Agricultural Drought Indices - US Department of Agriculture

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Drought Assessment Using MERIS Images Alberto Rodríguez Fontal General Directorate of Water Ministry of the Environment and Rural and Marine Affairs, Madrid, Spain Abstract Use of satellite images for drought monitoring purposes provides water managers with valuable information. The National Drought Mitigation Center of the University of Nebraska has been successfully using this methodology in the United States, using MODIS satellite images. In the present work, the methodology developed by the University of Nebraska is adapted to the European background, and it is applied to Spain. Main differences between American satellite MODIS and European satellite MERIS are shown, and a modified index adapted to MERIS satellite images is proposed. Introduction The spectral drought index developed by the University of Nebraska, called Normalized Difference Drought Index (NDDI), for the drought assessment from images MODIS (resolution of 1 km), is based on the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). Where: RED red, NIR near infrared and SWIR infrared shortwave Figure 1. Definition of Drought Index (University of Nebraska) The initial demonstrations of the NDDI potential in drought assessment were developed in the North American grasslands of the region of Flint Hills (Kansas and Oklahoma), where there is one of the vastest extensions of prairie. The conclusion reached was that the NDDI presented a remarkable sensitivity to NDVI-NDWI differences and broader ranges of response during periods of drought than during the rainy season (Gu et al., 2007). The application of drought index developed by the University of Nebraska in the Spanish territory is initially constrained by the characteristics of the data used in the study, which are images acquired from the MERIS sensor. A comparison of the MODIS and MERIS characteristics is shown in Table 1. Table 1. Comparison of MODIS and MERIS characteristics Sensor Number of bands Resolution (m) Revisit (days) Swath width VIS NIR SWIR TIR Min Max Min Max (Km) MERIS 12 3 260 1.040 1 3 1.150 MODIS 1 1 5 29 250 1.000 1 2 2.330 Where: VIS: visible; NIR: near infrared; SWIR: short-wave infrared; TIR: thermal infrared 165
Table 2. MERIS spectral bands MDS Nr. Band centre (nm) Bandwidth (nm) Potential Applications B1 412,5 10 Yellow substance and detrital pigments B2 442,5 10 Chlorophyll absorption maximum B3 490 10 Chlorophyll and other pigments B4 510 10 Suspended sediment, red tides B5 560 10 Chlorophyll absorption minimum B6 620 10 Suspended sediment B7 665 10 Chlorophyll absorption and fluo. reference B8 681,25 7,5 Chlorophyll fluorescence peak B9 708,75 10 Fluo. Reference, atmospheric corrections B10 753,75 7,5 Vegetation, cloud B11 761 3,75 Oxygen absorption R-branch B12 778,75 15 Atmosphere corrections B13 865 20 Vegetation, water vapour reference B14 885 10 Atmosphere corrections B15 900 10 Water vapour, land Both sensors have bands near the red and infrared wavelengths, and the NDVI derived from images is a product usually obtained from the bands 8 and 13. For the estimation of NDWI, the MERIS images do not have band in the short-wave infrared, so it has been selected as an alternative band, the one around 0.56 μm (band 5) where the absorption of the chlorophyll is minimal and reflectance of maximum water. Therefore, the equations for MERIS images are as follows: Figure 2. Equations for MERIS images From the spectral responses of the same vegetation cover with different states of drying (1-10), using USGS spectral signatures2, both drought indices were calculated for MODIS (MO) and MERIS (ME) bands wavelengths. Figure 3. Spectral response of the vegetation with different degrees of drying 166
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Agricultural Drought Indices Procee
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© World Meteorological Organizatio
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Agricultural Drought Indices Procee
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Preface With the world population p
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Segura River Basin: Spanish Pilot R
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Table 3. Segura River Basin resourc
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Figure 5. Segura River Basin water
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Drought Indicator Expression After
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Table 5. Analysis of drought impact
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Even with this drought action plan
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that complicates water management b
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Understanding the nature of the haz
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Figure 2. U.S. Drought Monitor for
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Summary Drought is a creeping pheno
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Agricultural Drought—WMO Perspect
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c) CAgM-VI (Washington, USA, 1974)
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an unusually large moisture demand.
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the-clock nature of WIGOS, the anal
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and temporal variations in drought
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Support to Regional Institutions WM
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Williams, M.A.J. and R.C. Balling,
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The day after Black Sunday, an Asso
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Meteorological research at these in
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United States is sent to the Secret
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Palmer Drought Severity Index (PDSI
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PDSI as an operational index since
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WAOB, GIS has become an important t
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USDM as the official criteria for F
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Monitoring Drought Risks in India w
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areas recording large incidences of
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The analysis revealed that out of 1
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Figure 3. Aridity anomaly chart of
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Figure 5. Progression of surface we
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Agricultural Drought Indices in Cur
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Meteorological and Agricultural Dro
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Palmer Drought Severity Index Adapt
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Accumulated WD and WS April 2010 Ac
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Soil Water Storage (SWS) September
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hydrological characteristics, and c
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Agricultural Drought Indices in Cur
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application of drought indices has
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The Value of Crop and Pasture Simul
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To assist, many agriculturally-spec
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change in Australia. The PDSI provi
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Mpelasoka, F.S., M.A. Collier, R. S
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supply and demand are fully taken i
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In Germany, the Deutsche Wetterdien
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creation of a system of European su
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The second index is extracted from
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Figure 7. Standardized Soil Water I
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References Durand, Y., E. Brun, L.
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Europe around the Iberian Peninsula
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The incoming rainwater and the atmo
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Figure 6. Example of SPI map for a
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The 1991-95 Drought Episode A long-
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adverse climatic impacts on agricul
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Agricultural Drought Indices in the
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efers to deficiencies in surface an
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Figure 5. Time series of standardiz
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Limitations of Agricultural Drought
Page 121 and 122: Incorporating a Composite Approach
Page 123 and 124: Figure 3. Daily gridded SPI by regi
Page 125 and 126: The North American Drought Monitor:
Page 127 and 128: process (a) emulates natural cycles
Page 129 and 130: Summary Given the complexity that d
Page 131 and 132: alance models cannot be applied for
Page 133 and 134: 60 50 40 Error 30 20 Systematic Err
Page 135 and 136: In Figure 4, examples of the normal
Page 137 and 138: Figure 7. Water balance for Brazil,
Page 139 and 140: The soil water balance module in th
Page 141 and 142: Water Balance as a Tool for Agricul
Page 143 and 144: Conclusions The topics discussed pr
Page 145 and 146: Use of Crop Models for Drought Anal
Page 147 and 148: The parameters used in crop simulat
Page 149 and 150: a model for cassava and potato (Tsu
Page 151 and 152: ackground. The system’s most impo
Page 153 and 154: management. In research, however, p
Page 155 and 156: Smajstrla, A.G. 1990. Technical Man
Page 157 and 158: Monitoring Regional Drought Conditi
Page 159 and 160: Figure 2 represents the regression
Page 161 and 162: Conclusions and Discussion Several
Page 163 and 164: Experiences During the Drought Peri
Page 165 and 166: During the years 2007 and 2008, for
Page 167 and 168: Figure 6. Management simulation mod
Page 169 and 170: Figure 9. Irrigation diversions in
Page 171: References Andreu, J., Ferrer Polo,
Page 175 and 176: Figure 6. Spanish NSDIE during 2008
Page 177 and 178: References Gu, Y., J. F. Brown, J.
Page 179 and 180: Agricultural Drought Indices: Summa
Page 181 and 182: Table 1. Agricultural drought indic
Page 183 and 184: Accumulated Rainfall Deficits In an
Page 185 and 186: Heim (2002) notes that as recently
Page 187 and 188: each calendar day in the base perio
Page 189 and 190: The purpose of the climatic charact
Page 191 and 192: to compare moisture conditions at d
Page 193 and 194: Table 6. Overview of different drou
Page 195 and 196: have been adopted because of the av
Page 197 and 198: TCI = 100 (T max - T) / (T max - T
Page 199 and 200: Figure 1. The February 8, 2011, US
Page 201 and 202: 8) There is a universal interest in
Page 203 and 204: Mavromatis, T. 2007. Drought index
Page 205: Agricultural Drought Indices Procee
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