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'''BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution)'''<ref name="arspub1"/><ref name="arspub2" /><ref name="arspub3" /><ref name=":1">{{Cite journal|last=Dhungel|first=Ramesh|last2=Allen|first2=Richard G.|last3=Trezza|first3=Ricardo|last4=Robison|first4=Clarence W.|date=2016|title=Evapotranspiration between satellite overpasses: methodology and case study in agricultural dominant semi-arid areas|journal=Meteorological Applications|language=en|volume=23|issue=4|pages=714–730|bibcode=2016MeApp..23..714D|doi=10.1002/met.1596|issn=1469-8080}}</ref> is a [[Computer model|Computer]] based [[Evapotranspiration]] (ET) model, which computes [[Water Use|water use]], primarily in [[agriculture]] [[landscape]] using [[remote sensing]] based information. It was developed by Ramesh Dhungel<ref name="arspub1" /><ref name="arspub2" /><ref name="arspub3" />'''<ref name=":1" /><ref name=":10" />[https://howwerespond.aaas.org/community-spotlight/kansas-farmers-minimize-water-use-as-the-southern-great-plains-become-more-arid/]''' and water resources group at [[University of Idaho]], Kimberly R & E Center between 2010 and 2014 and continuously improved (2017-2019). <ref name=":9">{{Cite web |url=https://scisoc.confex.com/scisoc/2018am/meetingapp.cgi/Paper/113852 |title=Next Generation Landscape Evapotranspiration Tools: Is It Feasible?. |website=ASA, CSSA, and SSSA - ASA, CSSA, and CSA International Annual Meeting |access-date=2019-05-11}}</ref><ref name=":10">{{Cite web |url=https://www.openaccessgovernment.org/water-for-plant-growth-the-foundation-of-the-global-food-supply-and-ecosystem-services/55166/ |title=Water for plant growth: The foundation of the global food supply and ecosystem services |last=Eccleston |first=Sally |date=2018-11-30 |website=Open Access Government |language=en-GB |access-date=2019-09-22}}</ref> It is used primarily for the analysis of [[Evapotranspiration|ET]] at point and regional scales, a frequently utilized method to estimate water use in [[agriculture]] and non-agriculture landscape.<ref name=":4">{{Cite journal |last=Zhang |first=Ying |last2=Zhang |first2=Ling |last3=Hou |first3=Jinliang |last4=Gu |first4=Juan |last5=Huang |first5=Chunlin |date=2017-09-21 |title=Development of an Evapotranspiration Data Assimilation Technique for Streamflow Estimates: A Case Study in a Semi-Arid Region |journal=Sustainability |language=en |volume=9 |issue=10 |pages=1658 |doi=10.3390/su9101658 |issn=2071-1050}}</ref><ref name=":0">{{Cite journal |last=Bhattarai |first=Nishan |last2=Wagle |first2=Pradeep |last3=Gowda |first3=Prasanna H. |last4=Kakani |first4=Vijaya G. |date=2017 |title=Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions |journal=ISPRS Journal of Photogrammetry and Remote Sensing |language=en |volume=133 |pages=128–141 |bibcode=2017JPRS..133..128B |doi=10.1016/j.isprsjprs.2017.10.010}}</ref><ref name=":2">{{Cite web |url=https://ogallala.tamu.edu/news/ |title=Sustaining economic activity from the Ogallala Aquifer through new water management technologies |date=2018-11-30 |access-date=11 January 2019}}</ref><ref name=":5">{{Cite journal |last=Akhtar |first=Fazlullah |last2=Awan |first2=Usman |last3=Tischbein |first3=Bernhard |last4=Liaqat |first4=Umar |date=2018-06-18 |title=Assessment of Irrigation Performance in Large River Basins under Data Scarce Environment—A Case of Kabul River Basin, Afghanistan |journal=Remote Sensing |language=en |volume=10 |issue=6 |pages=972 |bibcode=2018RemS...10..972A |doi=10.3390/rs10060972 |issn=2072-4292}}</ref> The estimation of [[Water Use|water use]] by vegetation is crucial for conserving water, managing Irrigation, and acquiring [[water right]] information.<ref name="arspub3" /> Farmers need these information for accurately applying water to crops for optimum yield.
'''BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution)'''<ref name="arspub1"/><ref name="arspub2" /><ref name="arspub3" /><ref name=":1">{{Cite journal|last=Dhungel|first=Ramesh|last2=Allen|first2=Richard G.|last3=Trezza|first3=Ricardo|last4=Robison|first4=Clarence W.|date=2016|title=Evapotranspiration between satellite overpasses: methodology and case study in agricultural dominant semi-arid areas|journal=Meteorological Applications|language=en|volume=23|issue=4|pages=714–730|bibcode=2016MeApp..23..714D|doi=10.1002/met.1596|issn=1469-8080}}</ref> is a [[Computer model|Computer]] based [[Evapotranspiration]] (ET) model, which computes water use, primarily in [[agriculture]] [[landscape]] using [[remote sensing]] based information. It was developed by Ramesh Dhungel<ref name="arspub1" /><ref name="arspub2" /><ref name="arspub3" />'''<ref name=":1" /><ref name=":10" />[https://howwerespond.aaas.org/community-spotlight/kansas-farmers-minimize-water-use-as-the-southern-great-plains-become-more-arid/]''' and water resources group at [[University of Idaho]], Kimberly R & E Center between 2010 and 2014 and continuously improved (2017-2019). <ref name=":9">{{Cite web |url=https://scisoc.confex.com/scisoc/2018am/meetingapp.cgi/Paper/113852 |title=Next Generation Landscape Evapotranspiration Tools: Is It Feasible?. |website=ASA, CSSA, and SSSA - ASA, CSSA, and CSA International Annual Meeting |access-date=2019-05-11}}</ref><ref name=":10">{{Cite web |url=https://www.openaccessgovernment.org/water-for-plant-growth-the-foundation-of-the-global-food-supply-and-ecosystem-services/55166/ |title=Water for plant growth: The foundation of the global food supply and ecosystem services |last=Eccleston |first=Sally |date=2018-11-30 |website=Open Access Government |language=en-GB |access-date=2019-09-22}}</ref> It is used primarily for the analysis of [[Evapotranspiration|ET]] at point and regional scales, a frequently utilized method to estimate water use in [[agriculture]] and non-agriculture landscape.<ref name=":4">{{Cite journal |last=Zhang |first=Ying |last2=Zhang |first2=Ling |last3=Hou |first3=Jinliang |last4=Gu |first4=Juan |last5=Huang |first5=Chunlin |date=2017-09-21 |title=Development of an Evapotranspiration Data Assimilation Technique for Streamflow Estimates: A Case Study in a Semi-Arid Region |journal=Sustainability |language=en |volume=9 |issue=10 |pages=1658 |doi=10.3390/su9101658 |issn=2071-1050}}</ref><ref name=":0">{{Cite journal |last=Bhattarai |first=Nishan |last2=Wagle |first2=Pradeep |last3=Gowda |first3=Prasanna H. |last4=Kakani |first4=Vijaya G. |date=2017 |title=Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions |journal=ISPRS Journal of Photogrammetry and Remote Sensing |language=en |volume=133 |pages=128–141 |bibcode=2017JPRS..133..128B |doi=10.1016/j.isprsjprs.2017.10.010}}</ref><ref name=":2">{{Cite web |url=https://ogallala.tamu.edu/news/ |title=Sustaining economic activity from the Ogallala Aquifer through new water management technologies |date=2018-11-30 |access-date=11 January 2019}}</ref><ref name=":5">{{Cite journal |last=Akhtar |first=Fazlullah |last2=Awan |first2=Usman |last3=Tischbein |first3=Bernhard |last4=Liaqat |first4=Umar |date=2018-06-18 |title=Assessment of Irrigation Performance in Large River Basins under Data Scarce Environment—A Case of Kabul River Basin, Afghanistan |journal=Remote Sensing |language=en |volume=10 |issue=6 |pages=972 |bibcode=2018RemS...10..972A |doi=10.3390/rs10060972 |issn=2072-4292}}</ref> The estimation of water use by vegetation is crucial for conserving water, managing Irrigation, and acquiring [[water right]] information.<ref name="arspub3" /> Farmers need these information for accurately applying water to crops for optimum yield.
[[File:LE H.png|alt=|thumb|485x485px|Schematic of BAITSSS latent heat flux (LE) and sensible heat flux (H)]]
[[File:LE H.png|alt=|thumb|485x485px|Schematic of BAITSSS latent heat flux (LE) and sensible heat flux (H)]]


Revision as of 16:07, 5 November 2019

  • Comment: This needs a history section on how it was developed and rolled out. AngusWOOF (bark • sniff) 16:57, 18 October 2019 (UTC)
  • Comment: I reorganized the section, removed excess links and capitalization, and adjusted grammar. It might now be possible to evaluate encyclopedic notability . DGG ( talk ) 08:38, 18 October 2019 (UTC)
  • Comment: This is still confusing what the article is about? Is it an organization? A computer model? A program? AngusWOOF (bark • sniff) 16:57, 13 October 2019 (UTC)
  • Comment: Please rewrite the lead paragaph and sentence so that is explains what this is. MOS:LEAD AngusWOOF (bark • sniff) 16:39, 18 September 2019 (UTC)
  • Comment: This still lacks context as to what this is. How was it developed? When was it used? Is it used extensively outside of the immediate research group? AngusWOOF (bark • sniff) 17:04, 16 September 2019 (UTC)
  • Comment: All the refs are either own refs as the developers of the model or academic papers from researchers who have used the model but nothing that discusses and analyses the benefits of the model in a reliable and independent way. I also have concerns that the diagram is a copyright violation  Velella  Velella Talk   00:53, 20 January 2019 (UTC)


Updated and added report from American Association for the Advancement of Science (AAAS) report; "How we respond" for Climate Change which mentioned about evapotranspiraiton model.

https://howwerespond.aaas.org/community-spotlight/kansas-farmers-minimize-water-use-as-the-southern-great-plains-become-more-arid/?fbclid=IwAR1_cUEhilQaiJjxDKCzo8FK7gwMubSreyl2zegm0Xbnnf8VcP4eI7ctGtI

Added he United States Senate Committee on Agriculture, Nutrition and Forestry report on Climate Change studies.

Added Urban Climate News

Added Upper Republican Regional Advisory Committee Meeting

Updated the lead paragraph to better understand this, please let me know.

Nicely edited, thanks.

Added small section of history as per found in web.

______________

BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution)[1][2][3][4] is a Computer based Evapotranspiration (ET) model, which computes water use, primarily in agriculture landscape using remote sensing based information. It was developed by Ramesh Dhungel[1][2][3][4][5][1] and water resources group at University of Idaho, Kimberly R & E Center between 2010 and 2014 and continuously improved (2017-2019). [6][5] It is used primarily for the analysis of ET at point and regional scales, a frequently utilized method to estimate water use in agriculture and non-agriculture landscape.[7][8][9][10] The estimation of water use by vegetation is crucial for conserving water, managing Irrigation, and acquiring water right information.[3] Farmers need these information for accurately applying water to crops for optimum yield.

Schematic of BAITSSS latent heat flux (LE) and sensible heat flux (H)

History

University of Idaho's Kimberly Research and Extension Center Water Resources Group is prominent on ET work in irrigated agriculture and natural systems (see METRIC, Standardization of Reference Evapotranspiration[11]). BAITSSS was developed during the Ph.D. [4][12][13], which was a part of project "Producing and integrating time series of gridded evapotranspiration for irrigation management, hydrology and remote sensing applications". [14] The conceptual model was first published in Meteorological Applications journal [4][15] in 2016 as a framework to interpolate ET between the satellite overpass when thermal based surface temperature is unavailable. The detailed independent model was evaluated against weighing lysimeters at USDA-ARS, Conservation and Production Research Laboratory, Bushland, TX. [1][2] Majority of remote sensing based instantaneous ET models use evaporative fraction (EF) or reference ET fraction (ETrF), similar to crop coefficients, for computing seasonal values, these ET models lack the soil water balance and Irrigation components in surface energy balance. [1][3][4] BAITSSS was developed to fill these gaps in remote sensing based models and to serve as a digital crop water tracker simulating high temporal and spatial resolution as next-generation ET tool. [3][4][16][6] The automated BAITSSS model is scripted in Python programming language, together with GDAL and Numpy libraries, that can compute hourly landscape ET and irrigation at Landsat 30 m resolution throughout the USA. [2][3]

Approach

Surface energy balance is one of the commonly utilized approaches to quantify evapotranspiration (latent heat flux in terms of flux), where weather variables and vegetation Indices are the drivers of this process. BAITSSS is an integrated two-source surface energy balance and two-layered soil water balance biophysical land surface model. BAITSSS computes latent heat flux (LE) and sensible heat flux (H) in the energy balance using aerodynamic methods or flux-gradient relationship equations[17] with stability functions associated with Monin–Obukhov similarity theory. Variable canopy conductance in terms of canopy resistance (rsc), based on the Jarvis-type algorithm[1][2] is used to compute transpiration. Evaporation in BAITSSS is computed based on soil resistance (rss) and soil water content in soil surface layer (upper 100 millimeters of soil water balance). BAITSSS simulates irrigation in agricultural landscapes[18][19] by mimicking a tipping-bucket approach (applied to surface or sub-surface layer), using Management Allowed Depletion (MAD), and soil water content regimes at rooting depth (lower 100-2000 millimeters of soil layer). Unlike other instantaneous remote sensing based ET models that utilize thermal infrared surface temperature, BAITSSS iteratively solves surface temperature at the soil surface (Ts) and canopy level (Tc) using continuous weather variables and surface roughness defined by vegetation Indices for each time step, which is unique among the ET models.[4][20] Typical Jarvis type-equation of rsc adopted in BAITSSS is shown below, Rcmin is the minimum value of rsc, LAI is leaf area index, fc is fraction of canopy cover, weighting functions representing plant response to solar radiation (F1), air temperature (F2), vapor pressure deficit (F3), and soil moisture (F4) each varying between 0 to 1[1].

The aerodynamic or flux-gradient equations of LE and H for canopy portion in BAITSSS are shown below. is saturation vapor pressure at the canopy, ea is ambient vapor pressure, rac is bulk boundary layer resistance of vegetative elements in the canopy, rah is aerodynamic resistance between d + zom and measurement height of wind speed[1].


Data Requirements

ET models, in general, need information about vegetation (physical property and vegetation indices) and environment condition (weather data) to compute water use. BAITSSS explicitly integrates energy and water balances with satellite imagery of the land surface cover with hourly (or sub-hourly) temporal resolution and 30-meter spatial resolution, at landscape and multi-season scales, using public-available input parameters representing soil, canopy and weather conditions. Primary weather data requirements in BAITSSS are solar irradiance (Rs↓), wind speed (uz), air temperature (Ta), relative humidity (RH) or specific humidity (qa), and precipitation (P). Vegetation indices requirements in BAITSSS are leaf area index (LAI) and fractional canopy cover (fc), estimated from normalized difference vegetation index (NDVI). Automated BAITSSS [2][3] can compute ET throughout USA using National Oceanic and Atmospheric Administration (NOAA) weather data (i.e. hourly NLDAS: North American Land Data Assimilation system at 1/8 degree; ~ 12.5 kilometer), Vegetation indices those acquired by Landsat, and soil information from SSURGO.

Application

BAITSSS has been used throughout USA where some of published reports were to compute landscape ET in southern Idaho (2008),[4] and northern California (2010),[4] calculating corn ET in Bushland, Texas (2016)[1][2], landscape ET and water right in northwest Kansas (2013-2017).[3][13][21][22] BAITSSS has been widely discussed among the peers around the world, for instance,[23][24][25][26][27][28][29][30] for various water management issues in agriculture sector. For instance, Bhattarai et al., 2017[8] discussed the implications of improved soil water balance model of BAITSSS to enhance the performance of the remote sensing based surface energy balance models under dry conditions.

In a recent published report (September, 2019), American Association for the Advancement of Science (AAAS) had discussed the development and use of BAITSSS for effective use of water in Sheridan County, Kansas; "How we respond[31] to climate change.[13] AAAS discussed the effort of stakeholders of Sheridan County, Kansas to prolong the life of Ogallala Aquifer by minimizing water use where this aquifer is depleting rapidly due to extensive agricultural practices.[13] The United States Senate Committee on Agriculture, Nutrition and Forestry list of Climate Change studies [32] (01/2017 - 08/2019) and Urban Climate News (September, 2019)[33] had incorporated ET study from BAITSSS[1] in their report. Upper Republican Regional Advisory Committee Meeting of Kansas (June 2019) discussed possible benefit and utilization of BAITSSS for managing water resources in Sheridan County, Kansas[21].

See also

References

  1. ^ a b c d e f g h i Dhungel, Ramesh; Aiken, Robert; Colazzi, Paul D.; Lin, Xiaomao; O'Brien, Dan; Baumhardt, Roland Louis; Brauer, David; Marek, Gary W.; Evett, Steve (June 3, 2019). "Evaluation of the uncalibrated energy balance model (BAITSSS)for estimating evapotranspiration in a semiarid, advective climate". Hydrological Processes. 33 (15). doi:10.1002/hyp.13458. Retrieved 2019-04-09 – via Agricultural Research Service.
  2. ^ a b c d e f g Dhungel, Ramesh; Aiken, Robert; Colaizzi, Paul; Lin, Xiaomao; Baumhardt, Roland; Brauer, David; Marek, Gary; Evett, Steven; O'Brien, Dan (April 11, 2019). "Increased bias in evapotranspiration modeling due to weather and vegetation indices data sources". Agronomy Journal. 111 (3): 1407–1424. doi:10.2134/agronj2018.10.0636. Retrieved 2019-04-09 – via Agricultural Research Service.
  3. ^ a b c d e f g h Dhungel, Ramesh; Aiken, Robert; Lin, Xiaomao; Kenyon, Shannon; Colaizzi, Paul D.; Luhman, Ray; Baumhardt, R. Louis; O’Brien, Dan; Kutikoff, Seth; Brauer, David K. (2020-01-20). "Restricted water allocations: Landscape-scale energy balance simulations and adjustments in agricultural water applications". Agricultural Water Management. 227: 105854. doi:10.1016/j.agwat.2019.105854. Retrieved 2019-10-23 – via Agricultural Research Service. {{cite journal}}: Cite has empty unknown parameter: |1= (help)
  4. ^ a b c d e f g h i Dhungel, Ramesh; Allen, Richard G.; Trezza, Ricardo; Robison, Clarence W. (2016). "Evapotranspiration between satellite overpasses: methodology and case study in agricultural dominant semi-arid areas". Meteorological Applications. 23 (4): 714–730. Bibcode:2016MeApp..23..714D. doi:10.1002/met.1596. ISSN 1469-8080.
  5. ^ a b Eccleston, Sally (2018-11-30). "Water for plant growth: The foundation of the global food supply and ecosystem services". Open Access Government. Retrieved 2019-09-22.
  6. ^ a b "Next Generation Landscape Evapotranspiration Tools: Is It Feasible?". ASA, CSSA, and SSSA - ASA, CSSA, and CSA International Annual Meeting. Retrieved 2019-05-11.
  7. ^ Zhang, Ying; Zhang, Ling; Hou, Jinliang; Gu, Juan; Huang, Chunlin (2017-09-21). "Development of an Evapotranspiration Data Assimilation Technique for Streamflow Estimates: A Case Study in a Semi-Arid Region". Sustainability. 9 (10): 1658. doi:10.3390/su9101658. ISSN 2071-1050.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  8. ^ a b Bhattarai, Nishan; Wagle, Pradeep; Gowda, Prasanna H.; Kakani, Vijaya G. (2017). "Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions". ISPRS Journal of Photogrammetry and Remote Sensing. 133: 128–141. Bibcode:2017JPRS..133..128B. doi:10.1016/j.isprsjprs.2017.10.010.
  9. ^ "Sustaining economic activity from the Ogallala Aquifer through new water management technologies". 2018-11-30. Retrieved 11 January 2019.
  10. ^ Akhtar, Fazlullah; Awan, Usman; Tischbein, Bernhard; Liaqat, Umar (2018-06-18). "Assessment of Irrigation Performance in Large River Basins under Data Scarce Environment—A Case of Kabul River Basin, Afghanistan". Remote Sensing. 10 (6): 972. Bibcode:2018RemS...10..972A. doi:10.3390/rs10060972. ISSN 2072-4292.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  11. ^ "Standardization of Reference Evapotranspiration". www.uidaho.edu. Retrieved 2019-11-01.
  12. ^ "TIME INTEGRATION OF EVAPOTRANSPIRATION USING A TWO SOURCE SURFACE ENERGY BALANCE MODEL USING NARR REANALYSIS WEATHER DATA AND SATELLITE BASED METRIC DATA". digital.lib.uidaho.edu. Retrieved 2019-10-19.{{cite web}}: CS1 maint: url-status (link)
  13. ^ a b c d "Kansas Farmers Minimize Water Use as the Southern Great Plains Become More Arid". How We Respond. Retrieved 2019-09-20.
  14. ^ "Producing and Integrating Time Series of Gridded Evapotranspiration for Irrigation Management, Hydrology and Remote Sensing Applications - UNIV OF IDAHO". reeis.usda.gov. Retrieved 2019-11-02.
  15. ^ "DHS BULLETIN" (PDF).{{cite web}}: CS1 maint: url-status (link)
  16. ^ "'It's all about water' Global Food Systems meeting". www.k-state.edu. Retrieved 2019-10-21.
  17. ^ Pagán, Brianna; Maes, Wouter; Gentine, Pierre; Martens, Brecht; Miralles, Diego (2019-02-18). "Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates". Remote Sensing. 11 (4): 413. Bibcode:2019RemS...11..413P. doi:10.3390/rs11040413. ISSN 2072-4292.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  18. ^ He, Ruyan; Jin, Yufang; Kandelous, Maziar; Zaccaria, Daniele; Sanden, Blake; Snyder, Richard; Jiang, Jinbao; Hopmans, Jan (2017-05-05). "Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations". Remote Sensing. 9 (5): 436. Bibcode:2017RemS....9..436H. doi:10.3390/rs9050436. ISSN 2072-4292.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  19. ^ Pan, Xin; Liu, Yuanbo; Gan, Guojing; Fan, Xingwang; Yang, Yingbao (2017). "Estimation of Evapotranspiration Using a Nonparametric Approach Under All Sky: Accuracy Evaluation and Error Analysis". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10 (6): 2528–2539. Bibcode:2017IJSTA..10.2528P. doi:10.1109/JSTARS.2017.2707586. ISSN 1939-1404.
  20. ^ Dhungel, Ramesh; Allen, Richard G.; Trezza, Ricardo (2016-06-09). "Improving iterative surface energy balance convergence for remote sensing based flux calculation". Journal of Applied Remote Sensing. 10 (2): 026033. doi:10.1117/1.JRS.10.026033. ISSN 1931-3195.
  21. ^ a b "Upper Republican Regional Advisory Committee" (PDF). kwo.ks.gov. Retrieved 2019-09-21.{{cite web}}: CS1 maint: url-status (link)
  22. ^ "Effective agricultural water management strategy with biophysical evapotranspiration algorithm (BAITSSS)".{{cite web}}: CS1 maint: url-status (link)
  23. ^ Jin, Yufang; He, Ruyan; Marino, Giulia; Whiting, Michael; Kent, Eric; Sanden, Blake L.; Culumber, Mae; Ferguson, Louise; Little, Cayle; Grattan, Stephen; Paw U, Kyaw Tha (2018-11-15). "Spatially variable evapotranspiration over salt affected pistachio orchards analyzed with satellite remote sensing estimates". Agricultural and Forest Meteorology. 262: 178–191. doi:10.1016/j.agrformet.2018.07.004. ISSN 0168-1923.
  24. ^ Majozi, Nobuhle; Mannaerts, Chris; Ramoelo, Abel; Mathieu, Renaud; Mudau, Azwitamisi; Verhoef, Wouter (2017-03-24). "An Intercomparison of Satellite-Based Daily Evapotranspiration Estimates under Different Eco-Climatic Regions in South Africa". Remote Sensing. 9 (4): 307. doi:10.3390/rs9040307. ISSN 2072-4292.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  25. ^ Pagán, Brianna; Maes, Wouter; Gentine, Pierre; Martens, Brecht; Miralles, Diego (2019-02-18). "Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates". Remote Sensing. 11 (4): 413. doi:10.3390/rs11040413. ISSN 2072-4292.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  26. ^ Khand, Kul; Taghvaeian, Saleh; Gowda, Prasanna; Paul, George (2019-03-02). "A Modeling Framework for Deriving Daily Time Series of Evapotranspiration Maps Using a Surface Energy Balance Model". Remote Sensing. 11 (5): 508. doi:10.3390/rs11050508. ISSN 2072-4292.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  27. ^ Numata, Izaya; Khand, Kul; Kjaersgaard, Jeppe; Cochrane, Mark; Silva, Sonaira (2017-01-06). "Evaluation of Landsat-Based METRIC Modeling to Provide High-Spatial Resolution Evapotranspiration Estimates for Amazonian Forests". Remote Sensing. 9 (1): 46. doi:10.3390/rs9010046. ISSN 2072-4292.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  28. ^ He, Ruyan; Jin, Yufang; Kandelous, Maziar; Zaccaria, Daniele; Sanden, Blake; Snyder, Richard; Jiang, Jinbao; Hopmans, Jan (2017-05-05). "Evapotranspiration Estimate over an Almond Orchard Using Landsat Satellite Observations". Remote Sensing. 9 (5): 436. doi:10.3390/rs9050436. ISSN 2072-4292.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  29. ^ Khand, Kul Bikram (2019-05-01). "Assessment and Development of Remotely Sensed Evapotranspiration Modeling Approaches". {{cite journal}}: Cite journal requires |journal= (help)
  30. ^ Trezza, Ricardo; Allen, Richard G.; Kilic, Ayse; Tasumi, Ian Ratcliffe and Masahiro (2018-11-20). "Influence of Landsat Revisit Frequency on Time-Integration of Evapotranspiration for Agricultural Water Management". Advanced Evapotranspiration Methods and Applications. doi:10.5772/intechopen.80946.
  31. ^ "How We Respond: Stories of Community Response to Climate Change". How We Respond. Retrieved 2019-09-18.
  32. ^ "Peer-Reviewed Research on Climate Change by USDA Authors January 2017-August 2019" (PDF).{{cite web}}: CS1 maint: url-status (link)
  33. ^ "Urban Climate News" (PDF).{{cite web}}: CS1 maint: url-status (link)

Category:Remote sensing Category:Hydrology models Category:Water resources management Category:Irrigation Category:Computer-aided engineering software Category:Numerical climate and weather models

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