Python IDLE (Integrated Development and Learning Environment) for remote sensing, hydrological and meteorological applications

4/14/2016 Python IDLE (Integrated Development and Learning Environment) for remote sensing, hydrological and meteorological applications | Ramesh Dhungel | L…
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Python IDLE (Integrated Development and Learning
Environment) for remote sensing, hydrological and
meteorological applications
Apr 14, 2016 87 views 0 Likes 0 Comments   
This post will discuss about Python IDLE (Integrated Development Environment
or Integrated Development and Learning Environment), especially for the
meteorological and hydrological applications. Image processing environment like
ERDAS Imagine, ENVI had been widely used in remote sensing community as
well as Matlab and R. But, there is a very little discussion about the Python IDLE
integrated with ArcGIS, basically with ArcScrit or ArcPy. It can be difficult to
implement looping and iterative process in ModelBuilder of ArcGIS, ERDAS
Imagine and ENVI, then the role comes of the Python IDLE. ERDAS Image
Model Maker can also be messy if you are developing complex algorithms. Python
IDLE not only can handle these complex processes in elegant way, but it will
provide a dynamic environment for advance algorithm development. I have been
working on the algorithm development of Land surface model using Python IDLE
to compute evapotranspiration (ET), agricultural water management, irrigation
Ramesh Dhungel
Water Resources, Remote Sensing and Land
Surface Modeler (LSM) (Ph.D. Civil Engineering)
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scheduling, precision agriculture, geospatial modeling etc., which virtually
includes many boundary layer processes. This post will show some of the
important steps for developing complex algorithms using Python IDLE. These
algorithms are implemented while estimating ET for 3hour time period.
Figure 1: Estimation of ET using NARR and METRIC model for 3hour time
period for the entire months ( i.e., 3 * 8 * 30 ) images of ET
For two source surface energy balance model, please follow the
cartoon in the following post.
https://www.linkedin.com/pulse/pixelscalevalidationgroundtruthsatellite
basedsurfacedhungel?trk=pulse_spockarticles
Importing necessary modules:
import arcgisscripting
import os
import time
import sys
# Create a geoprocessor object, for example gp or rdh or something
gp = arcgisscripting.create(9.3)

# Check out ArcGIS Spatial Analyst extension license
gp.CheckOutExtension("Spatial")
# Overwrite existing outputs
gp.OverWriteOutput = 1
Here are some of the examples: As usual, these codes are part of codes developed
in this process, so only a portion is shown.
a) Reading the image using Python IDLE:
ComDir = r'I:PapersMeterological_applicationsCommon'
DEM =os.path.join(ComDir, 'DEM_clipped_large.img')
b) Conditional sentences:
Theta_ref=os.path.join(ComDir,'theta_ref_.img')
Theta_ref_eqn = 'con( '+Soil_final+' == 0 , 0.360, con( '+Soil_final+' == 1 , 0.36,
con( '+Soil_final+' == 2 , 0.34, con( '+Soil_final+' == 3 , 0.329,'
Theta_ref_eqn+= 'con( '+Soil_final+' == 4 , 0.2, con( '+Soil_final+' == 5 , 0.2,
con( '+Soil_final+' == 6 , 0.30, con( '+Soil_final+' == 7, 0.360,'
Theta_ref_eqn+= 'con( '+Soil_final+' == 8 , 0.36, 0.36 ) ) ) ) ) ) ) ) )'
gp.SingleOutputMapAlgebra_sa(Theta_ref_eqn,Theta_ref)
c) Writing equations:
workDir =
r'I:PapersMeterological_applicationsCombined_Ess_Ec_interpolation_06112010'
Water_flag = os.path.join(workDir, 'Water_flag.img')
Water_flg_eqn= 'con(('+Landuse+' == 11) or ( '+NDVI+' 0), 1, 0)'

  gp.SingleOutputMapAlgebra_sa(Water_flg_eqn, Water_flag)
d) For and while looping (multi looping):
i= Current_satellite_passing_Index + 1
proceed with all the algorithms
H_Flux_new_soil = os.path.join(workDir,'sheat_new_soil_'+ str(i)+'.img')
G_Flux_new_soil = os.path.join(workDir,'gheat_new_soil_'+ str(i)+'.img')
while i = Next_satellite_passing_Index + 1 :
else:
Figure 2 shows an example the algorithm developed in the process:
e) Monitoring pixels in the simulation process using python shell:
   if DEBUG:
      PrintCellValue(gp,ETsoil_sec_pre,'tResult ETsoil_sec_pre: ')
      PrintCellValue(gp,ETveg_sec_pre,'tResult ETveg_sec_pre: ')
      PrintCellValue(gp,soilm_pre,'tResult soilm_pre: ')

Figure 3 shows the python shell while running the algorithm:
f) Extracting pixel or cell value from directory:
R_Rgl=(gp.GetCellValue( Rgl,'2601776.501 1325307.224  ', '1').getoutput(0))
myRgl = 'Rgl: '+ R_Rgl + 'n'
myfile.write(myRgl)
Write a table for certain cell value for entire simulation period:
for i in range (i, 1359, 1):
   myOut = ','.join((str(i), R_Date, R_precip, R_irrigation, R_NDVI, R_fc,
R_In_short, R_EThour_com, R_EThour_com_final, R_ETrF_com, R_ETcor,
R_ETveg_hour, R_ETsoil_hour, R_ETcor_veg, R_ETcor_soil,
R_ETcor_com,'n'))
   myfile.write(myOut)
myfile.close()
g) For realtime plotting options in Python IDLE, please look my
previous post:

Realtime simulation of the iterative calculation of the
satellite based surface energy fluxes
https://www.linkedin.com
White paper An iterative procedure is still used to converge fluxes in evapotranspiration (ET)
calculation in many ET models like METRIC (Allen et al., 2007), SEBAL (Bastiaanssen et al.,
1998) and SEBS (Su, 2002). In this post, a realtime simulation is shown to expedite the satellite
based surface energy balance fluxes in low wind speed condition.
Finally, once the image is processed, you can use ArcGIS or any other image
viewer to analyze the results. One of the drawbacks of Python IDLE might be the
need of ArcGIS license as it is integrated into ArcGIS.
For the further reading, please look these references:
Dhungel R, Allen R.G., Trezza R., Robison C. W., 2014. Comparison of Latent
Heat Flux Using Aerodynamic Methods and Using the Penman–Monteith
Method with SatelliteBased Surface Energy Balance. Remote Sensing.
6(9):88448877.
Using a Two Source Energy Balance model to compute evapotranspiration
between satellite overpasses: Simulation of ET using weather and Satellite data
(Under Review). Meteorological Applications (MET150095).
Dhungel, R., 2014. Time integration of evapotranspiration using a two source
surface energy balance model using NARR reanalysis weather data and satellite
based METRIC data. D. Dissertation, University of Idaho.
http://digital.lib.uidaho.edu/cdm/ref/collection/etd/id/829
Please keep on following for the updates of the post!!!!!!!!!!!

Python IDLE (Integrated Development and Learning Environment) for remote sensing, hydrological and meteorological applications

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