Assessing Drought in Agricultural Area of central U.S...

Assessing Drought in Agricultural Area of central U.S.

with the MODIS sensor

Oct 17th, 2012

Di Wu

George Mason University

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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.

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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...

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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/

Introduction:

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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

Introduction:

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• 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…)

Introduction:

Agricultural drought monitoring with remote sensing

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• 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)

Introduction:

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Different contributions of Remote Sensing for drought monitoring

• Vegetation Status

• Soil moisture

• Temperature

• Evapotranspiration

• Precipitation


Introduction:

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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


Introduction:

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• 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.


Introduction:

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• 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)


Introduction:

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Agricultural Drought assessment with MODIS

U. S drought monitor maps for May 10, and July 26, 2011

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）

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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.

Assessment of agricultural drought indices derived from remote sensing


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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.

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 May 9 and May 17 for 2000-2009 mean, 2010 and 2011

Percentage histogram, during July28 and August 5 for 2000-2009 mean, 2010 and 2011

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• 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.


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RS Vegetation index – LST relationship

Universal Triangle Relation between soil moisture, temperature and NDVI

(Carlson et al., 1994)


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the scatter plots of normalized LST and NDVI during July 28 and August 5, for

2011 and 2000 - 2009 mean


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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


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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


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• 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

NDVI anomaly 2011 NDII7 anomaly 2011

USDM drought map

July 26, 2011

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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.


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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.

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Thank You！

Assessing Drought in Agricultural Area of central U.S...

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