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Large-Scale Water Productivity Assessments with MODIS Images in a Changing Semi-Arid Environment: A Brazilian Case?Study

DOI: 10.3390/rs5115783

Keywords: evapotranspiration, biomass production, net radiation, surface resistance

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Abstract:

In the Brazilian semi-arid region, the intensification of agriculture results in a change of natural vegetation by irrigated crops. To quantify the contrast between these two ecosystems, the large-scale values of water productivity components were modelled in Petrolina (PE) and Juazeiro (BA) municipalities. The SAFER (Simple Algorithm For Evapotranspiration Retrieving) algorithm was used to acquire evapotranspiration (ET), while the Monteith's radiation model was applied for estimating the biomass production (BIO). Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used together with agro-meteorological data. In Petrolina and Juazeiro, the mean monthly ET values for irrigated crops were 938 and 739 mm?month ?1, with the corresponding ones for natural vegetation of 385 and 194 mm?month ?1.Water productivity (WP) was analysed by the ratio of BIO to ET, defined here as the ratio of the net benefits from the mixed agricultural systems to the amount of water required for producing those benefits. The highest incremental WP values, as a result of the irrigated crops introduction, happened outside the rainy period. More spatial WP uniformity occurred in natural vegetation, when comparing with irrigated crops. The most frequent WP values in Petrolina were between 1.6 and 2.2 kg?m ?3 while in Juazeiro this range was from 1.0 to 1.6 kg?m ?3. The differences between the municipalities can be mainly explained by differences in precipitation and soil water storages conditions, promoting better rainfall use efficiency by the natural vegetation in the first one. The results of the current research are important for appraising the land use change impacts in situations of expanding irrigation areas.

References

[1]  Teixeira, A.H.C.; Bassoi, L.H. Crop water productivity in semi-arid regions: From field to large scales. Ann. Arid Zone 2009, 48, 1–13.
[2]  Ceschia, E.; Beziat, P.; Dejoux, J.F.; Aubinet, M.; Bernhofer, C.; Bodson, B.; Carrara, A.; Cellier, P.; Di Tommasi, P.; Elbers, J.A.; et al. Management effects on net ecosystem carbon and GHG budgets at European crop sites. Agric. Ecossyst. Environ 2010, 139, 363–383.
[3]  Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements; Food and Agriculture Organization of the United Nations: Rome, Italy, 1998.
[4]  Teixeira, A.H. C.; Bastiaanssen, W.G.M.; Bassoi, L.H. Crop water parameters of irrigated wine and table grapes to support water productivity analysis in Sao Francisco River basin, Brazil. Agr. Water Manag 2007, 94, 31–42.
[5]  Teixeira, A.H.C.; Bastiaanssen, W.G.M. Five methods to interpret field measurements of energy fluxes over a micro-sprinkler-irrigated mango orchard. Irrig. Sci 2012, 30, 13–18.
[6]  Teixeira, A.H.C. Water Productivity Assessments from Field to Large Scale: A Case Study in the Brazilian Semi-arid Region; LAP Lambert Academic Publishing: Saarbrücken, Germany, 2009; pp. 1–226.
[7]  Tang, Q.; Rosemberg, E.A; Letenmaier, D.P. Use of satellite data to assess the impacts of irrigation withdrawals on Upper Klamath Lake, Oregon. Hydrol. Earth Syst. Sci 2009, 13, 617–627.
[8]  Teixeira, A.H.C. Determining regional actual evapotranspiration of irrigated and natural vegetation in the S?o Francisco river basin (Brazil) using remote sensing and Penman-Monteith equation. Remote Sens 2010, 2, 1287–1319.
[9]  Miralles, D.G.; Holmes, T.R.H.; de Jeu, R.A.M.; Gash, J.H.; Meesters, A.G.C.A.; Dolman, A.J. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci 2011, 15, 453–469.
[10]  P??as, I.; Cunha, M.; Pereira, L.S.; Allen, R.G. Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands. Int. J. Appl. Earth Obs. Geoinf 2013, 21, 159–172.
[11]  Teixeira, A.H.C.; Bastiaanssen, W.G.M; Ahmad, M–ud–D; Bos, M.G. Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle S?o Francisco River basin, Brazil Part A: Calibration and validation. Agric. For. Meteorol 2009, 149, 462–476.
[12]  Teixeira, A.H.C.; Bastiaanssen, W.G.M.; Ahmad, M–ud–D; Bos, M.G. Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle S?o Francisco River basin, Brazil Part B: Application to the large scale. Agric. For. Meteorol 2009, 149, 477–490.
[13]  Cleugh, H.A.; Leuning, R.; Mu, Q.; Running, S.W. Regional evaporation estimates from flux tower and MODIS satellite data. Remote. Sens. Environ 2007, 106, 285–304.
[14]  Nagler, P.L.; Glenn, E.P.; Nguyen, U.; Scott, R.L.; Doody, T. Estimating riparian and agricultural actual evapotranspiration by reference evapotranspiration and MODIS enhanced vegetation index. Remote Sens 2013, 5, 3849–3871.
[15]  Kamble, B.; Kilic, A.; Hubard, K. Estimating crop coefficients using remote sensing-based vegetation index. Remote Sens 2013, 5, 1588–1602.
[16]  Allen, R.G.; Tasumi, M.; Morse, A.; Trezza, R.; Wright, J.L.; Bastiaanssen, W.G.M.; Kramber, W.; Lorite, I.; Robison, C.W. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Applications. J. Irrig. Drain. Eng. ASCE 2007, 133, 395–406.
[17]  Teixeira, A.H.C.; Bastiaanssen, W.G.M.; Ahmad, M.D.; Bos, M.G. Analysis of energy fluxes and vegetation-atmosphere parameters in irrigated and natural ecosystems of semi-arid Brazil. J. Hydrol 2008a, 362, 110–127.
[18]  Teixeira, A.H.C. Modelling Evapotranspiration by Remote Sensing Parameters and Agro-meteorological Stations. In Remote Sensing and Hydrology; Neale, C.M.U., Cosh, M.H., Eds.;. IAHS Publ. 352; IAHS Press: Wallingford, UK, 2012; pp. 154–157.
[19]  Wu, C.; Munger, J.W.; Niu, Z.; Kuanga, D. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Havard Forest. Remote Sens. Environ 2010, 114, 2925–2939.
[20]  Adak, T.; Kumar, G.; Chakravarty, N.V.K.; Katiyar, R.K.; Deshmukh, P.S. Biomass and biomass water use efficiency in oilseed crop (Brassica. Jnceae. L.) under semi-arid microenvironments. Biomass Bioenergy 2013, 51, 154–162.
[21]  Yan, H.M.; Fu, Y.L.; Xiao, X.M.; Huang, H.Q.; He, H.I.; Ediger, L. Modeling gross primary productivity for winter wheat-maize double cropping system using MODIS time series and CO2 eddy flux tower data. Agr. Ecosyst. Environ 2009, 129, 391–400.
[22]  Claverie, M.; Demarez, V.; Duchemin, B.; Hagolle, O.; Ducrot, D.; Marais-Sicre, C.; Dejuoux, J.-F.; Huc, M.; Keravec, P.; Béziat, P.; et al. Maize and sunflower biomass estimation in southwest France using spatial and temporal resolution remote sensing data. Remote Sens. Environ 2012, 124, 884–857.
[23]  Prince, S.D. High Temporal Frequency Remote Sensing of Primary Production Using NOAA/AVHRR. In Applications of Remote Sensing in Agriculture; Steven, M.D., Clark, J.A., Eds.; Butterworths: London, UK, 1990; pp. 169–183.
[24]  Tesfaye, K.; Walker, S.; Tsubo, M. Radiation interception and radiation use efficiency of three grain legumes under water deficit conditions in a semi-arid environment. Europ. J. Agron 2006, 25, 60–70.
[25]  Monteith, J.L. Solar radiation and productivity in tropical ecosystems. J. App. Ecol 1972, 9, 747–766.
[26]  Zhao, M.; Heinsch, F.A.; Nemani, R.R.; Running, S.W. Improving of the MODIS terrestrial gross and net primary production global dataset. Remote Sens. Environ 2005, 95, 164–176.
[27]  Bastiaanssen, W.G.M.; Ali, S. A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Eco. Environ 2003, 94, 32–340.
[28]  Zwart, S.J.; Bastiaanssen, W.G.M.; de Fraiture, C.; Molden, D.J. WATPRO: A remote sensing based model for mapping water productivity of wheat. Agric. Water Manage 2010, 97, 1628–1636.
[29]  Ahamed, T.; Tian, L.; Zhang, Y.; Ting, K.C. A review of remote sensing methods for biomass feedstock production. Biomass Bioenergy 2011, 35, 2455–2469.
[30]  Baccine, A.; Friedl, M.A.; Woodcrock, C.E.; Warbington, R. Forest biomass estimation over regional scales using multisource data. Geophys. Res. Lett 2004, 31, 1–4.
[31]  Shi, X.; Elmore, A.; Li, X.; Gorence, N.J.; Jin, H.; Zhang, X. Using spatial information technologies to select sites for biomass power plants: A case study in Guangdong, China. Biomass Bioenergy 2008, 32, 35–43.
[32]  Lu, D. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon basin. Int. J. Remote Sens 2005, 26, 2509–2525.
[33]  Molden, D.; Bin, D.; Loeve, R.; Barker, R.; Tuong, T.P. Agricultural water productivity and savings: Policy lessons from two diverse sites in China. Water Policy 2007, 9, 29–44.
[34]  Teixeira, A.H.C. Modelling Water Productivity Components in the Low-Middle S?o Francisco River Basin, Brazil. In Sustainable Water Management in the Tropics and Subtropics and Case Studies in Brazil, 1st ed. ed.; University of Kassel: Kassel, Germany, 2012; pp. 1077–1100.
[35]  Yuan, M.; Zhang, L.; Gou, F.; Su, Z.; Spiertz, J.H.J.; van der Werf, W. Assessment of crop growth and water productivity for five C3 species in the semi-arid Inner Mongolia. Agric. Water Manage 2013, 122, 28–38.
[36]  Teixeira, A.H.C.; Bastiaanssen, W.G.M.; Moura, M.S.B.; Soares, J.M.; Ahmad, M.D.; Bos, M.G. Energy and water balance measurements for water productivity analysis in irrigated mango trees, Northeast Brazil. Agric. For. Meteorol 2008, 148, 1524–1537.
[37]  Mo, X.; Liu, S.; Lin, Z.; Guo, R. Regional crop yield, water consumption and water use efficiency and their responses to climate change in the North China. Agric. Ecosyst. Environ 2009, 134, 67–78.
[38]  Teixeira, A.H.C. Determination of Surface Resistance to Evapotranspiration by Remote Sensing Parameters in the Semi-arid Region of Brazil for Land-use Change Analyses. In Remote Sensing and Hydrology; Neale, C.M.U., Cosh, M.H., Eds.;. IAHS Publ. 352; IAHS Press: Wallingford, UK, 2012; pp. 167–170.
[39]  Valiente, J.A.; Nunez, M.; Lopez-Baeza, E.; Moreno, J.F. Narrow-band to broad-band conversion for Meteosat visible channel and broad-band albedo using both AVHRR-1 and -2 channels. Int. J. Remote Sens 1995, 16, 1147–1166.
[40]  Coll, C.; Caselles, V.A. Split-window algorithm for land surface temperature from advanced very high resolution radiometer data: Validation and algorithm comparison. J. Geophys. Res 1997, 102, 16697–16714.
[41]  Wang, X.; Ma, M.; Huang, G.; Veroustraete, F.; Zhang, Z.; Song, Y.; Tan, J. Vegetation primary production estimation at maize and alpine meadow over the Heihe River Basin, China. Int. J. App. Earth Obs. Geoinf 2012, 17, 94–101.
[42]  Sadras, A.D.; Angus, J.F. Benchmarking water-use efficiency of rainfed wheat in dry environments. Aus. J. Agric. Res 2006, 57, 847–853.
[43]  Hatfield, H.L.; Thomas, J.S.; John, H.P. Managing soil to achieve greater water use efficiency: A review. Agron. J 2001, 93, 271–280.

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