands and becomes more bouyant than the air above it. It therefore "floats" upward, vertically along with water vapor and is replaced by cooler air from above it. That process sustains some evapotranspiration even with zero wind speed. ET is all about energy. The energy inside a greenhouse that comes from solar radiation must be consumed by some other process that is represented by G, H or ET. FAO56 and ASCE literature therefore recommend that a lower limit of 0.5 m/s is used for the wind term in the PM equation to approximate the bouyant, convective transport and mixing of air under very low wind conditions. Rick Allen

The FAO recommends hargreaves samani equation (HG) is an alternative method to compute PET (ETo) in case the penman monteith equation can't be used for any reason

hi,

u can check these references.

@article{SALAMA2015418,

title = "Simple equation for estimating actual evapotranspiration using heat units for wheat in arid regions",

journal = "Journal of Radiation Research and Applied Sciences",

volume = "8",

number = "3",

pages = "418 - 427",

year = "2015",

issn = "1687-8507",

doi = "https://doi.org/10.1016/j.jrras.2015.03.002",

url = "http://www.sciencedirect.com/science/article/pii/S1687850715000321",

author = "M.A. Salama and Kh.M. Yousef and A.Z. Mostafa",

For evaporation you take the top most area. So the area contributing to evaporation is 3.14 x 0.1725 x 0.1725 = 0.093 m2 or 930 cm2

Yes there is a difference in evaporatranspiration. Evapotranspiration will be high in areas where water is not limited, that is where there are more water resources.

Thank you very much, it's a great job. When you have the same algorithm in R let me know.

Penman-Monteith (PM) approach

The energy balance equation is equal to:

rn= H+LE+H

Also the value H is equal to:

Use gdal, numpy, and matplotlib in Python.

I guess your pot has more or less the shape of a conical frustum (i.e. a cone with a cut-off tip). The surface area of a conical frustum without the bottom and top areas is A = pi * (r1 + r2) * s = pi * (r1 + r2) * sqrt((r1 - r2)^2 + h^2), where r1 and r2 are the bigger and smaller radius of the frustum, respectively, s is the slant height (length of the side) and h is the height of the frustum.

You likely want to include the area of the bottom, but not of the opening at the top (because the pot is open there), so you have to add pi * r2^2 and end up with pi * ((r1 + r2) * sqrt((r1 - r2)^2 + h^2) + r2^2) = 3.141593 * ((34.5 / 2 + 32 / 2) * sqrt((34.5 / 2 - 32 / 2)^2 + 32^2 ) + 32^2 / 4) cm^2 = 4149.5 cm^2

Here are the equations for surface and volume of a conical frustum: https://mathworld.wolfram.com/ConicalFrustum.html

The following link may help

www.atmos.washington.edu › ...PDF

Lecture 14 - Science of Climate

Agreed Arbind K. Choudhary

2011 Sensitivity of evapotranspiration to solar radiation, vapour pressure deficit and other meteorological parameters. Search on google.

You are welcome.

Dear Ngula,

maybe you can find your problem's answer in the following link:

Have a look at these articles for satellite-based ET estimates and CGIAR‐CSI datasets and also the methodology to standardize these products.

Article Evaluation of Variable-Infiltration Capacity Model and MODIS...

Article Hydrological Response to Agricultural Land Use Heterogeneity...

Article Modelling the dynamics of evapotranspiration using Variable ...

Article The Grass is not Always Greener on the Other Side: Seasonal ...

Article Multi-Model Approach to Assess the Dynamics of Hydrologic Co...

Article An Approach for Estimation of Evapotranspiration by Standard...

I hope you may find it useful

I have used the delta method to downscaling the grid (raster data) at 1 km of spatial resolution. I am attaching two papers for you to consult.