Validating FAO AquaCrop using Landsat images and regional crop information

Agricultural Water Management

Volumn 149, Issue , 2015, Pages 143-155

  Kim, Daeha ^a   Kaluarachchi, Jagath ^b  

^a UTAH STATE UNIVERSITY   (United States)

^b Utah State University   (United States)

Author keywords

AquaCrop validation; Crop Observations; Remote sensing model; Salinity stress

Indexed keywords

CALIBRATION; DECISION MAKING; REMOTE SENSING; WATER MANAGEMENT;

ABOVEGROUND BIOMASS; AGRICULTURAL WATER MANAGEMENT; AQUACROP VALIDATION; GROUND MEASUREMENTS; REGIONAL CROPS; REMOTE SENSING MODELS; SALINITY STRESS; STRESS CONDITION;

CROPS;

ABOVEGROUND BIOMASS; AGRICULTURAL MODELING; LANDSAT; MAIZE; SALINITY; SATELLITE IMAGERY; WATER MANAGEMENT;

EID: 84911935405     PISSN: 03783774     EISSN: 18732283     Source Type: Journal    
DOI: 10.1016/j.agwat.2014.10.013     Document Type: Article

References (58)

1. Abrha B., Delbecque N., Raes D., Tsegay A., Todorovic M., Heng L., Vanuytrecht E., Geerts S., García-Vila M., Deckers S. Sowing strategies for barley based on modelled yield response to water with AquaCrop. Exp. Agric. 2012, 48:252-271.
2. Ainsworth E.A., Ort D.R. How do we improve crop production in a warming world?. Plant Physiol. 2010, 154:526-530.
3. Allen R.G., Pereira L.S., Raes D. Crop evapotranspiration (guidelines for computing crop water requirements). FAO Irrigation and Drainage Paper No. 56 1998, FAO, Water Resources, Development and Management Service, Rome, Italy.
4. Andarzian B., Aval M.B., Steduto P., Mazraeh H., Barati M.E., Barati M.A., Rahnama A. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agric. Water Manage. 2011, 100:1-8.
5. Araya A., Habtu S., Hadgu K.M., Kebede A., Dejene T. Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley. Agric. Water Manage. 2010, 97:1838-1846.
6. Bridger G.M., Klinck H.R., Smith D.L. Timing and rate of ethephon application to two-row and six-row spring barley. Agron. J. 1995, 87:1198-1206.
7. Brown H., Moot D.J., Fletcher A.L., Jamieson P.D. A framework for quantifying water extraction and water stress response of perennial lucerne. Crop Pasture Sci. 2009, 60:785-794.
8. Cai X.M., Rosegrant M.W., Ringler C. Physical and economic efficiency of water use in the river basin: implications for efficient water management. Water Resour. Res. 2003, 39:1013. 10.1029/2001WR000748.
9. Calera A., González-Piqueras J., Melia J. Monitoring barley and corn growth from remote sensing data at field scale. Int. J. Remote Sens. 2004, 25:97-109.
10. Calera A., Martinez C., Melia J. A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. Int. J. Remote Sens. 2001, 22:3357-3362.
11. Carlson T.N., Ripley D.A. On relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens. Environ. 1997, 62:241-252.
12. Chander G., Markham B.L., Helder D.L. Summary of current radiometric coefficient for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens. Environ. 2009, 113:893-903.
13. Doorenbos J., Kassam A.H. Yield response to water. Irrigation and Drainage Paper, No. 33 1979, FAO, Rome, Italy.
14. Farnham D.E. Row spacing, plant density, and hybrid effects on corn grain yield and moisture. Agron. J. 2001, 93:1049-1053.
15. Food and Agriculture Organization FAOSTAT, Production-Crops 2013, Food and Agriculture Organization, available at http://faostat3.fao.org/faostat-gateway/go/to/download/Q/QC/E (accessed on September-10/2013).
16. García-Vila M., Fereres E. Combining the simulation crop model AquaCrop with an economic model for the optimization of irrigation management at farm level. Eur. J. Agron. 2012, 36:21-31.
17. García-Vila M., Fereres E., Mateos L., Orgaz F., Steduto P. Deficit irrigation optimization of cotton with AquaCrop. Agron. J. 2009, 101:477-487.
18. Geerts S., Raes D. Deficit irrigation as an on-farm strategy to maximize crop water productivity in dry areas. Agric. Water Manage. 2009, 96:1275-1284.
19. Geerts S., Raes D., Garcia M. Using AquaCrop to derive deficit irrigation schedules. Agric. Water Manage. 2010, 98:213-216.
20. Griggs T.C., Stringer W.C. Prediction of alfalfa herbage mass using sward height, ground cover, and disk technique. Agron. J. 1988, 80:204-208.
21. Gutman G., Ignatov A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens. 1998, 19:1533-1543.
22. Harmoney K.R., Moore K.J., George J.R., Brummer E.C., Russell J.R. Determination of pasture biomass using four indirect methods. Agron. J. 1997, 89:665-672.
23. Heng L.K., Hsiao T., Evett S., Howell T., Steduto P. Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agron. J. 2009, 101:488-498.
24. Hsiao T.C., Heng L., Steduto P. AquaCrop-the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agron. J. 2009, 101:448-459.
25. Huete A.R., Didan K., Miura T., Rodriguez E.P., Gao X., Ferreira G. Overview of the radiometric and biophysical performance of the MODIS Vegetation Indices. Remote Sens. Environ. 2002, 83:195-213.
26. Hunt E.R., Rock B.N., Nobel P.S. Measurement of leaf relative water content by infrared reflectance. Remote Sens. Environ. 1987, 22:429-435.
27. Iqbal M.A., Shen Y., Stricevic R., Pei H., Sun H., Amiri E., Penas A., del Rio S. Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation. Agric. Water Manage. 2014, 135:61-72.
28. Johnson L.F., Trout T.J. Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California's San Joaquin Valley. Remote Sens. 2012, 4:439-455.
29. Jones J.W., Hoogneboom G., Porter C.H., Boote K.J., Batchelor W.D., Hunt L.A., Wilkens P.W., Singh U., Gijsman A.J., Ritchie J.T. The DSSAT cropping system model. Eur. J. Agron. 2003, 18:235-265.
30. Kiniry J.R., Williams J.R., Vanderlip R.L., Atwood J.D., Reicosky D.C., Mulliken J., Cox W.J., Mascagni H.J., Hollinger S.E., Wiebold W.J. Evaluation of two maize models for nine U.S. locations. Agron. J. 1997, 89:421-426.
31. Liu J., Pattey E., Miller J.R., McNairn H., Smith A., Hu B. Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model. Remote Sens. Environ. 2010, 114:1167-1177.
32. Lobell D.B., Hicke J.A., Asner G.P., Field C.B., Tucker C.J., Los S.O. Satellite estimates of productivity and light use efficiency in United States agriculture, 1982-98. Glob. Change Biol. 2002, 8:722-735.
33. Lopez-Urrea R., Montoro A., Gonzalez-Piqueras J., Lopez-Fuster P., Fereres E. Water use of spring wheat to raise water productivity. Agric. Water Manage. 2009, 96:1305-1310.
34. McCown R.L., Hammer G.L., Hargreaves J.N.G., Holzworth D.P., Freebarin D.M. APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agric. Syst. 1996, 50:255-271.
35. McVay K., Burrows M., Jones C., Wanner K., Menalled F. Montana Barley Production Guide, EB0186 2009, Montana State University, Southern Agricultural Center, Huntley, MT.
36. Mabane V.J., Day R.L., Hamlett J.M., Watson J.E., Roth G.W. Validating the FAO AquaCrop model for rainfed maize in Pennsylvania. Agron. J. 2013, 105:419-427.
37. Monteith J.L. Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 1972, 9:747-766.
38. Peltonen-Sainio P., Muurinen S., Rajala A., Jauhiainen L. Variation in harvest index of modern spring barley, oat, and wheat cultivars adapted to northern growing conditions. J. Agric. Sci. 2008, 146:35-47.
39. Prince S.D., Haskett J., Steininger M., Strand H., Wright R. Net primary production of U.S. Midwest croplands from agricultural harvest yield data. Ecol. Appl. 2001, 11:1194-1205.
40. Raes D. A Summary Simulation Model of The Water Budget of a Cropped Soil 1982, K. U. Leuven Univ., Leuven, Belgium, (Ph.D. diss. Dissertationes de Agricultura no. 122).
41. Raes D., Steduto P., Hsiao T.C., Fereres E. AquaCrop-the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron. J. 2009, 101:438-447.
42. Raes D., Steduto P., Hsiao T.C., Fereres E. AquaCrop Reference Manual 2011, FAO, Land and Water Division, Rome, Italy, Available at htttp://www.fao.org/nr/water/docs/aquacrop.html (accessed on Mar-07/2013).
43. Rankin M. Alfalfa Seeding Rates: How Much is Too Much? 2007, University of Wisconsin-Extension, Wisconsin, Available at http://www.uwex.edu/ces/crops/AlfSeedingRate.htm (accessed on July-3/2013).
44. Ritchie J.T., Godwin D.C., Otter-Nacke S. CERES-wheat. A simulation model of wheat growth and development 1985, Taxas A&M Univ. Press, College Station, TX.
45. Roujean J.L., Breon F.-M. Estimating PAR absorbed by vegetation from bi-directional reflectance measurements. Remote Sens. Environ. 1995, 51:375-384.
46. Ruimy A., Dedieu G., Saugier B. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J. Geophys. Res. D: Atmos. 1994, 99:5263-5284.
47. Saeed I.A.M., El-Nadi A.H. Irrigation effects on the growth, yield, and water use efficiency of alfalfa. Irrig. Sci. 1997, 17:63-68.
48. State of Utah Natural Resources Utah State Water Plan, Sevier River Basin Chapter 10 1999, Agricultural Water, Division of Water Resources, Salt Lake City, UT, Available at http://www.water.utah.gov/planning/waterplans.asp (accessed on Jan-10/2013).
49. Steduto P., Hsiao T.C., Fereres El Raes D. Crop Yield Response to Water 2012, Food and Agriculture Organization of the United Nations, Rome, Italy.
50. Steduto P., Hsiao T.C., Raes D., Fereres E. AquaCrop-the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J. 2009, 101:426-437.
51. Trout T.J., Johnson L.F., Gartung J. Remote sensing of canopy cover in horticultural crops. HortScience 2008, 43:333-337.
52. United States Department of Agriculture CropScape-Cropland Data Layer 2013, United States Department of Agriculture, Available at http://nassgeodata.gmu.edu/CropScape/ (accessed on June-26/2013).
53. United States Department of Agriculture National Agriculture Statistical Service, 2012 Utah Annual Statistics Bulletin 2013, United States Department of Agriculture, Available at http://www.nass.usda.gov/Statistics_by_State/Utah/Publications/Annual_Statistical_Bulletin/AB12.asp (accessed on June-26/2013).
54. United States Department of Agriculture National Agriculture Statistical Service, Utah Crop Progress and Condition 2013, United States Department of Agriculture, Available at http://www.nass.usda.gov/Statistics_by_State/Utah/Publications/Crop_Progress_&_Condition/index.asp (accessed on June-26/2013).
55. United States Department of Agriculture Natural Resources Conservation Service, Web Soil Survey 2013, United States Department of Agriculture, Available at http://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx (accessed on July-3/2013).
56. Utah Automated Geographic Reference Center Water Related Land Use 2013, Utah Automated Geographic Reference Center, Available at http://gis.utah.gov/data/water-data-services/ (accessed on June-26/2013).
57. Vaux H.J., Pruitt W.O. Crop-water production function. Adv. Irrig. 1983, 2:61-97.
58. Wilde T., Curtis K., Lewis C. Millard County Crop Production Cost and Returns, 2012 2012, Available at https://apecextension.usu.edu/files/uploads/Agribusiness%20and%20Food/Budgets/Crops/2011/MillardCountyCrops2011.pdf (assessed on June-26/2013).