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Special Issue "Advances in Remote Sensing of Crop Water Use Estimation"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 January 2013)

Special Issue Editor

Guest Editor
Dr. Jeppe H. Kjaersgaard

South Dakota Water Resources Institute, Dept. of Agric. and Biosystems Engineering, South Dakota State University, Box 2120, Brookings, SD 57007, USA
Website | E-Mail
Fax: +1605 688 6764
Interests: monitoring and modeling of evapotranspiration and the surface energy balance using remote sensing technologies and ground-based monitoring equipment for agricultural and environmental water management; agricultural water use and water quality; tile drainage and its impact on water quality

Special Issue Information

Dear Colleagues,

In recent years remote sensing-based estimates of crop water use have become an invaluable and powerful tool in research, water policy making, water rights regulation, hydrological modeling and water planning and management throughout the world. This special issue on "Advances in Remote Sensing of Crop Water Use Estimation" will draw from ongoing advancements and novel developments of methodologies to further the use and quality of crop water use mapping including, for example, fusion of multi-platform data having different temporal and spatial scale, use of hyperspectral imagery to characterize e.g. vegetation or surface energy balance variables, methodologies for handling common challenges when producing maps of water consumption including cloud masking and filling, interpolations between satellite overpass dates and impacts of terrain, slope, crop type and scale.
I also invite papers describing new applications or implementations of crop water use maps, for example in water planning or decision making, hydrological models, agricultural and environmental water use estimates, life cycle analysis applications and similar.

Dr. Jeppe H. Kjaersgaard
Guest Editor

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs).

Keywords

  • evapotranspiration
  • crop water use
  • crop vigor
  • remote sensing
  • water management
  • water decision support
  • hydrological modeling
  • water policy and management

Published Papers (7 papers)

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Research

Open AccessArticle Estimation of Actual Evapotranspiration along the Middle Rio Grande of New Mexico Using MODIS and Landsat Imagery with the METRIC Model
Remote Sens. 2013, 5(10), 5397-5423; doi:10.3390/rs5105397
Received: 22 July 2013 / Revised: 16 October 2013 / Accepted: 17 October 2013 / Published: 23 October 2013
Cited by 8 | PDF Full-text (3872 KB) | HTML Full-text | XML Full-text
Abstract
Estimation of actual evapotranspiration (ET) for the Middle Rio Grande valley in central New Mexico via the METRIC surface energy balance model using MODIS and Landsat imagery is described. MODIS images are a useful resource for estimating ET at large scales when high
[...] Read more.
Estimation of actual evapotranspiration (ET) for the Middle Rio Grande valley in central New Mexico via the METRIC surface energy balance model using MODIS and Landsat imagery is described. MODIS images are a useful resource for estimating ET at large scales when high spatial resolution is not required. One advantage of MODIS satellites is that images having a view angle < ~15° are potentially available about every four to five days. The main challenge of applying METRIC using MODIS is the selection of the two calibration conditions due to the low spatial resolution of MODIS. A calibration procedure specific to MODIS is described that utilizes the higher vegetation index areas of the image along with a consistently low ET location to develop the estimation function for sensible heat flux. This paper compares ET images for the Rio Grande region as produced by both MODIS and by Landsat. Application of METRIC energy balance processes along the Middle Rio Grande using MODIS imagery indicates that one can successfully produce monthly and annual ET estimates that are similar in value to those obtained using Landsat imagery if a cross-calibration scheme is considered. However, spatial fidelity is degraded. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
Open AccessArticle Estimating Riparian and Agricultural Actual Evapotranspiration by Reference Evapotranspiration and MODIS Enhanced Vegetation Index
Remote Sens. 2013, 5(8), 3849-3871; doi:10.3390/rs5083849
Received: 26 June 2013 / Revised: 22 July 2013 / Accepted: 22 July 2013 / Published: 5 August 2013
Cited by 13 | PDF Full-text (2035 KB) | HTML Full-text | XML Full-text
Abstract
Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on
[...] Read more.
Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI) − c], where the term (1 − e−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
Open AccessArticle Application of Landsat to Evaluate Effects of Irrigation Forbearance
Remote Sens. 2013, 5(8), 3776-3802; doi:10.3390/rs5083776
Received: 15 June 2013 / Revised: 26 July 2013 / Accepted: 30 July 2013 / Published: 2 August 2013
Cited by 12 | PDF Full-text (4624 KB) | HTML Full-text | XML Full-text
Abstract
Thirty-meter resolution Landsat data were used to evaluate the effects of irrigation management in the Wood River Valley, Upper Klamath Basin, Oregon. In an effort to reduce water use and leave more of the water resource in-stream, 4,674 ha of previously flood irrigated
[...] Read more.
Thirty-meter resolution Landsat data were used to evaluate the effects of irrigation management in the Wood River Valley, Upper Klamath Basin, Oregon. In an effort to reduce water use and leave more of the water resource in-stream, 4,674 ha of previously flood irrigated pasture was managed as dryland pasture. Ground-based measurements over one irrigated and one unirrigated pasture site were used to monitor the difference in evapotranspiration (ET) using the Bowen ratio-energy balance method. These data sets represent point measurements of the response to irrigation, but do not allow for the spatial integration of effects of irrigated versus unirrigated land treatment. Four Landsat scenes of the Wood River Valley during the 2004 growing season were evaluated using reconstructed METRIC algorithms. Comparisons of ET algorithm output with ground-based data for all components of the energy balance, including net radiation, soil heat flux, sensible heat flux and evapotranspiration, were made for the four scenes. The excellent net radiation estimates, along with reasonable estimates of the other components, are demonstrated along with the capability to integrate results to the basin scale. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
Open AccessArticle A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields
Remote Sens. 2013, 5(7), 3331-3356; doi:10.3390/rs5073331
Received: 18 May 2013 / Revised: 28 June 2013 / Accepted: 5 July 2013 / Published: 12 July 2013
Cited by 6 | PDF Full-text (1081 KB) | HTML Full-text | XML Full-text
Abstract
Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would
[...] Read more.
Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI) model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
Figures

Open AccessArticle Estimating Crop Coefficients Using Remote Sensing-Based Vegetation Index
Remote Sens. 2013, 5(4), 1588-1602; doi:10.3390/rs5041588
Received: 11 January 2013 / Revised: 13 March 2013 / Accepted: 15 March 2013 / Published: 26 March 2013
Cited by 11 | PDF Full-text (1141 KB) | HTML Full-text | XML Full-text
Abstract
Crop coefficient (Kc)-based estimation of crop evapotranspiration is one of the most commonly used methods for irrigation water management. However, uncertainties of the generalized dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper
[...] Read more.
Crop coefficient (Kc)-based estimation of crop evapotranspiration is one of the most commonly used methods for irrigation water management. However, uncertainties of the generalized dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper No. 56 can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. Similarities between the crop coefficient curve and a satellite-derived vegetation index showed potential for modeling a crop coefficient as a function of the vegetation index. Therefore, the possibility of directly estimating the crop coefficient from satellite reflectance of a crop was investigated. The Kc data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop coefficients procedure using field data obtained during 2007 from representative US cropping systems in the High Plains from AmeriFlux sites. A simple linear regression model ( ) is developed to establish a general relationship between a normalized difference vegetation index (NDVI) from a moderate resolution satellite data (MODIS) and the crop coefficient (Kc) calculated from the flux data measured for different crops and cropping practices using AmeriFlux towers. There was a strong linear correlation between the NDVI-estimated Kc and the measured Kc with an r2 of 0.91 and 0.90, while the root-mean-square error (RMSE) for Kc in 2006 and 2007 were 0.16 and 0.19, respectively. The procedure for quantifying crop coefficients from NDVI data presented in this paper should be useful in other regions of the globe to understand regional irrigation water consumption. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
Open AccessArticle Estimation of Evapotranspiration from Fields with and without Cover Crops Using Remote Sensing and in situ Methods
Remote Sens. 2012, 4(12), 3796-3812; doi:10.3390/rs4123796
Received: 15 October 2012 / Revised: 20 November 2012 / Accepted: 23 November 2012 / Published: 29 November 2012
Cited by 7 | PDF Full-text (450 KB) | HTML Full-text | XML Full-text
Abstract
Estimation of actual evapotranspiration (ETa) based on remotely sensed imagery is very valuable in agricultural regions where ETa rates can vary greatly from field to field. This research utilizes the image processing model METRIC (Mapping Evapotranspiration at high Resolution with
[...] Read more.
Estimation of actual evapotranspiration (ETa) based on remotely sensed imagery is very valuable in agricultural regions where ETa rates can vary greatly from field to field. This research utilizes the image processing model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) to estimate late season, post-harvest ETa rates from fields with a cover crop planted after a cash crop (in this case, a rye/radish/pea mixture planted after spring wheat). Remotely sensed EToF (unit-less fraction of grass-based reference ET, ETo) maps were generated using Erdas Imagine software for a 260 km2 area in northeastern South Dakota, USA. Meteorological information was obtained from a Bowen-Ratio Energy Balance System (BREBS) located within the image. Nine image dates were used for the growing season, from May through October. Five of those nine were captured during the cover crop season. METRIC was found to successfully differentiate between fields with and without cover crops. In a blind comparison, METRIC compared favorably with the estimated ETa rates found using the BREBS (ETλE), with a difference in total estimated ETa for the cover crop season of 7%. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)
Open AccessArticle Infrared Thermometry to Estimate Crop Water Stress Index and Water Use of Irrigated Maize in Northeastern Colorado
Remote Sens. 2012, 4(11), 3619-3637; doi:10.3390/rs4113619
Received: 26 September 2012 / Revised: 14 November 2012 / Accepted: 15 November 2012 / Published: 20 November 2012
Cited by 14 | PDF Full-text (709 KB) | HTML Full-text | XML Full-text
Abstract
With an increasing demand of fresh water resources in arid/semi-arid parts of the world, researchers and practitioners are relying more than ever on remote sensing techniques for monitoring and evaluating crop water status and for estimating crop water use or crop actual evapotranspiration
[...] Read more.
With an increasing demand of fresh water resources in arid/semi-arid parts of the world, researchers and practitioners are relying more than ever on remote sensing techniques for monitoring and evaluating crop water status and for estimating crop water use or crop actual evapotranspiration (ETa). In this present study, infrared thermometry was used in conjunction with a few weather parameters to develop non-water-stressed and non-transpiring baselines for irrigated maize in a semi-arid region of Colorado in the western USA. A remote sensing-based Crop Water Stress Index (CWSI) was then estimated for four hourly periods each day during 5 August to 2 September 2011 (29 days). The estimated CWSI was smallest during the 10:00–11:00 a.m. and largest during the 12:00–13:00 p.m. hours. Plotting volumetric water content of the topsoil vs. CWSI revealed that there is a high correlation between the two parameters during the analyzed period. CWSI values were also used to estimate maize actual transpiration (Ta). Ta estimates were more influenced by crop biomass rather than irrigation depths alone, mainly due to the fact that the effects of deficit irrigation were largely masked by the significant precipitation during the growing season. During the study period, applying an independent remotely sensed energy balance model showed that maize ETa was 159 mm, 30% larger than CWSI-Ta (122 mm) and 9% smaller than standard-condition maize ET (174 mm). Full article
(This article belongs to the Special Issue Advances in Remote Sensing of Crop Water Use Estimation)

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