Using Time-Series MODIS Data for Agricultural Drought Analysis in Texas

Chunming Peng

GGS Department, George Mason University4400 Univ. Dr, Fairfax, VA. U. S.

cpeng1@gmu.edu

Liping Di

GGS Department, George Mason University4400 Univ. Dr, Fairfax, VA. U. S.

ldi@gmu.edu

Meixia Deng

GGS Department, George Mason University4400 Univ. Dr, Fairfax, VA. U. S.

mdeng@gmu.edu

Ali Yagci

GGS Department, George Mason University4400 Univ. Dr, Fairfax, VA. U. S.

ayagci@gmu.edu

Abstract—In this study time-series VCI data provided by George Mason University’s Global Agricultural Drought Monitoring and Forecasting System (GADMFS) are used for agricultural drought monitoring and forecasting. The validity of using VCI as a primary tool for drought monitoring is supported by the statistical result obtained in this article that the VCI is highly correlated with the PDSI at specific temporal and geospatial resolutions. Three classification schemes for drought severity are discussed here – Fixed Threshold, Natural Breaks (with Jenks) and Quantile schemes. Fixed Threshold Scheme is used though the article because it is computation effective compared with other two, and in the meantime the resulting map proves to be similar to the drought map provided by USDM. The correlation relationships between VCI and PDSI are not identical for different areas in Texas, depending on the vegetation distributions for the specific region. Areas with less variability in vegetation and fewer drought-resistant crops turn out to better reflect the correlations between VCI and PDSI.

Index Terms—time-series, VCI, agricultural drought, MODIS,PDSI, correlation

I. INTRODUCTION

Drought is challenging to monitor accurately for three reasons [7]: 1) droughts are developing slowing (over months and years) and they exist before they are realized to be occurring. 2) A drought’s severity varies by precipitation deficit, spatial extent, and duration, and is difficult to be compared to another drought. 3) The impacts of a drought are based on the range of economic, environmental, and social resources within a region, and are not operational to be quantified into a single index. In recent years remote sensing based methods (mostly based on NDVI) have become a great cure for agricultural drought monitoring, since they serve as more time fashioned and spatially complete data than the traditional station-collected ground truth. Indices such as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Crop Water Starvation Index (CWSI), Normalized Difference Vegetation Index (NDVI), Vegetation Health Index (VHI), and Drought Severity Index (DSI) can all be utilized to determine the duration, severity and impacts of

droughts in the area. In order to meet the increasing demands of decision makers and agricultural markets to commit operational and systematic monitoring in the near real-time basis at continental and global scales, and thus overcome limitations of traditional approaches for currently existing systems, the Global Agriculture Drought Monitoring and Forecasting System (GADMFS) was developed by George Mason University (http://gis.csiss.gmu.edu/GADMFS/

The goal of this research is to use the VCI data provided by GADMFS for agricultural drought monitoring and analyzing in the Texas area, and hence discover the relationship between different drought related indices, and finally to provide information of the sensitivity of vegetation to changes in climatic variables for drought monitoring considerations. It is well known that Kogan[2] used VCI as proxy for estimation of crop yield and pasture biomass in other parts of the world with advance AVHRR-based data. However, this article will perform statistical analysis using MODIS-based data to the special agricultural environment of Texas, and adopt PDSI(Palmer Drought Severity Index) as a reference to drought occurrence and severity in comparison with VCI in different locations and at different spatial units. Later, the VCI will be shown as highly positively correlated with the PDSI for yearly observations.

).

II. STUDY AREA, DATA AND METHODOLOGIES

Due to its large size, Texas’s climate varies widely, from arid in the west to humid in the east. There are several distinct regions within the state which have varying climates: Northern Plains (climate division 1), Trans-Pecos Region (climate division 5), Texas Hill Country, Piney Woods, and South Texas. CD 1 and CD 5 in Texas are selected as our study areas because they are 1) both vulnerable to drought and extreme water deficiency, 2) having clear sky for the majority of the days in growing season, and 3) having obvious wetter and drying months each year. We will analyze the behavior of VCIfor these two divisions together with the entire state of Texas, and verify the VCI curves with the actual occurrences of drought.

In order to derive VCI from MODIS NDVI time series and thus monitor and assess the agricultural droughts happened in Texas from year 2000 to 2012, the MODIS dataset MOD13Q1 of the 13-year long (between day 049 of 2000 and day 113 of year 2012) were downloaded from the NASA’s Land Processes Distributed Active Archive Center (LP DAAC). The MOD13Q1 product is the global vegetation indices dataset with 250m spatial and 16-day temporal resolutions. In the later analysis, we will keep its resolutions and original sinusoidal projection.

Kogan [2] introduced the Vegetation Condition Index as to reflect the extreme changes of the climate, and eliminate the spatial diversification of NDVI, and thus make the vegetation conditions between different regions comparable. Its definition is as below,

Here, is the VCI at time j; is the NDVI value

at time j; is the maximum NDVI of all frames;

is the minimum NDVI of all frames. VCI is an

indicator of “relative greenness”, a percentage value that expresses how green each pixel is in relation to the average greenness over the historical record for a pixel location at a given time [7]. The resulting VCI is then converted from [0, 1] to a range of 0 to 250, with 251 to 254 representing exceptional conditions (e.g. clouds or ice), for which the 8-bit storing format is chosen for VCI values. When it comes to displaying the drought or no drought conditions of an area in a map, assigning every value in the range a unique color is not realistic and visually meaningful. Hence, we borrow U. S. Drought Monitor’s grouping and legend, in which droughts are grouped under different severities: incipient, mild, moderate, severe and exceptional droughts (no drought is another group). Now what we need is a way to divide the VCI values into 6 groups.

.

Fig. 1. Fixed Threshold Scheme (using the per-16-day VCI data retrievedfrom 09/14/2011 to 09/29/2011 for example).

Three classification schemes have been considered: fixed threshold, natural breaks with Jenks optimization, and the quantile schemes. Kogan [2] illustrated that VCI threshold of35% may be used to identify extreme drought conditions and suggested the future research would be necessary to categorize

the VCI by its severity in the range between 0 to 35%. This way of dividing the VCI values at fixed thresholds is proved to be most computation effective, and is most widely adopted by the community. Illustrated as below using the per 16 day VCI data retrieved from 09/14/2011 to 09/29/2011 for example, values from 0 to 20 are defined as in D4 (drought level 4), 21 to 40 defined as in D3, 41 to 60 defined as in D2, 61 to 80 defined as in D1, 81 to 100 defined as in D0, and values above 100 are defined as in the “no drought” level

The natural breaks with Jenks optimization method (shown in Fig. 2) is a data classification method designed to determine the best arrangement of values into different classes. It is done by seeking to minimize each class’s average deviation from the class mean, while maximizing each class’s deviation from the means of the other groups. Though it characterizes the class difference at a fine scale, the computation is very costing compared to the fixed threshold scheme. For this specific image, the 6 groups of VCI values are respectively [0, 8], [9, 38], [39, 65], [66, 77], [78, 103], and [104, 250].

Fig. 2. Natural Breaks Scheme (using the per-16-day VCI data retrieved from 09/14/2011 to 09/29/2011 for example).

The next scheme, Quantile classification (shown in Fig. 3) is a common data classification method that distributes a set of values into groups that contain an equal number of values. This method can be misleading when the number of classes is small but the difference between values is large. For this specific image, the 6 groups of VCI values are respectively [0, 0], [1, 13], [14, 28], [29, 49], [50, 94], and [95, 250].

Fig. 3. Quantile Scheme (using the per-16-day VCI data retrieved from 09/14/2011 to 09/29/2011 for example).

Figure 1 and 2 both look very similar to what the U.S. Drought monitor display on the same days, yet fixed threshold scheme is much more computation effective than the natural breaks method. Thus, in this article, we will use fixed threshold to divide VCI values for drought classification.

Served as an in-situ observation result to validate with the remotely sensed vegetation index, Palmer Drought Severity Index (PDSI), the monthly value (index) to indicate the severity of a wet or dry spell, is fetched from NOAA’s National Climatic Data Center (NCDC). This index is based on the principles of a balance between moisture supply and demand, and in the calculation man-made changes were not considered. The index generally ranges from -6 to 6, with negative values denoting dry spells and positive values indicating wet spells. We will discuss the relationship between VCI and PDSI in later paragraphs.

TABLE I. PDSI VALUES AND DROUGHT CLASSIFICATION

PDSI values Drought Definition

0 to -.5 normal

-0.5 to -1.0 incipient drought

-1.0 to -2.0 mild drought

-2.0 to -3.0 moderate drought

-3.0 to -4.0 severe drought

greater than - 4.0 extreme drought

III. ANALYSIS & RESULTS

NDVI and VCI indices are direct indicators of the vegetation conditions and can act as the accumulators for the drought’s impacts on vegetations. Kogan and other scientists have long used VCI for agricultural drought monitoring.However, there are limitations to the use of time-series VCI that cannot be ignored:

1) The VCI is most useful during growing season because it is a measure of vegetation vigor, when the vegetation is dormant the VCI cannot be used to measure moisture stress or drought.

2) Though VCI is most appropriate for meteorological and agricultural drought monitoring, it is not suitable for comparing current drought conditions with historical droughts because MODIS data is only available since Feb 2000.

3) It is also difficult to compare droughts that occur in different locations because the response of VCI is ecosystem dependent. Thus, the VCI only provides a relative measure of drought conditions and because different locations may have experienced different drought severity since 2000.

If only we can work around these limitations and focus onto studying the behavior of VCI for a specific region across valid time durations, the characteristics of VCI can be interesting and drought reflecting.

A. Relationship between 16-day VCI and monthly PDSI for past 12 months

Judging from the 16-day VCI average for CD1, the extreme drought (D3 and D4) should have happened to the area starting

from April 23rd, 2011 (day 113) and stayed until Jan 15th, 2012. Based on the monthly PDSI values for CD1, extreme drought should have occurred to CD1 when PDSI hit below -4 in May 2011, and stayed in the area until PDSI bounced back to above -4 in Feb 2012. The drought durations obtained using both VCI and PDSI match with each other, and even the onset and close of droughts do not differ much (with only one month’s phase delay for PDSI). Shifting to CD5, the VCI values stayed below 40 from July 12th, 2011 (day 193) to April 22nd, 2012 (day 113), except two outliers at day 001 and day 017 of year 2012. The PDSI values for CD5 remained all the way below -4 from May 2011 to April 2012. For CD5, the drought duration determined by VCI is 2 months shorter than that determined by PDSI.

Fig. 4. 16-day VCI v.s. monthly PDSI for CD1 (up), and CD5 (down) in the past 12 months.

For the state of Texas, the extreme drought (D3 and D4) defined in VCI perspective ranged from April 2011 to December 2011, while the extreme drought defined in PDSI perspective ranged from April 2011 to February 2012. Again, the duration of extreme drought defined in VCI is 2 months shorter than that defined in PDSI. The VCI curve hit the bottom at day 257, 2011 (Sep 14th, 2011) and the PDSI curve reached the lowest in Sep 2011. Also, the VCI curve reached the highest at day 81, 2012 (March 21st, 2012), which coincided with PDSI cresting the curve in March 2012. The two curves have highly similar shapes and trends at the state level. Based on observations of these three comparing charts, we would say that the correlation relationship between 16-day VCI and monthly PDSI is most significant for the state level.

Fig. 5. 16-day VCI V.S. monthly PDSI for Texas (past 12 months).

B. Relationship between yearly VCI average and 12-month PDSI

The first chart in Fig. 6 displays the correlation between yearly VCI average and the 12-month PDSI for CD1 from year 2000 to 2011. The PDSI values of year 2006 and 2011 are lower than -2, signaling moderate droughts or more severe. And the VCI values of year 2000 and 2011 are lower than 80, which based on fixed threshold classification, indicates moderate drought or more severe for years 2000 and 2011. Using a linear regression model, the coefficient of correlation between PDSI and VCI for CD1 is 0.839.

The correlation relationship, between yearly VCI average and the 12-month PDSI for CD5 from year 2000 to 2011, is very obvious from the second chart in Fig. 6. The PDSI values are lower than -2 in years 2000 to 2003 and 2011, while the VCI values are lower than 80 in the exact same years. It is not surprising to find that the correlation coefficient for CD5 is 0.978, signaling a very high positive correlation. For TX, PDSI values are below -2 in years 2000, 2006, 2009 and 2011 while the VCI curve is always higher than 80 except for year 2011. And the correlation coefficient for the area is 0.849. The correlation fails when the VCI values can only tell one year suffering from moderate drought. Because VCI of year i is in fact a relative measure of NDVI of year i compared to the maximum and minimium NDVIs of the valid range, if there are too few of years in the reference library then the resulting VCI can be biased, which is to say the average of all those yearly VCI values will be far different from 100 (the threshold of drought/no drought). Since we have only 12 years of historic VCI (from 2000 to 2011), the VCI values tend to be biased. One simple way to solve the issue is to correct the biase difference. For example, the average of the yearly VCI for TX is 118.056 for year 2000-2011. The fixed threshold of moderate drought/abnormally dry (80) should be added the difference 18.056, and thus the corrected threshold to determine whether the area is suffering from moderate drought is now 98.056. Applying the new threshold to the yearly VCI

average, years 2000, 2006 and 2011 are then drought years (D1, moderate drought).

Fig. 6. Yearly average VCI V.S. 12-month PDSI for CD1, CD5 and TX from 2000 to 2011.

C. Influence of vegetation coverage to the VCI-PDSI correlation

The correlation between the yearly average VCI and the 12-month PDSI for CD5 is highly positive (R=0.978) while coefficient correlation for Texas is 0.849 and that for CD1 is 0.839. What makes CD 5 more sensitive to drought than CD1 and even the state of Texas? One of the reasons is the different vegetation distribution for CD1, CD5 and Texas. According to statistic results from CropScape Data Explorer, there are 20 major types of vegetation for CD5, and shrubland is the most important constituent vegetation (87% of all). For CD1, there are 40 major vegetation kinds, and besides shrubland and grassland herbaceous, cotton (25%) and corn also share significant portions of vegetation. For the entire Texas, there are near 60 major vegetation types, and shrubland and grassland occupy 70% of all vegetation. Since cotton is known for its drought resistance and it constitutes 25% of its entire vegetation, CD1 will be less sensitive to drought occurrences. And because of its complexity in constituent vegetation types, the VCI response for TX is not as sensitive as the VCI for CD5. That is to say, the sensitivity of VCI responding to drought should be listed as: CD5 > CD1 > TX. And this is just the case for the VCI-PDSI correlation relationships we have observed during the past 12 years.

Fig. 7. Pie chart of vegetation distribution for CD1, CD5 and TX (source: CropScape Data Explorer)

D. Impacts of Drought onto crop yields and price

Drought causes loss of crop yields and increase of crop prices. The yearly crop yields can be seen as an indicator for agricultural drought, and shall correlate positively with the VCI values. Years 2000, 2006 and 2011 as shown in Fig. 10 are troughs of the curve while years 2007 and 2010 are at the crest – which matches perfectly when the VCI values reach the troughs and crests.

Fig. 8. Yearly cotton yields for TX from 2000 to 2011

IV. CONCLUSION

The study has shown that there is a high positive correlation between VCI and PDSI, especially for the 12-month average of an area with less variability in vegetation types and fewer drought-resistant crops. On the other hand, the correlation relationship shows that the onset and close of a local drought can be determined using time-series 16-day VCI data. Also whether a specific year is suffering from drought condition can be determined using yearly VCI average. This result is compared against the yearly PDSI and crop yields for the state, which displays a positive correlation as well.

Remote Sensing based methods are proved to be effectively useful when analyzing drought conditions, with which scientists can perform near real-time agricultural drought monitoring at a fine spatial and temporal resolution, can identify sensitive regions which are particular susceptible to agricultural failure arising from month-to-month climate variations, and can estimate crop condition and yield and thus support decision making.

V. FUTURE RESEARCH

In the future, the reference library needs to be extended back into the 1900s in order to have an unbiased baseline for VCI. In the meantime, when choosing study sites, it will be more appropriate to select areas with unique vegetation instead of an area with tens of different vegetation types. Because drought is a natural disaster occurring to large areas, the article has not given any explanation for whether or not it is valid to choose climate division as a unit for drought analysis. More research needs to be done in the future to calibrate the most appropriate geospatial unit for drought. Last but not least, using VCI solely for drought correlation analysis is not enough. In the next phase, we will use Climate Variability Impact Index as another indicator to study the monitoring and assessing of drought.

ACKNOWLEDGMENT

This work is supported by a grant from the National Oceanic and Atmospheric Administration (NOAA) (Grant#: NA09NES4280007, PI: Prof. Liping Di) and a grant from the National Aeronautics and Space Administration (NASA)

(Grant#:NNX09AO14G, PI: Prof. Liping Di).

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