moisture stress index; semi-arid; irrigation; SPOT-5,; surface water content; normalized difference water index
Abstract :
[en] Irrigated agriculture is an important strategic sector in arid and semi-arid regions. Given the large spatial coverage of irrigated areas, operational tools based on satellite remote sensing can contribute to their optimal management. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high-resolution visible (HRV) data, to retrieve the surface water content values (from bare soil to completely covered soil) over wheat fields and detect irrigation supplies in an irrigated area. These indices are the normalized difference water index (NDWI) and the moisture stress index (MSI), covering the main growth stages of wheat. These indices were compared to corresponding in situ measurements of soil moisture and vegetation water content in 30 wheat fields in an irrigated area of Morocco, during the 2012–2013 and 2013–2014 cropping seasons. NDWI and MSI were highly correlated with in situ measurements at both the beginning of the growing season (sowing) and at full vegetation cover (grain filling). From sowing to grain filling, the best correlation (R2 = 0.86; p < 0.01) was found for the relationship between NDWI values and observed soil moisture values. These results were validated using a k-fold cross-validation methodology; they indicated that NDWI can be used to estimate and map surface water content changes at the main crop growth stages (from sowing to grain filling). NDWI is an operative index for monitoring irrigation, such as detecting irrigation supplies and mitigating wheat water stress at field and regional levels in semi-arid areas.
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R.Balaghi,, M.Jlibene, B.Tychon, and H.Eerens. 2013. Agrometeorological Cereal Yield Forecasting in Morocco. Rabat: INRA.
T.Benabdelouahab, 2009. “Spatialisation des paramètres hydrodynamiques des sols du périmètres irrigué du Tadla par les méthodes géostatistiques.” In INRA Research Activities Report, edited by INRA, 87. Rabat: INRA.
A.Ben-Gal,, D.Kool, N.Agam, G.E.van Halsema, U.Yermiyahu, A.Yafe, E.Presnov, R.Erel, A.Majdop, I.Zipori, E.Segal, S.Rüger, U.Zimmermann, Y.Cohen, V.Alchanatis, and A.Dag. 2010. “Whole-Tree Water Balance and Indicators for Short-Term Drought Stress in Non-Bearing ‘Barnea’ Olives.” Agricultural Water Management 98 (1): 124–133. doi:10.1016/j.agwat.2010.08.008.
H.Bi,, X.Li, X.Liu, M.Guo, and J.Li. 2009. “A Case Study of Spatial Heterogeneity of Soil Moisture in the Loess Plateau, Western China: A Geostatistical Approach.” International Journal of Sediment Research 24 (1): 63–73. doi:10.1016/S1001-6279(09)60016-0.
L.Bouchaou,, J.L.Michelot, M.Qurtobi, N.Zine, C.B.Gaye, P.K.Aggarwal, H.Marah, A.Zerouali, H.Taleb, and A.Vengosh. 2009. “Origin and Residence Time of Groundwater in the Tadla Basin (Morocco) Using Multiple Isotopic and Geochemical Tools.” Journal of Hydrology 379 (3–4): 323–338. doi:10.1016/j.jhydrol.2009.10.019.
D.L.Cassel, 2007. “Re-Sampling and Simulation, the SAS Way.” Paper presented at the Proceedings of the SAS Global Forum 2007 Conference, Cary, NC, May 17.
P.Ceccato,, S.Flasse, and J.M.Grégoire. 2002. “Designing a Spectral Index to Estimate Vegetation Water Content from Remote Sensing Data: Part 2. Validation and Applications.” Remote Sensing of Environment 82 (2–3): 198–207. doi:10.1016/S0034-4257(02)00036-6.
P.Ceccato,, S.Flasse, S.Tarantola, S.Jacquemoud, and J.M.Grégoire. 2001. “Detecting Vegetation Leaf Water Content Using Reflectance in the Optical Domain.” Remote Sensing of Environment 77 (1): 22–33. doi:10.1016/S0034-4257(01)00191-2.
P.Ceccato,, N.Gobron, S.Flasse, B.Pinty, and S.Tarantola. 2002. “Designing a Spectral Index to Estimate Vegetation Water Content from Remote Sensing Data: Part 1: Theoretical Approach.” Remote Sensing of Environment 82: (2–3): 188-97. doi:10.1016/S0034-4257(02)00037-8.
T.Cheng,, D.Riaño, A.Koltunov, M.L.Whiting, S.L.Ustin, and J.C.Rodriguez. 2013. “Detection of Diurnal Variation in Orchard Canopy Water Content Using MODIS/ASTER Airborne Simulator (MASTER) Data.” Remote Sensing of Environment 132: 1–12. doi:10.1016/j.rse.2012.12.024.
T.Cheng,, B.Rivard, and A.Sánchez-Azofeifa. 2011. “Spectroscopic Determination of Leaf Water Content Using Continuous Wavelet Analysis.” Remote Sensing of Environment 115 (2): 659–670. doi:10.1016/j.rse.2010.11.001.
T.Cheng,, B.Rivard, A.G.Sánchez-Azofeifa, J.B.Féret, S.Jacquemoud, and S.L.Ustin. 2012. “Predicting Leaf Gravimetric Water Content from Foliar Reflectance across a Range of Plant Species Using Continuous Wavelet Analysis.” Journal of Plant Physiology 169 (12): 1134–1142. doi:10.1016/j.jplph.2012.04.006.
L.Du,, Q.Tian, T.Yu, Q.Meng, T.Jancso, P.Udvardy, and Y.Huang. 2013. “A Comprehensive Drought Monitoring Method Integrating MODIS and TRMM Data.” International Journal of Applied Earth Observation and Geoinformation 23: 245–253. doi:10.1016/j.jag.2012.09.010.
C.D.Elvidge,, and R.J.P.Lyon. 1985. “Influence of Rock-Soil Spectral Variation on the Assessment of Green Biomass.” Remote Sensing of Environment 17 (3): 265–279. doi:10.1016/0034-4257(85)90099-9.
S.Er-Raki,, A.Chehbouni, and B.Duchemin. 2010. “Combining Satellite Remote Sensing Data with the FAO-56 Dual Approach for Water Use Mapping in Irrigated Wheat Fields of a Semi-Arid Region.” Remote Sensing 2 (1): 375–387. doi:10.3390/rs2010375.
H.Feng,, C.Chen, H.Dong, J.Wang, and Q.Meng. 2013. “Modified Shortwave Infrared Perpendicular Water Stress Index: A Farmland Water Stress Monitoring Method.” Journal of Applied Meteorology and Climatology 52 (9): 2024–2032. doi:10.1175/jamc-d-12-0164.1.
B.C.Gao, 1996. “NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space.” Remote Sensing of Environment 58 (3): 257–266. doi:10.1016/S0034-4257(96)00067-3.
S.Geerts,, and D.Raes. 2009. “Deficit Irrigation as On-Farm Strategy to Maximize Crop Water Productivity in Dry Areas.” Agricultural Water Management 96: 1275–1284. doi:10.1016/j.agwat.2009.04.009.
A.Ghulam,, Q.Qin, T.Kuskya, and Z.Li. 2008. “A Re-Examination of Perpendicular Drought Indices.” International Journal of Remote Sensing 29: 6037–6044. doi:doi:10.1080/01431160802235811.
A.Ghulam,, Q.Qin, T.Teyip, and Z.L.Li. 2007. “Modified Perpendicular Drought Index (MPDI): A Real-Time Drought Monitoring Method.” ISPRS Journal of Photogrammetry and Remote Sensing 62 (2): 150–164. doi:10.1016/j.isprsjprs.2007.03.002.
M.-C.Girard,, and C.-M.Girard. 2010. Traitement des données de télédétection: Environnement et ressources naturelles. 2 ed., edited by Dunod. Paris: Dunod. Environnement et sécurité.
Y.Guo,, and F.Zeng. 2012. “Atmospheric Correction Comparison of SPOT-5 Image Based on Model FLAASH and Model QUAC.” International Archives of the Photogrammetry and Remote Sensing Spatial Information Science XXXIX-B7: 7–11. doi:10.5194/isprsarchives-XXXIX-B7-7-2012.
R.Hadria,, B.Duchemin, L.Jarlan, G.Dedieu, F.Baup, S.Khabba, A.Olioso, and T.Le Toan. 2010. “Potentiality of Optical and Radar Satellite Data at High Spatio-Temporal Resolutions for the Monitoring of Irrigated Wheat Crops in Morocco.” International Journal of Applied Earth Observation and Geoinformation 12 (1): S32–S37. doi:10.1016/j.jag.2009.09.003.
M.A.Hardisky,, R.Michael Smart, and V.Klemas. 1983. “Growth Response and Spectral Characteristics of a Short Spartina Alterniflora Salt Marsh Irrigated with Freshwater and Sewage Effluent.” Remote Sensing of Environment 13 (1): 57–67. doi:10.1016/0034-4257(83)90027-5.
E.R.Hunt, Jr, L.Li, M.T.Yilmaz, and T.J.Jackson. 2011. “Comparison of Vegetation Water Contents Derived from Shortwave-Infrared and Passive-Microwave Sensors over Central Iowa.” Remote Sensing of Environment 115 (9): 2376–2383. doi:10.1016/j.rse.2011.04.037.
E.R.Hunt, Jr, and B.N.Rock. 1989. “Detection of Changes in Leaf Water Content Using Near- and Middle-Infrared Reflectances.” Remote Sensing of Environment 30 (1): 43–54. doi:10.1016/0034-4257(89)90046-1.
S.Jacquemoud,, and F.Baret. 1990. “PROSPECT: A Model of Leaf Optical Properties Spectra.” Remote Sensing of Environment 34 (2): 75–91. doi:10.1016/0034-4257(90)90100-Z.
J.R.Jensen, 2005. Introductory Digital Image Processing: A Remote Sensing Perspective. 3rd ed. Upper Saddle River, NJ: Prentice-Hall.
J.R.Jensen, 2007. Remote Sensing of the Environment: An Earth Resource Perspective. 2nd ed. Upper Saddle River, NJ: Prentice Hall.
M.B.Kirkham, 2005. “8 - Field Capacity, Wilting Point, Available Water, and the Nonlimiting Water Range.” In Principles of Soil and Plant Water Relations, edited by M.B.Kirkham, 101–115. Burlington, MA: Academic Press.
F.N.Kogan, 2000. “Contribution of Remote Sensing to Drought Early Warning.” Paper presented at the Early Warning Systems for Drought Preparedness and Drought Management, Lisbon, September 5–7.
R.Lasaponara,, and N.Masini, eds. 2012. Satellite Remote Sensing: A New Tool for Archaeology. Dordrecht: Springer.
J.Lei,, L.Zhang, and W.Bruce. 2009. “Analysis of Dynamic Thresholds for the Normalized Difference Water Index.” Photogrammetric Engineering & Remote Sensing 75: 1307–1317. doi:10.14358/PERS.75.11.1307.
H.Lionboui,, A.Fadlaoui, F.Elame, and T.Benabdelouahab. 2014. “Water Pricing Impact on the Economic Valuation of Water Resources.” International Journal of Education and Research 2 (6): 147–166.
S.Liu,, D.A.Roberts, O.A.Chadwick, and C.J.Still. 2012. “Spectral Responses to Plant Available Soil Moisture in a Californian Grassland.” International Journal of Applied Earth Observation and Geoinformation 19: 31–44. doi:10.1016/j.jag.2012.04.008.
D.B.Lobell,, G.P.Asner, J.I.Ortiz-Monasterio, and T.L.Benning. 2003. “Remote Sensing of Regional Crop Production in the Yaqui Valley, Mexico: Estimates and Uncertainties.” Agriculture, Ecosystems and Environment 94: 205–220. doi:10.1016/S0167-8809(02)00021-X.
M.Momeni,, and M.R.Saradjian. 2007. “Evaluating Ndvi-Based Emissivities of MODIS Bands 31 and 32 Using Emissivities Derived by Day/Night LST Algorithm.” Remote Sensing of Environment 106 (2): 190–198. doi:10.1016/j.rse.2006.08.005.
A.Moreno,, F.Maselli, M.Chiesi, L.Genesio, F.Vaccari, G.Seufert, and M.A.Gilabert. 2014. “Monitoring Water Stress in Mediterranean Semi-Natural Vegetation with Satellite and Meteorological Data.” International Journal of Applied Earth Observation and Geoinformation 26: 246–255. doi:10.1016/j.jag.2013.08.003.
E.Muller,, and H.Décamps. 2001. “Modeling Soil Moisture-Reflectance.” Remote Sensing of Environment 76: 173–180. doi:10.1016/S0034-4257(00)00198-X.
A.Najine,, M.Jaffal, K.E.Khammari, T.Aïfa, D.Khattach, M.Himi, A.Casas, S.Badrane, and H.Aqil. 2006. “Contribution de la gravimétrie à l'étude de la structure du bassin de Tadla (Maroc): Implications hydrogéologiques.” Comptes Rendus Geoscience 338 (10): 676–682. doi:10.1016/j.crte.2006.04.015.
Z.Ning,, H.Yang, Q.Qiming, and L.Lu. 2013. “VSDI: A Visible and Shortwave Infrared Drought Index for Monitoring Soil and Vegetation Moisture Based on Optical Remote Sensing.” International Journal of Remote Sensing 34: 4585–4609. doi:10.1080/01431161.2013.779046.
ORMVAT. 2009. “Rapport annuel de l'Office de la mise en valeur agricole de Tadla.” ORMVAT Activities Report 27.
M.Ozdogan,, Y.Yang, G.Allez, and C.Cervantes. 2010. “Remote Sensing of Irrigated Agriculture: Opportunities and Challenges.” Remote Sensing 2 (9): 2274–2304. doi:10.3390/rs2092274.
J.Penuelas,, J.Pinol, R.Ogaya, and I.Filella. 1997. “Estimation of Plant Water Concentration by the Reflectance Water Index WI (R900/R970).” International Journal of Remote Sensing 18 (13): 2869–2875. doi:10.1080/014311697217396.
Q.Qin,, A.Ghulam, L.Zhu, L.Wang, J.Li, and P.Nan. 2008. “Evaluation of MODIS Derived Perpendicular Drought Index for Estimation of Surface Dryness Over Northwestern China.” International Journal of Remote Sensing 29: 1983–1995. doi:doi:10.1080/01431160701355264.
Y.QiuXiang,, B.AnMing, L.Yi, and Z.Jin. 2012. “Measuring Cotton Water Status Using Water-Related Vegetation Indices at Leaf and Canopy Levels.” Journal of Arid Land 4 (3): 310−9. doi:10.3724/SP.J.1227.2012.00310.
K.Richter,, C.Atzberger, T.B.Hank, and W.Mauser. 2012. “Derivation of Biophysical Variables from Earth Observation Data: Validation and Statistical Measures.” Journal of Applied Remote Sensing 6 (1): 063557–23. doi:10.1117/1.jrs.6.063557.
A.S.Rogers,, and M.S.Kearney. 2004. “Reducing Signature Variability in Unmixing Coastal Marsh Thematic Mapper Scenes Using Spectral Indices.” International Journal of Remote Sensing 25 (12): 2317–2335. doi:10.1080/01431160310001618103.
D.Seckler,, R.Barker, and U.Amarasinghe. 1999. “Water Scarcity in the Twenty-First Century.” International Journal of Water Resources Development 15: 29–42. doi:10.1080/07900629948916.
E.L.Skidmore,, J.D.Dickerson, and H.Shimmelpfennig. 1975. “Evaluating Surface-Soil Water Content by Measuring Reflectance.” Soil Science Society of American Proceedings 39: 238–242. doi:10.2136/sssaj1975.03615995003900020009x.
Q.Tian,, Q.Tong, R.Pu, X.Guo, and C.Zhao. 2001. “Spectroscopic Determination of Wheat Water Status Using 1650-1850 Nm Spectral Absorption Features.” International Journal of Remote Sensing 22 (12): 2329–2338. doi:10.1080/01431160118199.
M.Trombetti,, D.Riaño, M.A.Rubio, Y.B.Cheng, and S.L.Ustin. 2008. “Multi-Temporal Vegetation Canopy Water Content Retrieval and Interpretation Using Artificial Neural Networks for the Continental USA.” Remote Sensing of Environment 112 (1): 203–215. doi:10.1016/j.rse.2007.04.013.
Y.-G.Wang,, H.Zhu, and L.Yan. 2013. “Spatial Heterogeneity of Soil Moisture, Microbial Biomass Carbon and Soil Respiration at Stand Scale of an Arid Scrubland.” Environmental Earth Sciences 70 (7): 3217–3224. doi:10.1007/s12665-013-2386-z.
S.Xiaoning,, Z.Xia, L.Xiaotao, and L.Xinhui. 2009. “Spatial Heterogeneity Analysis of Soil Moisture Based on Geostatistics.” Paper presented at 2009 1st International Conference on Information Science and Engineering (ICISE), Nanjing, December 26–28.
N.Yang,, Q.Qin, C.Jin, and Y.Yao. 2008. “The Comparison and Application of the Methods for Monitoring Farmland Drought Based on NIR-Red Spectral Space.” International Geoscience and Remote Sensing Symposium (IGARSS 2008) III871–III4. doi:10.1109/IGARSS.2008.4779488.
M.T.Yilmaz,, E.R.HuntJr, L.D.Goins, S.L.Ustin, V.C.Vanderbilt, and T.J.Jackson. 2008. “Vegetation Water Content during SMEX04 from Ground Data and Landsat 5 Thematic Mapper Imagery.” Remote Sensing of Environment 112 (2): 350–362. doi:10.1016/j.rse.2007.03.029.
M.T.Yilmaz,, E.R.HuntJr, and T.J.Jackson. 2008. “Remote Sensing of Vegetation Water Content from Equivalent Water Thickness Using Satellite Imagery.” Remote Sensing of Environment. 112 (5): 2514–2522. doi:10.1016/j.rse.2007.11.014.
H.Zhang,, H.Chen, S.Shen, G.Zhou, and W.Yu. 2008. “Drought Remote Sensing Monitoring Based on the Surface Water Content Index (SWCI) Method.” Remote Sensing Technology and Application 23: 624–628.
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