Estimation of spatial and temporal water requirements of grain amaranth using satellite, local and virtual weather stations datasets in Uganda

Joseph Kyagulanyi, Isa Kabenge, Noble Banadda, John Muyonga, Peter Mulamba, Nicholas Kiggundu

Abstract


In this study, an integrated approach incorporating Remote Sensing (RS), Geographical Information System (GIS), local meteorological weather stations’ data and NASA’s virtual meteorological stations’ data were used to quantify Grain Amaranth (GA) water requirements in Uganda. Penman-Monieth method within CropWAT8 model and Surface Energy Balance Algorithm for Land (SEBAL) Model was used to quantify the evapotranspiration. Normalized Difference Vegetation Index (NDVI), daily spatial distribution of Evapotranspiration (ET), Land Surface Temperature (LST) and surface albedo were extracted from satellite imagery. The ratio of effective rainfall (Pe) to Potential Evapotranspiration (PET) – (Pe/PET) and time series for NDVI were computed to determine the growth stage of GA in different areas. The GA water demand was the highest in Karamoja sub-region (467.5 mm/season) and the lowest in Tororo (174.1 mm/season). The growing season for GA in most areas of Uganda was from March to December. Estimation of evapotranspiration in Karamoja sub-region with SEBAL model corresponded to the NDVI extracted, especially for highly vegetated areas. CROPWAT indicated that if GA was planted during the late September and early October in Karamoja sub-region, despite the decreasing moisture levels, the crop could have sufficient water supply during emergence to maturity. The ability to utilize low available moisture levels makes GA a potential crop to bridge the gap (due to the elongated drought) for the food production cycle in Karamoja sub-region.
Keywords: Grain Amaranth, water requirement, remote sensing, SEBAL, evapotranspiration, Uganda
DOI: 10.3965/j.ijabe.20160902.1676

Citation: Kyagulanyi J, Kabenge I, Banadda N, Muyonga J, Mulamba P, Kiggundu N. Estimation of spatial and temporal water requirements of grain amaranth using satellite, local and virtual weather stations datasets in Uganda. Int J Agric & Biol Eng, 2016; 9(2): 85-97.

Keywords


grain amaranth, water requirement, remote sensing, SEBAL, evapotranspiration, Uganda

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References


Kabenge I, Irmak S, Meyer G E, Gilley J E, Knezevic S, Arkebauer T J, et al. Evapotranspiration and surface energy balance of a common reed-dominated riparian system in the Platte River Basin, Central Nebraska. Trans. ASABE, 2013; 56: 135–153.

Musiitwa F, Komutunga E T. Agricultural systems. In: Agriculture in Uganda. Fountain Publishers, Kampala, Uganda. 2001.

Mubiru D N. Climate change and adaptation options in Karamoja. A report submitted to FAO and EU. 2010. http://www.disasterriskreduction.net. Available online [2015-01-07]

Muyonga J H, Ugen M, Bisikwa J, Nakimbugwe D, Masinde D, Bbemba J, et al. Promoting production and utilization of grain amaranth for improved nutrition and health in Uganda. Annual progress report, submitted to McKnight Foundation. 2010.

Mulder V L de Bruin S, Schaepman M E, Mayr T R. The use of remote sensing in soil and terrain mapping. Geoderma, 2011; 162: 1–19.

Qi J, Marsett R, Heilman P, Biedenbender S, Moran M S, Goodrich D. RANGES improves satellite-based information and land cover assessments in southwest United States. EOS, Trans. Am. Geophys. Union, 2002; 83(51): 601, 605−606.

Bastiaanssen W G M. SEBAL-based sensible and latent heat fluxes in the irrigated Gedi Basin Turkey. J. Hydrol., 2000; 229 (1-2): 87−100.

Bastiaanssen W G M, Menenti M, Feddes R A, Holtslag A A M. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol, 1998; 198–212.

Allen R G, Morse A, Tasumi M, Trezza R, Bastiaanssen W G M, et al. Evapotranspiration from a satellite based surface Energy Balance for the Snake Plain Aquifer in Idaho Proc. USID Conf. San Luis Obispo, CA, USA, July 2002.

Farah H O, Bastiaanssen W G M. Spatial variations of surface parameters and related evaporation in the lake Naivasha Basin estimated from remote sensing measurements. J. Hydrol., 2001; 15: 1585-1607.

Bastiaanssen W G M, Chandrapala L, Water balance variability across Sri Lanka for assessing agricultural and environmental water use. J. Agric. Wat. Manage, 2003; 58: 171–192.

Bastiaanssen W G M, Ahmad M D, Chemin Y. Satellite surveillance of evaporative depletion across the Indus Basin. Water Resource Res, 2002; 38(12): 1–9.

Bastiaanssen W G M, Noordman E J M, Pelgrum H, Davids G, Thoreson B P, Allen R G. SEBAL model with remotely sensed data to improve water resource management under actual field conditions. J. Irrig. Drain. Eng., 2005; 131(1): 85-93.

UBOS. 2013. Statistical abstracts. http://www.ubos.org/ onlinefiles/uploads/ubos/pdf%20documents/abstracts/Statistical%20Abstract%202013.pdf. Available online [2014-02-27]

NASA. 2014. http://power.larc.nasa.gov/cgibin/cgiwrap/ solar/agro.cgi?email=agroclim@larc.nasa.gov. Available online [2014-02-02]

Clarke D, Smith M, Askari K E. CROPWAT for windows: user guide. Food and Agriculture Organization of the United Nations, 1998.

Allen R G, Pereira L S, Raes D, Smith M. Crop evapotranspiration: guidelines for computing crop water requirements. Irrig. Drain. Pap. No. 56. FAO, Rome, Italy. 1998.

Mwangi D, Introduction to Grain Amaranth. Joy pet Services and Printers Ltd., Nairobi, Kenya. 2003.

Allen R G, Tasumi M, Trezza R. SEBAL (surface energy

balance algorithms for land).advanced training and users manual. Idaho Implementation (version 1.0). 2002.

Hong S Y, Minasny B, Han K H, Kim Y, Lee K. Predicting and mapping soil available water capacity in Korea. Peer J.1, 2013; 71.

FAO. 1996. Agro-ecological zoning guidelines. FAO Soils Bulletin 73.

Chander G, Markham B L, Helder D L. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Rem. Sens. Env., 2009; 113(5): 893−903.

Goslee S C. Analyzing remote sensing data in R: the landsat package. J. Stat. Soft. 2011; 43(4): 1−25.

Rajeshwari A, Mani N D. Estimation of land surface temperature of Dindigul district using Landsat 8 data. Int. J. Res. Eng. Tech., 2014; 2321−7303.

Landsat Project Science Office, 2002.Landsat 7 Science Data User’s Handbook.Goddard Space Flight Center, Greenbelt, MD.

Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R, Fahey D W, et al. Changes in atmospheric constituents and in radiative forcing, in: climate change: the physical science basis. Contribution of working group i to the fourth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007.

NASA, 2013. The Landsat 7 science data user's handbook. http://landsathandbook.gsfc.nasa.gov/. Available online [2014-02-01]

Owiti Z, Zhu W. Spatial distribution of rainfall seasonality over East Africa. J. Geog. Reg. Plan., 2012; 5(15): 409−421.

Phillips J, McIntyre B. ENSO and interannual rainfall variability in Uganda: Implications for agricultural management. Int. J. Climatol., 2000; 20:171–182.

Mubiru D N, Komutunga E, Agona A, Apok A, Ngara T. Characterising agrometeorological climate risks and uncertainties: Crop production in Uganda. S. Afr. J. Sci., 2012; 108(3-4):108–118.

Lafleur P M. Connecting atmosphere and wetland: Energy and water vapor exchange. Geography Compass, 2008; 2(4): 1027–1057.

Milla R N, Reich P B. Multi-trait interactions, not phylogeny, fine-tune leaf size reduction with increasing altitude. Annals of Botany, 2011; pp.1−11.

Karnieli A, Nurit A, Rachel T, Pinker Martha A, Marc L, Imhoff Garik G, et al. Use of NDVI and land surface temperature for drought assessment: merits and limitations. J. Clim., 2010; 23: 618–633.

Rafn E B, Contor B, Ames DP. Evaluation of a method for estimating irrigated crop transpiration coefficients from remotely SensedData in Idaho. J.Irrig. Drain.Eng., 2008; 134(6): 722.

Kondoh A, Higuchi A. Relationship between satellite-derived spectral brightness and evapotranspiration from grassland. J. Hydrol. Proc., 2001; 15: 1761−1770.

Bowen I S. The ratio of heat losses by conduction and by evaporation from any water surface. Physical Rev., 1926; 27: 779−787.

Nobel P S. Physiochemical and environmental plant physiology. 4th ed. Academic Press, San Diego, CA, USA. 1999.

Salifu T, Agyare W A. Distinguishing different land use types using surface albedo and normalized difference vegetation index derived from the SEBAL for the Atankwidi and a farm sub catchments in Ghana. J. Eng. Appl. Sci.,

; 7(1): 69−80.

Tani H. Estimation of surface albedo from NOAAAVHRR data. J. Fac. Agric., Hokkaido University., 1992; 65(4): 331−341.

Allen R G, Tasumi M, Trezza R. METRICT: Mapping evapotranspiration at high resolution. Applications Manual for Landsat Satellite Imagery. Version 2.0.3, University of Idaho, Kimberly, Idaho, USA. 2007.

Kremer R G, Running S W. Community type differentiation using NOAA/AVHRR data within a sagebrush-steppe ecosystem. Rem. Sens. Environ., 1993; 46: 311–318.

Peters A J, Eve M D. Satellite monitoring of desert plant community response to moisture availability. Environ. Monit. Assess, 1995; 37: 273–287.




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