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Jenkins Macedo
Christina Geller
GEOG392
November 4, 2013
Discussion Questions: Monitoring of Surface and Groundwater Resources
Choi et al., 2008
1. On  page  2,  the  authors explain  the problems with the three measurement systems for calculating
soil  moisture.  What  problems  do  you  think  are  more  concerning  than  others?  What  problems
will carry over in a mixed analysis scheme?
2. On  page  4,  Choi  et  al.  explain  how  the  vegetation temperature and the surface temperature are
assumed  to  be  the  same  in   the  calculation  of  soil moisture by AMSR­E. Do you agree with this
assumption? Why or why not?
3. Given  what  you  learned  about  the  Common  Land  Model  during  our  discussion  of  surface
albedo from our discussion of surface albedo and the results from this study, would you push for
further utilization of CLM? Why or why not?
4. The  authors  suggest  developing  a  better  understanding  of  the  impact  of  slope  and  elevation   on
soil  moisture  estimations.  Given  what  you  know  already,  what  impacts  would  you  expect  to
see?
5. Table  3  on  page  8  shows  how  the  correlation  coefficient  (R2)  declined  when  CLM  AMSR­E
are  used  together  yet  the  Root  Mean  Squared  Error  (RMSE)  typically  improved. Why do you
think that is?
Reichle et al., 2007
1. On  page  1,  the  authors’  claimed  that  “satellite  retrievals  alone;  however,  are  not  sufficient  for
weather  and  climate  forecast  initialization  because  of  gaps in spatial and temporal coverage and
because  model  variables,  such  as  deeper  root  zone  soil  moisture  cannot  be   observed  from
space.”  Given  what  we’ve  have  learned  thus  far,  do  you  agree  or  disagree?  What  are  the
associated  complexities   of  using   meteorological  forcing  inputs  from  land  surface  model  with
satellite­driven soil moisture retrievals?
2. On  page  2,  it  is  stated  that  “via  the  land  surface  model,  the  system  propagates  the  surface
information  the  satellite  into  deeper  soil  and  thereby  provides  improved  estimates  of  root  zone
soil  moisture.”  What  do  you  would  be  some  of  the  setbacks  with  this  approach?  How  would
this  approach  be  applicable  in  ecosystems  with  dense  forest  canopy  and  those  with  lesser
canopy coverage?
3. On  page  2,  the  authors  noted  that  rescaling  satellite  data  prior  to  the  assimilation  process   by
matching  the  satellite  data’s  cumulative  distribution  function  to  the  model’s  climatology  builds
heavily  on  the  anomaly  time  series,  which  is the basis for forecast initialization, instead of relying
on  the  mean  square  error measures, which cannot be validated. Are four years of data sufficient
ground  to make this assertion about the  validation  process? Do you agree with their claim? Why
or why not?
4. On  page  2  in  the  second  column  in  the  3rd  paragraph  under  satellite  retrievals,  the  authors
described  that  the  AMSR­E  satellite  retrievals  of  surface  soil  moisture  data  used  are  from
NASA  Level­2B  AMSR­E  “AE_Land”  product,  which  includes  measurements  of  surface  soil
moisture,  a  vegetation/roughness  correction,  and  “quality  control  variables.”  What  are  the
quality  control  variables?  Do  you  think  the  authors’  could have deliberated on these variables a
little more?
5. What  are  your   critiques  about  the  structure  of  the  paper  and  the  way  in  which  results  were
presented? Who  do  you  think are the targeted audience for  such a paper apart from just the fact
that it was published in the Journal of Geophysical Research?
Swenson et al., 2008
1. Who do you think are the targeted audience of this paper and why? Was the subject matter
adequately assessed?
2. Given the scope of the data used, do you think the authors did a great job explaining the data,
methods, results, and purpose of the research adequately?
3. On page 2 in the second column, the authors claim that “the methods described account for
deeper soil moisture is applied to the region of the Southern Great Plains of the U.S., but is
applicable to other observational data sets, whether in situ or remotely sensed data.” Do you
agree with this claim given the challenges in many developing countries where in situ and
remotely sensed data acquisition is not only a logistical problem?
4. What are some of the complexities in monitoring and assessing groundwater resources; given
the potential for groundwater to extend beyond geographic boundaries of states and regions?
Syed et al., 2008
1. Who do you think are the targeted audience of this paper? Why?
2. What are some of the major challenges/advantages of this approach?
3. Do  you  agree  with  the  authors’  claim  that  with  longer  time  series,  GRACE  will  contribute  to
improved  the  understanding   of  how  terrestrial  water  storage  respond  to  climate  change
variability?
4. Are  the  results  from  their  analyses  compelling  enough  to  warrant  the  advancement  of  this
science?
5. If  you  were  to  make  a  constructive  input  to  the  methodology  in  the  advancement  of  this
approach, what would your recommendation be and why?
de Jeu et al., 2008
1. On  page  408,  the  authors  explain   that  “a  data  mask  was  developed  on  the  AMSR­E  data
products  to  eliminate  those  data   cells  where  data  values  were   either  meaningless  due  to  frozen
soil  conditions,  snow  cover  or  excessive  vegetation”.  What  biases  do  you  see  arising  from  the
use of this data mask?
2. The  AMSR­E  developed  by  NSIDC  had  a  correlation  coefficient  of  ­0.01 in the France study
area  and  0.00  for  Spain.  Because  of  this,  the  authors  decided  to  exclude  AMSR­E  (NSIDC)
from  their comparison analysis. What do you think the authors might have missed contributing to
the field by excluding this soil moisture product?
3. This  study  pointed  out  a  strong  similarity  between  both  products  in  sparse  to  moderate
vegetated  regions  with  an  average  correlation  coefficient  of  0.83.  Given  this  similarity,  do  you
see a benefit of further developing one product or using one product over the other?
4. Do you agree with the authors’ suggestion of combining the two products?
5. The  authors  explain  the  reasons  why  low  correlations  may  be  found  in densely vegetated areas
and deserts. Are there other reasons you think are missing from their explanation?
General Questions
1. If remote sensing of soil moisture is primarily capable of representing variation in near surface
wetness (e.g. upper 5 to 10 cm of soil) and not wetness deeper in the rooting zone, what does
this mean for the processes that can be well­presented by this product?
2. Does satellite­based microwave remote sensing of soil moisture have an adequate spatial
resolution for representing the processes in which we are interested? How does its spatial
representativeness compare to other products we are used to dealing with (e.g. NDVI, surface
temperature, etc.)?
3. Does gravity­based remote sensing of terrestrial water storage have a spatial resolution
adequate for representing the processes of interest? What processes can and cannot be
characterized at this resolution?
4. What are some of the potential challenges with integrating soil moisture or terrestrial water
storage products with the VI or surface temperature products that are also used for
characterizing evapotranspiration and the surface water status?
Bibliography
Choi, M., Jacobs, J.M., and Bosch, D.D. (2008). Remote Sensing Observatory Validation of Surface Soil
Moisture using Advanced Microwave Scanning Radiometer E, Common Land  Model, and Ground­based
Data: Case Study in SMEX03 Little River Region, Georgia, U.S. Water Resources Research, Vol. 44, pg.
1­14.
de Jeu, A.M., Wagner, W., Holmes, T.R.H., Dolman, A.J., van de Giesen, N.C., and Friesen, J.
(2008). Global Soil Moisture Patterns Observed by Space Borne Microwaves Radiometers and
Scatterometers. Survey Geophysics, Vol. 29, pg. 399­420.
Reichle, R.H., Koster, R.D., Lui, P., Mahanama, S.P.P., Njoku, E.G., and Owe, M., (2007).
Comparison and Assimilation of Global Soil Moisture Retrievals from the Advanced Microwave Scanning
Radiometer for the Earth Observing System (AMSR­E) and the Scanning Multichannel Microwave
Radiometer (SMMR). Journal of Geophysical Research, Vol. 112, pg. 1­14.
Swenson, S., Famiglietti, J., Basara, J., and Wahr, J. (2008). Estimating Profile Soil Moisture and
Groundwater Variations using Gravity Recovery and Climate Experiment (GRACE) and Oklahoma Mesonet
Soil Moisture Data. Water Resource Research, Vol. 44, pg. 1­12.
Syed, T.H., Famiglietti, J.S., Rodell, M., Chen, J., and Wilson, C.R. (2008). Analysis of Terrestrial
Water Storage Changes from Gravity Recovery and Climate Experiment (GRACE) and Global Land Data
Assimilation System (GLDAS). Water Resources Research, Vol. 44, pg. 1­15.

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Surface Soil Moisture & Groundwater Monitoring, Estimations, Variations, & Retrievals Discussions Questions

  • 1. Jenkins Macedo Christina Geller GEOG392 November 4, 2013 Discussion Questions: Monitoring of Surface and Groundwater Resources Choi et al., 2008 1. On  page  2,  the  authors explain  the problems with the three measurement systems for calculating soil  moisture.  What  problems  do  you  think  are  more  concerning  than  others?  What  problems will carry over in a mixed analysis scheme? 2. On  page  4,  Choi  et  al.  explain  how  the  vegetation temperature and the surface temperature are assumed  to  be  the  same  in   the  calculation  of  soil moisture by AMSR­E. Do you agree with this assumption? Why or why not? 3. Given  what  you  learned  about  the  Common  Land  Model  during  our  discussion  of  surface albedo from our discussion of surface albedo and the results from this study, would you push for further utilization of CLM? Why or why not? 4. The  authors  suggest  developing  a  better  understanding  of  the  impact  of  slope  and  elevation   on soil  moisture  estimations.  Given  what  you  know  already,  what  impacts  would  you  expect  to see? 5. Table  3  on  page  8  shows  how  the  correlation  coefficient  (R2)  declined  when  CLM  AMSR­E are  used  together  yet  the  Root  Mean  Squared  Error  (RMSE)  typically  improved. Why do you think that is? Reichle et al., 2007 1. On  page  1,  the  authors’  claimed  that  “satellite  retrievals  alone;  however,  are  not  sufficient  for weather  and  climate  forecast  initialization  because  of  gaps in spatial and temporal coverage and because  model  variables,  such  as  deeper  root  zone  soil  moisture  cannot  be   observed  from space.”  Given  what  we’ve  have  learned  thus  far,  do  you  agree  or  disagree?  What  are  the associated  complexities   of  using   meteorological  forcing  inputs  from  land  surface  model  with satellite­driven soil moisture retrievals? 2. On  page  2,  it  is  stated  that  “via  the  land  surface  model,  the  system  propagates  the  surface information  the  satellite  into  deeper  soil  and  thereby  provides  improved  estimates  of  root  zone soil  moisture.”  What  do  you  would  be  some  of  the  setbacks  with  this  approach?  How  would this  approach  be  applicable  in  ecosystems  with  dense  forest  canopy  and  those  with  lesser canopy coverage?
  • 2. 3. On  page  2,  the  authors  noted  that  rescaling  satellite  data  prior  to  the  assimilation  process   by matching  the  satellite  data’s  cumulative  distribution  function  to  the  model’s  climatology  builds heavily  on  the  anomaly  time  series,  which  is the basis for forecast initialization, instead of relying on  the  mean  square  error measures, which cannot be validated. Are four years of data sufficient ground  to make this assertion about the  validation  process? Do you agree with their claim? Why or why not? 4. On  page  2  in  the  second  column  in  the  3rd  paragraph  under  satellite  retrievals,  the  authors described  that  the  AMSR­E  satellite  retrievals  of  surface  soil  moisture  data  used  are  from NASA  Level­2B  AMSR­E  “AE_Land”  product,  which  includes  measurements  of  surface  soil moisture,  a  vegetation/roughness  correction,  and  “quality  control  variables.”  What  are  the quality  control  variables?  Do  you  think  the  authors’  could have deliberated on these variables a little more? 5. What  are  your   critiques  about  the  structure  of  the  paper  and  the  way  in  which  results  were presented? Who  do  you  think are the targeted audience for  such a paper apart from just the fact that it was published in the Journal of Geophysical Research? Swenson et al., 2008 1. Who do you think are the targeted audience of this paper and why? Was the subject matter adequately assessed? 2. Given the scope of the data used, do you think the authors did a great job explaining the data, methods, results, and purpose of the research adequately? 3. On page 2 in the second column, the authors claim that “the methods described account for deeper soil moisture is applied to the region of the Southern Great Plains of the U.S., but is applicable to other observational data sets, whether in situ or remotely sensed data.” Do you agree with this claim given the challenges in many developing countries where in situ and remotely sensed data acquisition is not only a logistical problem? 4. What are some of the complexities in monitoring and assessing groundwater resources; given the potential for groundwater to extend beyond geographic boundaries of states and regions? Syed et al., 2008 1. Who do you think are the targeted audience of this paper? Why? 2. What are some of the major challenges/advantages of this approach? 3. Do  you  agree  with  the  authors’  claim  that  with  longer  time  series,  GRACE  will  contribute  to improved  the  understanding   of  how  terrestrial  water  storage  respond  to  climate  change variability? 4. Are  the  results  from  their  analyses  compelling  enough  to  warrant  the  advancement  of  this science? 5. If  you  were  to  make  a  constructive  input  to  the  methodology  in  the  advancement  of  this
  • 3. approach, what would your recommendation be and why? de Jeu et al., 2008 1. On  page  408,  the  authors  explain   that  “a  data  mask  was  developed  on  the  AMSR­E  data products  to  eliminate  those  data   cells  where  data  values  were   either  meaningless  due  to  frozen soil  conditions,  snow  cover  or  excessive  vegetation”.  What  biases  do  you  see  arising  from  the use of this data mask? 2. The  AMSR­E  developed  by  NSIDC  had  a  correlation  coefficient  of  ­0.01 in the France study area  and  0.00  for  Spain.  Because  of  this,  the  authors  decided  to  exclude  AMSR­E  (NSIDC) from  their comparison analysis. What do you think the authors might have missed contributing to the field by excluding this soil moisture product? 3. This  study  pointed  out  a  strong  similarity  between  both  products  in  sparse  to  moderate vegetated  regions  with  an  average  correlation  coefficient  of  0.83.  Given  this  similarity,  do  you see a benefit of further developing one product or using one product over the other? 4. Do you agree with the authors’ suggestion of combining the two products? 5. The  authors  explain  the  reasons  why  low  correlations  may  be  found  in densely vegetated areas and deserts. Are there other reasons you think are missing from their explanation? General Questions 1. If remote sensing of soil moisture is primarily capable of representing variation in near surface wetness (e.g. upper 5 to 10 cm of soil) and not wetness deeper in the rooting zone, what does this mean for the processes that can be well­presented by this product? 2. Does satellite­based microwave remote sensing of soil moisture have an adequate spatial resolution for representing the processes in which we are interested? How does its spatial representativeness compare to other products we are used to dealing with (e.g. NDVI, surface temperature, etc.)? 3. Does gravity­based remote sensing of terrestrial water storage have a spatial resolution adequate for representing the processes of interest? What processes can and cannot be characterized at this resolution? 4. What are some of the potential challenges with integrating soil moisture or terrestrial water storage products with the VI or surface temperature products that are also used for characterizing evapotranspiration and the surface water status?
  • 4. Bibliography Choi, M., Jacobs, J.M., and Bosch, D.D. (2008). Remote Sensing Observatory Validation of Surface Soil Moisture using Advanced Microwave Scanning Radiometer E, Common Land  Model, and Ground­based Data: Case Study in SMEX03 Little River Region, Georgia, U.S. Water Resources Research, Vol. 44, pg. 1­14. de Jeu, A.M., Wagner, W., Holmes, T.R.H., Dolman, A.J., van de Giesen, N.C., and Friesen, J. (2008). Global Soil Moisture Patterns Observed by Space Borne Microwaves Radiometers and Scatterometers. Survey Geophysics, Vol. 29, pg. 399­420. Reichle, R.H., Koster, R.D., Lui, P., Mahanama, S.P.P., Njoku, E.G., and Owe, M., (2007). Comparison and Assimilation of Global Soil Moisture Retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR­E) and the Scanning Multichannel Microwave Radiometer (SMMR). Journal of Geophysical Research, Vol. 112, pg. 1­14. Swenson, S., Famiglietti, J., Basara, J., and Wahr, J. (2008). Estimating Profile Soil Moisture and Groundwater Variations using Gravity Recovery and Climate Experiment (GRACE) and Oklahoma Mesonet Soil Moisture Data. Water Resource Research, Vol. 44, pg. 1­12. Syed, T.H., Famiglietti, J.S., Rodell, M., Chen, J., and Wilson, C.R. (2008). Analysis of Terrestrial Water Storage Changes from Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS). Water Resources Research, Vol. 44, pg. 1­15.