Assessing Drought in Agricultural Area of central U.S. with the MODIS sensor Di Wu George Mason University Oct 17 th, 2012
Introduction: Drought is one of the major natural hazards which has devastating effects on environmental, social and economic sectors Among many human-related sectors, agriculture is often the first and most vulnerable sector to be affected by drought events. Drought events can reduce productivity of desirable crops. The variations of precipitation pattern can increase short-run crop failures and long-run production declines (Nelson et al., 2009). Drought can also cause death of livestock, increase crop diseases and aggravate crop growing environment. 2
Introduction: Factors affecting agricultural drought: - climate/ weather (evaporation, precipitation, temperature) - soil (type, moisture) - crops (species, stage of growth) - irrigation Traditional agricultural drought monitoring Agricultural drought can be monitored effectively based on in-situ meteorological data from weather stations. Drought indices aid in agricultural drought detection (PDSI, SPI, precipitation percentiles ) models considers soil AWC, crop water deficiency, irrigation... 3
Introduction: USDM drought map Created in 1999, the weekly USDM (The U.S. Drought Monitor) was a composite index which combines information from many existing drought indicators, including the PDSI and the SPI, along with local reports from state climatologists and observers across the country. USDM are using a simple D0-D4 scheme and a percentile ranking methodology to monitor drought occurrence across the country http://droughtmonitor.unl.edu/ 4
Introduction: Limitation of current drought monitoring methods Traditional drought monitoring is mostly based on meteorological data derived from weather station which is not adequate for characterization of drought conditions at regional scale, especially where current networks of weather stations are sparse Limited to topographic condition, it is not possible to set up an intensive weather monitoring network at complex terrain. Using interpolation to estimate meteorological variables often produce some uncertainties Drought information provided by USDM drought map is at coarse level of spatial detail which limits drought study at finer scale 5
Introduction: Agricultural drought monitoring with remote sensing In the past 10-15 years, satellite remote sensing has proven to be a perfect utility for operational drought management as a separate tool and is complementary to weather data (Kogan 2002) Remote sensing provide cost effective, near real time agricultural monitoring over large area. The satellite constantly monitor various environmental components which potentially affected by agricultural drought (soil, vegetation, ET, LST ) 6
Introduction: Agricultural drought monitoring with remote sensing Compared to traditional drought monitoring methods, remote sensing technique can detect drought onset, duration and severity, providing farmers and scientists with timely drought information at continuous spatial coverage (Thiruvengadachari and Gopalkrishna 1993) 7
Introduction: Agricultural drought monitoring with remote sensing Different contributions of Remote Sensing for drought monitoring Vegetation Status Soil moisture Temperature Evapotranspiration Precipitation 8
Introduction: Agricultural drought monitoring with remote sensing Advantages Cost-effective and rapid method of acquiring up-to-date information over a large geographical area. An practical way to obtain data from inaccessible or isolated areas. High repetition rate and continuous coverage. Standard tools and techniques. Limitations Indirect measurements of the phenomenon. interference from cloud cover and atmospheric particles Geometric issues Sensor calibration issues 9
Introduction: Agricultural drought monitoring with remote sensing AVHRR -> MODIS -> VIIRS Historically, the most widely used satellite sensor for large-area drought monitoring is AVHRR due to its large spatial coverage and relatively long time record. With the launch of Terra and Aqua platforms in 1999 and 2002, MODIS provides a potential for more accurate and real time drought monitoring. MODIS outperforms AVHRR in that it provides higher spatial, more spectral channels, more accurate geolocation, and improved atmospheric corrections. Visible Infrared Imaging Radiometer Suite (VIIRS) is designed for long-term continuity of spatial data series initiated by AVHRR and MODIS. It is on Suomi NPP satellite and launched on 2011. Like MODIS, VIIRS collects visible and infrared imagery and radiometric measurements of the Earth and improves spectral measurements and image quality provided by AVHRR and MODIS. 10
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Introduction: Agricultural drought monitoring with remote sensing NDVI (Normalized Difference Vegetation Index): (NIR Red)/(NIR + Red) (Tucker, 1979) VCI (Vegetation Condition Index): NDVI NDVImin / (NDVImax NDVImin)*100 (Kogan, 1990) PVI (Perpendicular Vegetation Index) (Richardson & Wiegand, 1977) PDI (Perpendicular Drought Index) (Ghulam et al., 2006) and MPDI (Modified Perpendicular Drought Index) (Ghulam et al., 2007) EVI (Enhanced Vegetation Index) : 2.5(NIR-Red)/(NIR+6Red-7.5Blue+1) (Huete et al. 2002) NDWI (Normalized Difference Water Index): (NIR SWIR)/(NIR + SWIR) (Gao, 1996) GVMI (Global Vegetation Monitoring Index): ((NIR + 0.1) (SWIR + 0.02))/((NIR rect + 0.01) + (SWIR + 0.02)) (Ceccato et al., 2001) NMDI (Normalized Multi-band Drought Index) : NIR-(Modis6-Modis7)/ (NIR+(Modis6+Modis7)) (Wang and Qu, 2007) NDII (Normalized Difference Infrared Index) : (NIR SWIR)/(NIR + SWIR) (Hunt and Rock, 1989) 12
Agricultural Drought assessment with MODIS 2011 central U.S. drought In 2011, Texas has experienced most severe drought disaster in history The intense dryness, high evapotranspiration combined with temperature extreme makes drought rapid spread in south central of United States (State of the Climate Drought, NOAA) U. S drought monitor maps for May 10, and July 26, 2011 13
Agricultural Drought assessment with MODIS Assessment of agricultural drought indices derived from remote sensing 500-m resolution 8-day MODIS surface reflectance product were collected for Texas agricultural areas from year 2000 to 2011 for the growing season. The percentage histogram of each remote sensing index was derived from 2000 to 2011. These distributions were based on valid agricultural pixels. 2011 data were compared with 2010 data and with 10-year average from 2000 to 2009 for two selected period, May 9 to May 17 and July 28 to August 5. 14
The dotted and solid black lines are the percentage histogram of 2011 and 2010 data, respectively and histogram of 2000-2009 mean is presented in red line. Percentage histogram, during May 9 and May 17 for 2000-2009 mean, 2010 and 2011 Compared with 2010 and 10-year mean, 2011 indices variable has significant left shift. The shift amplitude was directly related to sensitivity of indices to drought events. Percentage histogram, during July28 and August 5 for 2000-2009 mean, 2010 and 2011 15
Agricultural Drought assessment with MODIS An indicator with a larger shift provides a better discrimination between drought and non-drought situations. As shown in the figure, for Texas, agricultural area, NDVI, NDII6 and NDII7 have larger statistical dispersion than NDWI and NMDI which is indicative of stronger capability of these three indices to detect drought. 16
Agricultural Drought assessment with MODIS RS Vegetation index LST relationship Universal Triangle Relation between soil moisture, temperature and NDVI (Carlson et al., 1994) 17
Agricultural Drought assessment with MODIS the scatter plots of normalized LST and NDVI during July 28 and August 5, for 2011 and 2000-2009 mean 18
Agricultural Drought assessment with MODIS Using index anomaly for agricultural drought detection The index anomaly ( INDEX) is measured as departure from long-term average for each pixels standardized by the standard deviation: Where INDEXijk is a spectral index for pixeli in a period j for year k; INDEXijk bar is multiyear index average for pixel i during time scale j and σindexijk is standard deviation for pixel i during time scale j 19
Agricultural Drought assessment with MODIS Among five indices, NDVI and NDII7 show comparably stronger capability of drought detection. The anomaly of two indices is measured as 2011 departure from 2000-2010 average for Texas agricultural area. NDVI anomaly 2011 NDII7 anomaly 2011 20
Large part of drought region could be captured on anomaly drought maps. Some regions show discrepancies in drought distribution between anomaly map and USDM map. Moderate drought pattern in Kansas is exaggerated in anomaly maps NDVI anomaly 2011 USDM drought map July 26, 2011 NDVI anomaly 2011 NDII7 anomaly 2011 21
Agricultural Drought assessment with MODIS Combinations of multiple remote sensing indices in drought detection Using two or more indices to describe drought condition is expected to be more effective since local drought signature could be widely captured. NDVI and NDII7 derived from Texas agricultural area from 2011 and 10-year mean were used to test relationship between two indices for drought and non-drought affected years. 22
Summary : This study explores the use of remote sensing in agricultural drought monitoring for central U.S. 1) For Texas agricultural area, NDVI, NDII6 and NDII7 shows comparably stronger capability of drought detection. 1) Central U.S. Agricultural drought pattern can be captured by index anomaly map while discrepancies exist between index anomaly map and USDM map 2) Vegetation index - LST relationship 1) The potential of using multiple drought indices for agricultural drought study. 23
Thank You! 24