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Messages - Karthikeyan L

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A work by Shwetha who is a PhD student under Prof. D. Nagesh Kumar has been published recently in ISPRS Journal of Photogrammetry and Remote Sensing.

Article: Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN

Description: Land Surface Temperature (LST) with high spatio-temporal resolution is in demand for hydrology, climate change, ecology, urban climate and environmental studies, etc. Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the most commonly used sensors owing to its high spatial and temporal availability over the globe to obtain LST, but is incapable of providing this data under cloudy conditions, resulting in gaps in the data. In contrast, microwave measurements captured by the microwave sensors such as Advance Microwave Scanning Radiometer (AMSR)-Earth Observing System and AMSR2 are capable of penetrating through clouds. The current study proposes a methodology by exploring this property to predict high spatio-temporal resolution LST under cloudy conditions during daytime and nighttime without employing in-situ LST measurements. To achieve this, Artificial Neural Networks (ANNs) based models are employed for different land cover classes, utilizing Microwave Polarization Difference Index (MPDI) at finer resolution with ancillary data. MPDI was derived using resampled (from 0.25 to 1 km) brightness temperatures (Tb) at 36.5 GHz channel of dual polarization from AMSR-E and AMSR2 sensors. The proposed methodology is tested over Cauvery basin in India. Results indicated that the proposed methodology performed well for the considered land cover classes.

Shwetha, H. R., & Kumar, D. N. (2016). Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN. ISPRS Journal of Photogrammetry and Remote Sensing, 117, 40-55.

Prof. Nagesh Kumar can be reached at

Work carried out by Dr. Sonali along with Prof D. Nagesh Kumar and Prof. R. S. Nanjundaiah has been published in Theoretical and Applied Climatology.

Title: Intercomparison of CMIP5 and CMIP3 simulations of the 20th century maximum and minimum temperatures over India and detection of climatic trends

Description: Climate change impact assessment has become one of the most important subjects because of the recent increase in frequency of extreme events. This study analyzed the skills of climate models from CMIP3 and CMIP5 for capturing the maximum and minimum temperatures (Tmax and Tmin) over India. Skills viz. Skill_r, Skill_s, Skill_rmse, Skill_All, and behavior viz. pattern correlation, pattern rmse are used to assess the ability of the 46 climate models. Skill_All, which is an intersection of the three metrics viz. Skill_r, Skill_s, Skill_rmse has been introduced for the first time. There is an enhancement in skill of models from CMIP5 compared to CMIP3. Significant trends in Tmin are observed  during most of the seasons over the entire Indian region during last four decades. This establishes the signature of climate change over most parts of India.

Sonali, P., Kumar, D. N., & Nanjundiah, R. S. (2016). Intercomparison of CMIP5 and CMIP3 simulations of the 20th century maximum and minimum temperatures over India and detection of climatic trends. Theoretical and Applied Climatology, 1-25.

Prof. Nagesh Kumar can be reached at

A work carried out by Seetha, PhD student of Prof. M. S. Mohan Kumar has been published in Water Resources Research (WRR) journal.

Correlation equations for average deposition rate coefficients of nanoparticles in a cylindrical pore
In this study, correlation equations for the deposition rate coefficients of nanoparticles in a cylindrical pore under  unfavorable conditions are developed as a function of nine pore-scale parameters: the pore radius, nanoparticle radius, mean flow velocity, solution ionic strength, viscosity, temperature, solution dielectric constant, and nanoparticle and
collector surface potentials. The correlation equations, which follow a power law relation with nine pore-scale parameters, are found to be consistent with the column-scale and pore-scale experimental results, and qualitatively agree with the  colloid filtration theory. These equations can be incorporated into pore network models to study the effect of pore-scale parameters on nanoparticle deposition at larger length scales such as Darcy scale.

Article Citation:
Seetha, N., Majid Hassanizadeh, S., Kumar, M., & Raoof, A. (2015). Correlation equations for average deposition rate coefficients of nanoparticles in a cylindrical pore. Water Resources Research, 51(10), 8034-8059.

Prof. M. S. Mohan Kumar can be reached at

Recently a work carried out by Eswar, student of Prof. M. Sekhar has been published in International Journal of Remote Sensing (IJoR).

Land surface temperature (LST) is a vital parameter to understand and quantify the surface energy balance (which includes evapotranspiration, ET). Quantifying the water lost to the atmosphere through ET is needed for water budgeting and irrigation supply. LST varies spatially and temporally and is strongly influenced by surface moisture, vegetation and surface type. LST is measured by thermal sensors in remote sensing satellites. We get reliable measurements of LST from MODIS sensor at daily time scale with 1000 m spatial resolution. But unfortunately, the agricultural plots in India is typically of the size 100 m x 100 m and is very small when compared with a MODIS pixel. It becomes really difficult to get plot scale ET from MODIS LST measurements. High spatial resolution sensors like the Landsat provides LST measurements at 100 m spatial resolution but only once in 16 days. In order to get routine LST measurements at fine spatial scales, suitable spatial disaggregation approaches for LST needs to be developed. The article "Disaggregation of LST over India: comparative analysis of different vegetation indices" discusses about one such spatial disaggregation model for LST that is more suited for semi-arid countries like India.

Article Citation:
Eswar, R. , Sekhar, M. and  Bhattacharya, B. K. (2016), Disaggregation of LST over India: comparative analysis of different vegetation indices, International Journal of Remote Sensing, 37(5), 1035-1054,

Prof. M. Sekhar can be reached at

Announcements / Summer Courses in Climate Time Series Analysis in Germany
« on: February 18, 2016, 10:29:58 AM »
Conducted by: Manfred Mudelsee
At Bad Gandersheim, Heckenbeck, Germany

Basic Course: from 8 to 12 August 2016 (Requires knowledge in calculus)
Advanced Course: 15 to 19 August 2016 (Requires knowledge in calculus and basic statistics)

The courses are tailored to the needs of PhD students and postdocs in climatology as well as ecology, environmental sciences, geosciences, meteorology and hydrology. You get the required statistical tools and extensive hands-on training to become able to optimally analyse your data and answer the associated questions about the climate. You acquire the theoretical basis for understanding the tools and interpreting the results. You learn to determine the various sources of uncertainty in data, climate models and statistical estimation. You become aware of the three major pitfalls in climate data analysis: ignored autocorrelation, violated Gaussian assumption and ignored multiplicity of hypothesis tests.

Registration Deadline: 15 July 2016


The research findings of Dr. Sonali Pattnaik, working with Prof. D. Nagesh Kumar at the Department of Civil Engineering, IISc, have been published in

They have carried out "Detection & Attribution study over Temperatures of India" and concluded that the temperatures over India are indeed rising over past decades. It is observed that human induced changes have predominant effect over natural climate variations in causing this warming phenomenon.

Further details on the work can be found from the following weblinks.

Prof. Nagesh Kumar can be reached at

Post your question/information / Re: Modeling and Validation
« on: February 11, 2016, 03:54:31 PM »
Also, just to add some more, in case you have decided from pre-analysis of data that linear regression is indeed sufficient, then you can use all the data in fitting the model. In such a case, Cal-Val would be meaningless as the model once fitted can't be modified upon the mistakes it has learnt through calibration. So, we need not waste the data in calibrating and validating the model in this situation.

Post your question/information / Re: Modeling and Validation
« on: February 10, 2016, 07:55:57 PM »

Fitting regression involves some pre-analysis of data. This has to be done to check what kind of model suits your dataset. For example, if the predictors and predictand are linearly correlated (of course with some physical relationship involved), then one can go with simple linear regression (SLR) model. Now when the variables turnout to be nonlinearly correlated, SLR may not work that well. In that case, one has to go with either nonlinear regression models or blackbox models.

Once the model is fixed, now the data needs to be divided into parts to calibrate and validate the model. The data that goes into calibrating the model need to be carefully provided in such a way that all the extremes are taken into consideration. Remember that regression always works well in the case of interpolation and once it encounters out-of-range predictors, the simulations might be highly uncertain. Hence, our attempts of calibrating the model should be in such a way that we can - to the maximum extent - avoid extrapolation.

Once the model is successfully calibrated, provide it with validation data (which the model would have never encountered before) and compare the simulated and observed predictand. The most stringent way of comparing them will be to carry out white noise test (e.g., Ljung-Box test) on the residuals i.e., if residuals turn out to be white noise, then it indicates model did a good job in understanding the variability of data. But, in general this test may fail, the model may not completely mimic the data. In such a scenario one can represent how close simulated predictand values are to the observed values. This can be done using linear correlation, RMSE etc.

Programming / Re: [R] Reading a huge netcdf file :memory error
« on: January 22, 2016, 08:10:44 PM »
How about 'ncread' in matlab?

Programming / Make an executable file to run R script
« on: October 29, 2015, 05:39:05 PM »
Hi, I am trying to run R script on a linux machine which needs code to be provided as an executable file. Can anyone please provide relevant syntax to carry out this process?

Kindly let me know if I am missing any other information that is needed.

Thank you.

Programming / Re: IDE for R
« on: January 16, 2015, 11:03:59 AM »
Dear Sat Kumar,
RStudio is an IDE for R. Please find the concerned link below:

Hope this helps.

Post your question/information / Re: requesting IJRS article
« on: January 08, 2015, 12:22:29 PM »
Dear Sat Kumar,

Hi Jay,
Impact factors are calculated by Reuters' Journal Citation Reports []. All the journals consider their numbers for impact factors.

Post your question/information / Re: Remote Sensing
« on: December 16, 2014, 11:30:27 AM »
I can see that imwrite rescales the images either to [0 255] or to [0 1]. In your case I dont think you can covert the matrix into unsigned int since you actually need the image in with 'float' values.
I thought of one indirect way of getting things done.

Looks like imagesc displays the matrix as it is without doing any rescaling. So, create an image using imagesc and save it through its handle in tiff format.

For example,
I have taken a 50x50 matrix X containing values in [-1 to 1] range.

Code: [Select]
axis off

I have attached an image below that I generated with above lines.

Just in case, this is how X is generated:
Code: [Select]
for i=1:50
    for j=1:50
        X(i,j) = rand(1)*(b-a)+a;

Data / Passive Microwave Satellite Soil Moisture Data
« on: December 01, 2014, 12:30:06 PM »
I am posting links of various sources for getting soil moisture data. The list only covers passive microwave sensors. Pl suggest if any more additions can be made.

Passive Microwave Sensors:
1) AMSR-E- VUA-NASA- 0.25 degree daily global
Ascending Level 3 -
Descending Level 3 -

2) AMSRE- Univ. Montana- 0.25 degree daily global
Asc/ Dec Level 3 -

3) AMSRE- NASA- 0.25 degree daily global
Asc/ Dec Level 3 -

4) TRMM/TMI- VUA-NASA- 0.25 degree daily global
Asc/ Dec Level 3 -

5) SMOS- Level 2 Data-

6) SMOS - 0.25 degree daily global
Asc/ Dec Level 3 -

7) The SMMR and SSM/I soil moisture products are available through the following ftp site. One need ftp downloaders like FileZilla to access.
username : adaguest
pwd : downloader

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