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Impacts of hydrologic soil properties on drought detection with MODIS thermal data
Remote Sensing of Environment 89 (2004) 53 – 62 www.elsevier.com/locate/rse
Impacts of hydrologic soil properties on drought detection with MODIS thermal data Sunyurp Park a,*, Johannes J. Feddema b, Stephen L. Egbert a a
Kansas Applied Remote Sensing Program, University of Kansas, 2101 Constant Avenue, Lawrence, KS 66047, USA b Department of Geography, Lindley Hall, University of Kansas, 1475 Jayhawk Blvd., Lawrence, KS 66045, USA Received 14 July 2003; received in revised form 26 September 2003; accepted 1 October 2003
Abstract Remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), a climatic water budget model, and the STATSGO database were used within a GIS environment to determine the influences of hydrologic soil properties on soil moisture and thermal emission in western – central Kansas for a dry year, 2000. Two important variables, water-holding capacity (WHC) and hydrologic soil group (HSG), were controlled in our water budget experiment to evaluate their impacts on soil moisture content (SMC) changes throughout the period. Results showed that HSG affected drought detection and occurrence very little, but WHC variations explained most local variations of soil moisture content. As a strong indicator of relative soil moisture deficit, the Standardized Thermal Index (STI) patterns were also influenced by WHC. Generally, the earlier the soil moisture content drops below 40%, the earlier the STI reaches a threshold value of 0.2 or higher. Vegetation responses to thermal detection lagged behind the STI by up to 8 weeks, which was computed by comparing the STI and Normalized Difference Vegetation Index (NDVI) deviation from a 10-year mean. The spatial pattern of lag-times was not apparent, but lag-times were correlated with a WHC component. D 2003 Elsevier Inc. All rights reserved. Keywords: MODIS; STATSGO; Thermal emission; Water-holding capacity; Hydrologic soil group; Standardized Thermal Index
1. Introduction Innovations in remote sensing technology have provided new solutions to environmental problems in the earth sciences. In natural hazard monitoring, such as with drought events, remote sensing techniques are especially crucial for timely decision-making because they provide prompt geospatial data and a better spatial footprint of environmental phenomena characterizing an entire area compared to point locations, such as weather stations (Kogan, 2000; Legates, 2000; Unganai & Kogan, 1998). Formulated from remote sensing data, the Normalized Difference Vegetation Index (NDVI) is a well-known measure linked to biophysical variables and has been widely used for vegetation monitoring. However, NDVI is not always an appropriate tool for ‘‘real-time’’ drought monitoring. Due to a lagged vegetation response to drought, NDVI cannot detect drought events * Corresponding author. Tel.: +1-785-864-1524; fax: +1-785-8641534. E-mail address: [email protected] (S. Park). 0034-4257/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2003.10.003
instantaneously (Lozano-Garcia, Fernandez, Gallo, & Johannsen, 1995; Peters, Rundquist, & Wilhite, 1991; Reed, 1993; Rundquist & Harrington, 2000). Earlier drought detection may be possible using thermal emission patterns from remote sensors. Land surface temperature (LST) is an important biophysical indicator because it is directly linked to the net radiation flux and surface moisture conditions. Interacting with the soil – plant –air system, LST represents the instantaneous state of the energy flux for a land surface. Therefore, spatially and temporally well-sampled surface temperatures should theoretically help identify waterstressed vegetation canopies. It is believed that by using thermal emission patterns in combination with meteorological observations, the relationship between surface temperature and the moisture regime on the ground will detect drought areas before biomass degradation occurs. With high radiometric and temporal resolution, thermal infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS) allow us to more accurately infer changes in surface thermal regimes and assist in improved drought detection.
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Drought is a temporary condition resulting from prolonged absence, deficiency, or poor distribution of precipitation (Ogallo, 1994; Wilhite, 1993). Although precipitation is the main controlling factor in drought events in the central Great Plains region (Wang, 2000), vegetation growth is dependent upon a number of additional environmental factors, such as high temperature, high winds, low soil moisture content, or low relative humidity. In particular, hydrologic soil properties play an important role in affecting vegetation growth (De Jong, Shields, & Sly, 1984; Farrar, Nicholson, & Lare, 1994; Nicholson & Farrar, 1994; Timlin, Loechel, Pachepsky, & Walthal, 2001). The availability of soil moisture for plant uptake is influenced by soil type, soil texture, water-holding capacity, infiltration rate, etc. These characteristics determine how long water will be held in soil layers and how much water will be held there, and they control changes in soil moisture content (SMC). Among the soil properties that affect vegetation growth, water-holding capacity (WHC) is an important factor for plant productivity. Since the total amount of water available for plant growth in a field is a function of the depth and WHC of the soil, WHC is considered one of the most influential hydrologic soil variables in calculating the amount of water storage in a soil profile (Brady & Weil, 2002; De Jong et al., 1984; Timlin et al., 2001; Wright, Boyer, Winant, & Perry, 1990). Estimation of SMC is often done with the use of water budget models. The initial step in a water budget model is the calculation of potential evapotranspiration. Empirical methods for estimating potential evapotranspiration have proven useful and are generally accepted because they require only limited climatic data (Thornthwaite, 1948; Thornthwaite & Mather, 1955, 1957). Although these empirical methods are often less accurate than other, more complicated, methods, their reliance on few data inputs has proven advantageous for large-scale applications, and this outweighs their inability to reflect short-period changes in wind or humidity conditions (Mather, 1978). The advantage of using a water budget model is that it integrates various environmental aspects in calculating soil moisture content, such as WHC, hydrologic conditions, and land use. Therefore, by controlling one of these input variables, the theoretical influence of the variable on soil moisture conditions can be evaluated with a climatic water budget model. The objective of this study is to determine the influences of hydrologic soil characteristics on early detection of drought with MODIS thermal emission data and on the response time of NDVI to the thermal signals. Although a few previous studies have shown that NDVI responses lag behind antecedant precipitation by up to 3 months (Davenport & Nicholson, 1993; Farrar et al., 1994; Justice, Holben, & Gwynne, 1986; Malo & Nicholson, 1990; Nicholson & Farrar, 1994; Wang, 2000; Wang, Price, & Rich, 2001), no documentation has been observed regarding NDVI’s response time to satellite thermal signals and the impact of hydrologic soil factors on the lag-time. In recent research
conducted in Kansas, Wang et al. (2001) suspected that soil moisture might affect NDVI patterns in the Konza Prairie, but they did not find a rigorous relationship due to varying lag-times between soil moisture and NDVI. A hydrologic approach to this question may help incorporate hydrologic soil attributes into better drought monitoring schemes in the future.
2. Methodology 2.1. Study area Western and central Kansas were selected as the study areas because of frequently occurring droughts and their potential impact on the local agricultural economy and natural grassland/rangeland management practices (Fig. 1). Due to rapid conversion from grassland to cropland or rangeland since the late 1800s, the region’s economy is based upon grazing, feeding of livestock, dryland farming, and increasingly, irrigated agriculture. Due to limited water supplies and water deficits resulting from high levels of evapotranspiration, the agricultural economy of western and central Kansas is very susceptible to drought conditions, and as a result has become dependent upon the High Plains aquifer as a water resource. Water availability from the aquifer has transformed much of the semi-arid shortgrass prairie region into one of the largest irrigated agricultural regions in the nation (Kromm & White, 1992). Since this region has relatively flat topography and is mostly vegetated by grasses or crops, the thermal signals from the surface are assumed to be relatively uncontaminated by slope, aspect, or shadows.
Fig. 1. Study area includes western and central parts of Kansas. The locations of weather stations are shown as solid squares, and internal lines represent boundaries of climatic divisions for the state.
S. Park et al. / Remote Sensing of Environment 89 (2004) 53–62
2.2. Satellite and meteorological data 2.2.1. MODIS thermal infrared data LST data from MODIS were obtained from the USGS EROS Data Center, Sioux Falls, South Dakota. Since LST data before July 2000 were not available, only data acquired from July to October 2000 were analyzed in this study. In all, 67 daily MODIS LST images were obtained for the study area. To synchronize LST data with weekly NDVI values and to avoid cloud interference, the daily LST values were combined into weekly composite data with the same weekly interval as the NDVI data set. In creating weekly composites, the highest LST in each weekly period was included because it has been found that the maximum temperature composite data most closely represented land surface conditions (Cihlar, Manak, & D’iorio, 1994; Park, Feddema, & Egbert, 2002a; Park, Feddema, & Egbert, 2002b). It was hypothesized that areas suffering from a water shortage would have lower NDVI values and higher temperature gradients between the land surface and the air compared to drought-free areas. Therefore, a LST deviation from air temperature, defined as LST – air temperature, was expected to have an inverse relationship with soil moisture and evapotranspiration, but a positive relationship with moisture deficit. LST deviation is an adequate indicator of surface moisture conditions because it is believed to have a linear inverse relationship with vapor pressure deficit of air, and a positive relationship with sensible heat flux, which increases as water is restricted (Choudhury, 1989; Ehrler, 1973; Idso, Jackson, Ehrler, & Mitchell, 1977; Idso, Jackson, Pinter, Reginato, & Hatfield, 1981; Inoue & Moran, 1997; Jackson, Idso, Reginato, & Pinter, 1981; Jackson, Reginato, & Idso, 1977; Lhomme & Monteny, 1993; Moran & Jackson, 1991; Moran, Humes, & Pinter, 1997; Moran et al., 1996; Troufleau et al., 1997; Yang, Zhou, & Melville, 1997). In a previous study, time-integrated, or cumulative LST deviations had significant negative relationships with soil moisture content (SMC) and the actual/potential evapotranspiration ratio (AE/PE), while they had a positive relationship with the moisture deficit/potential evapotranspiration ratio (MD/PE) (Park et al., 2002a,b). This indicated that LST deviation increased with soil moisture deficit. A formulated index, called the Standardized Thermal Index (STI), was proposed to rescale the LST deviations from 0 to 1. The STI is defined as the cumulative mean of LST standardized with mean air temperature ([LST meanTair]cum) divided by the cumulative mean of the sum of the LST and the mean air temperature ([LST + meanTair]cum): STI ¼ ½LST meanTair cum =½LST þ meanTair cum It was found that STI values of 0.2 corresponded to 15% or more decline in NDVI (Park, 2003). Therefore, the value
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of 0.2 was considered as a threshold for thermal detection of drought. 2.2.2. NDVI Weekly AVHRR maximum-NDVI composite data for the growing season (March – October) in 2000 and 10-year (1990 –1999) weekly mean NDVI data were obtained from archives at the Kansas Applied Remote Sensing (KARS) Program at the University of Kansas. To measure NDVI degradation in the dry year 2000, NDVI departures from the 10-year mean were computed for each week. The NDVI departure, or drought severity, is defined as the target NDVI value minus the 10-year mean NDVI for each week as follows: Drought severity ¼ NDVI2000 10 -year mean NDVI Positive departure values represent above-normal vegetation growth, whereas negative ones represent below-normal conditions. Oftentimes, surface thermal emission data and hydroclimatological parameters do not have a significant relationship with NDVI over short periods of time because plant response to changes in a soil moisture regime may not be instantaneous. Once drought severity was computed from the NDVI data, image pixels with significant NDVI decreases were identified to determine how long it took for vegetation to experience abnormal degradation following a thermal indication of drought. Since hydrologic soil properties control the amount and duration of soil moisture storage, they were expected to have a significant relationship with the lag-time. 2.2.3. Meteorological data Meteorological data including daily maximum and minimum air temperatures and precipitation data for the year of 2000 were acquired from the High Plains Regional Climate Center (HPRCC) at the University of Nebraska. Air temperature was interpolated between observations from weather station networks using an inverse-distance weighted method. 2.3. Analysis 2.3.1. Water budget experiment In addition to WHC, hydrologic soil group (HSG), which represents infiltration rates of soils, is another important hydrologic input variable required for running a water budget program coupled with a runoff model. These variables are readily available from the State Soil Geographic (STATSGO) database (USDA, 1995). The HSG for each map unit, the fundamental graphic feature in STATSGO, falls into one of four major groups, ranked A (high infiltration rates) through D (very slow infiltration rates) on the basis of their runoff potential (Mather, 1978; SCS, 1972). To determine the impacts of hydrologic soil properties on soil moisture changes, two main factors, namely WHC and
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HSG, were selected as control factors. By comparing simulated results based on these variables with satellitederived measures of drought, the influence of soil factors on drought can be evaluated. Our preliminary study showed that SMC decreased to 40% or less of WHC before NDVI declined below a normal value (Park, 2003). If the hydrologic soil factors affect the timing and duration of the soil moisture threshold, it is expected that the thermal emission pattern will be correlated with the spatial pattern of the time required for soil moisture to fall below 40% of WHC, or with the duration that conditions below this threshold persist. These two aspects of soil moisture changes were considered in terms of Julian day calculations: (1) the time required for soil moisture conditions to fall below 40% was represented by the Julian day when soil moisture content (SMC) first declined below this threshold and (2) the duration of drought was represented by the number of succeeding days where SMC stayed below the 40% threshold. Spatial representation of the influences of the hydrologic soil factors on these two measurements was performed by running four different water budget experiments at the 18 weather stations (Fig. 1). These experiments were based on different combinations of the two soil factors as follows: 1. A standard condition: Used a standard WHC of 150 mm and the most dominant HSG in the study area, group B, for water budget calculations at all locations. 2. HSG controlled: Used observed WHC at each station and the dominant HSG B for all locations. 3. WHC controlled: Used a standard WHC of 150 mm at all locations and observed HSG at each station. 4. Observed: Used observed values for both WHC and HSG at each station. For each experiment, the first Julian day when SMC dropped below 40% of WHC, and the number of succeeding days where SMC stayed below this threshold were calculated. An inverse-distance weighted interpolation method was performed to create the maps of these variables. The impacts of WHC and HSG on SMC can then be evaluated in two ways. First, maps from the resulting four experiment conditions can be compared to each other to determine which of these two factors contributes most to the observed conditions. The other simple way to determine the influence of WHC and HSG on soil moisture changes is to calculate deviation values of experiments (2) and (3) from the standard condition. These two deviation measures were termed ‘WHC component’ and ‘HSG component,’ respectively. This differencing method shows which factor affects soil moisture conditions most and where that factor is most influential across the landscape. It was also hypothesized that the arrival time and the frequency of the threshold SMC were closely correlated with thermal emission.
2.3.2. Hydrologic soil factors and surface thermal emission If hydrologic soil factors affect surface thermal emission, it is expected that thermal emission patterns will resemble the spatial patterns of the first Julian day when SMC declined to 40% and the number of succeeding days below the threshold SMC of 40%. For this comparison, two additional STI maps were created and compared to the water budget experiment maps. First, using weekly STI maps, the first week when the STI reached 0.2 was identified for each image pixel. Second, the number of weeks when the STI was 0.2 or higher was also computed for each image pixel. These two maps represented how early the thermal emission signal reached the threshold and how long it persisted above the threshold. Two evaluation methods were used to compare these STI maps with the water budget experiment maps. First, a comparison was conducted based on the descriptive statistics of the experiment maps and their relationships with STI values in terms of the time when STI first reached 0.2 and the number of weeks where STI stayed 0.2 or higher. Mean, median, and standard deviation values for each STI level, which were categorized with an interval of 0.05, were computed for both the arrival time and the frequency of STI 0.2. Additionally, pixel-to-pixel-based correlation coefficients were computed between the STI maps and the water budget experiment maps. The temporal correlation of WHC and HSG components with STI was also determined using the deviations of experiments (2) and (3) from the control (1) with respect to the Julian day when SMC first reached 40% for each composite period. 2.3.3. Vegetation response time and WHC It has been demonstrated that WHC controls the amount and time of soil moisture release, mainly by soil texture and pore space (Brady & Weil, 2002). Generally, the lower the WHC the greater the runoff, and the quicker drought sets in and the longer the duration of drought. Based on these observations, we hypothesize that there should be a relationship between soil WHC and the lag-time of vegetation response to thermal signals. To test this hypothesis, the time lag between the detection of thermal signals indicating drought and the time of plant response to drought was calculated. Time of onset of drought was determined from the first of occurrence of STI values of 0.2 or higher. Knowing that this threshold STI was followed by 15% or greater declines in NDVI declines below a 10-year mean (1990 – 1999), a conservative value of a 20% decline of NDVI below normal was used to determine how long it took for plants to respond to the thermal drought indication for each pixel on a weekly basis. A map representing this lag-time was compared against the water budget derived Julian day when SMC reached 40% and the number of days where SMC remained below 40% for both the standard and the HSG-controlled conditions to see if the lag-time was correlated with precipitation and the WHC factor. In addition, to avoid possible interference by precipitation on the correlation, a deviation map of the days to
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reach 40% SMC and duration below the 40% threshold was derived by subtracting the standard condition from the HSG-controlled condition. Since WHC is the only condition changed in the HSG-controlled condition, the deviation values represent the effect of WHC on temporal changes in the SMC threshold. Correlation coefficients were calculated between the lag-time and Julian day of WHC 40% and the number of days below WHC 40% for the standard condition, the HSG-controlled condition, and the deviation of the HSG-controlled condition, respectively.
3. Results and discussion 3.1. Water budget experiment 3.1.1. Soil moisture content changes The results from the experiment showed that, for the year 2000, the WHC-controlled case was very similar to the standard condition, and the observed case was almost identical to the HSG-controlled case for both the Julian day when WHC first reached 40% and the number of succeeding days where SMC was below 40%. This suggests that WHC has a significant impact on drought onset and duration while HSG has relatively little influence on drought conditions. The spatial pattern of the standard condition clearly showed that there was a northwest – southeast precipitation-deficit gradient with higher precipitation in the northwest. This can be surmised because WHC and HSG were standardized across the study area, and therefore,
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precipitation is the main factor influencing water balance conditions. This result somewhat deviates from the normal annual precipitation pattern, which typically has an increasing west –east gradient (Goodin, Mitchell, Knapp, & Biven, 1995). Estimated SMC reflected this pattern. St. Francis, a northwestern site, was an extreme case for the wet condition, where SMC did not drop below 40% until mid-August. In contrast, McPherson, a central site, represented very dry conditions, where SMC reached 40% in mid-May (Fig. 2, locations are shown in Fig. 1). The number of days with SMC of 40% or lower was greatest in the northwestern area (130 days or more), while the south – central area had the fewest number of days (70 days or less, Fig. 3). Wang et al. (2001) recently confirmed that precipitation is a strong predictor of spatial patterns of NDVI in Kansas. Since SMC is strongly governed by precipitation, this is the primary factor affecting drought. However, changes in SMC due to internal soil factors may play an important secondary role in drought susceptibility, and this additional information could improve drought monitoring at specific locations. Overall, hydroclimatological parameters, including SMC, AE/PE, and MD/PE, had statistically significant correlations with STI. Their mean correlation coefficients calculated at six major weather stations across the study area were 0.64, 0.65, and 0.66, respectively (Table 1). 3.1.2. Impacts of WHC and HSG Since the observed pattern of the first Julian day of SMC 40% corresponded well with the HSG-controlled experiment, WHC is believed to be a dominant hydrologic
Fig. 2. Climatic water budget experiment setting for Julian day of WHC 40%. (a) The standard condition, (b) HSG-controlled at group B, (c) WHC-controlled at 150 mm, and (d) WHC and HSG are observed values. Numbers represent the Julian day when WHC 40% is reached.
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Fig. 3. Climatic water budget experiment setting for the number of days below WHC 40%: (a) the standard condition, (b) HSG-controlled at group B, (c) WHC-controlled at 150 mm, and (d) WHC and HSG are observed values. Numbers represent the number of days when WHC was lower than 40%.
factor in affecting SMC. A map of the WHC component illustrated that, in the southwestern region of the study area, SMC reached 40% about 10 to 20 days later than the standard condition due to low WHC. In contrast, it took 40 – 80 days longer than the standard condition in the central region to reach the same SMC level due to relatively high WHC (Fig. 4a). The observed pattern of the number of days where SMC fell below 40% was very similar to that of the HSGcontrolled case (Fig. 4b). This confirmed that WHC played a role in changing SMC. The WHC component map also showed that the southwestern region did not deviate much from the standard condition, having less than a 30-day difference from the standard condition due to low WHC. The most affected areas were in the northern part of the state. The high WHC of these areas made the soil layers hold more water and have fewer days with dry soils compared to the theoretical standard condition. This characteristic made a large difference in the number of days with
SMC of 40% or lower between the standard and the HSGcontrolled experiment. In the central region, despite a relatively high WHC, the distribution of SMC simulated with the observed WHC was not different significantly from that computed with the standard WHC of 150 mm. The weaker impact of WHC on this region was caused by the fact that the central region had several significant rainfall events during the growing season. This resulted in there being little difference in the number of days where SMC was under 40% between the standard and the HSG-controlled conditions. In other words, the rainfall events may not have allowed soil moisture levels to drop below 40% very often, even for the shallow standard WHC condition (150 mm). 3.2. Hydrologic factor –thermal signal relations Because there were no thermal data available before week 28, the relationship between the arrival time of STI
Table 1 Relationships between the Standardized Thermal Index and three hydroclimatological parameters Relationships
Dodge City
Goodland
Healy
Salina
Washington
Wichita
Meany
STI:SM STI:AE/PE STI:MD/PE Meany
0.82** 0.85** 0.81** 0.83
0.71** 0.73** 0.74** 0.73
0.77** 0.77** 0.81** 0.78
0.50* 0.50* 0.51* 0.50
0.49* 0.50* 0.52* 0.50
0.57* 0.58* 0.57* 0.57
0.64 0.65 0.66 0.65
* Significant at the 0.05 level. ** Significant at the 0.01 level. y Mean values of absolute numbers of correlation coefficients.
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Fig. 4. Maps of the WHC component, the deviation of the HSG-controlled experiment from the standard condition. They are expressed by the first Julian day of SMC 40% (a) and the number of days where SMC stayed below 40% (b). Numbers describe how the WHC component has changed the first Julian day of SMC 40% and the number the days where SMC stayed below 40% compared to the standard condition.
0.2 and the first Julian day when SMC fell below 40% was not fully analyzed. However, descriptive statistics of areas with different weekly arrival times of STI 0.2 show that the chronological order of STI 0.2 conforms most closely to the observed Julian day (mean and median days); and the standard deviation of the observed Julian day for each weekly STI class is smaller than that of the standard condition. The chronological order of locations where STI first reached 0.2 and SMC first reached 40%
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agreed well with each other. Their correlation coefficients were 0.62 for the standard condition, 0.64 for the observed case, and 0.47 for the WHC component (Table 2). These results demonstrated that the arrival of STI 0.2 was more similar to that of the first observed Julian day of SMC 40%, indicating that WHC was a factor for the time of thermal detection of drought conditions. Pixel-to-pixel correlation coefficients also showed that the first week of STI 0.2 was more strongly correlated with the observed Julian day of SMC 40% (r = 0.49) compared to the standard condition (r = 0.31), demonstrating that the time when STI reaches 0.2 is influenced by soil hydrologic factors in addition to precipitation. Comparing the number of weeks where the STI was 0.2 or higher with the experiment results, it is clear that the spatial distribution of the STI parameter resembles that of the number of days below SMC 40% (Fig. 5). Areal mean and median values of the observed number of days with SMC of 40% or lower had stronger correlations with the number of weeks with STI 0.2 or higher (r = 0.96 and 0.92) compared to those of the standard condition (r = 0.96 and 0.89). The WHC component also had a significant correlation with the number of days of STI value of 0.2 or higher (Table 3). A pixel-to-pixel correlation analysis showed that the observed number of days with SMC of 40% or lower had the highest correlation with the number of weeks where the STI was 0.2 or higher (r = 0.67) followed by the standard condition (r = 0.40) and the WHC component (deviation from the standard condition, r = 0.30). From these results, it is demonstrated that the STI threshold had correlations with the different experimental conditions in the decreasing order of observed>standard>WHC component>HSG component (not significant). A negative relationship between the WHC component and the STI indicated that those areas with a relatively low WHC reached the threshold SMC earlier and had higher STI values compared to areas with high WHC. During the growing season, the mean correlation coefficient between STI (0.2 or higher) and WHC was 0.62 (range, 0.39 to 0.72). However, this value was not as high as that
Table 2 The relationship between the week when the STI reached 0.2 and the Julian day when SMC declined to 40% for the standard condition, the observed case, and deviation from the standard condition. Mean, median, and standard deviation of Julian day are calculated for each weekly category when the STI reached 0.2. An NDVI deviation from a 10-year mean is also shown for each week Week Std_mean Std_med S.D. Obs_mean Obs_med S.D. Dev_mean Dev_med S.D. NDVI** 28 30 34 35 36 37 38 r*
124.6 131.3 122.0 125.5 132.1 138.2 148.0 0.62
129 131 129 130 132 140 146 0.69
17.6 13.3 20.8 16.5 14.5 14.3 18.8 –
150.5 164.2 153.3 156.1 162.3 167.2 181.6 0.64
148 165 156 158 162 166 179 0.69
* Pearson product-moment correlation coefficient. ** NDVI deviation from the mean in period 38, 9/15/00 – 9/21/00 (%).
13.2 12.9 15.2 12.7 13.3 16.3 18.4 –
26.7 33.3 31.9 31.3 31.1 29.7 34.0 0.47
34 36 45 37 32 31 36 0.10
10.7 6.2 10.9 8.7 7.5 9.1 8.7 –
15.7 22.6 16.0 18.0 13.6 11.1 11.0 0.68
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Fig. 5. The number of weeks for which the STI was 0.2 or higher from week 28 (July 7 – 13) to week 38 (September 15 – 21). Fig. 6. Relationships between STI for each composite period and NDVI declines in mid-September.
between thermal emission and precipitation (r = 0.72). Therefore, it is concluded that, as expected, thermal signals for land surface conditions are partly influenced by soil WHC in addition to the primary control by precipitation. 3.3. Lag-time between STI and NDVI Mean STI values for each STI class ranged from 0.03 to 0.31 during the study period. As expected, these STI values had significant positive relationships with NDVI deviations in each composite period, and the correlation coefficients increased from 0.85 to 0.98 as time progressed. These results clearly show that higher STI correlates with more severe drought. This relationship in each composite period is represented in Fig. 6, and an overall regression line was fit to the data. Comparing class mean values of STI and NDVI deviations, STI values higher than 0.2 corresponded to 15% or greater declines in NDVI in September. Lag-times of up to 8 weeks were calculated. An apparent spatial pattern was not observed in the lag-time distribution, but patches of pixels with short lag-times were found in the
southern, western, and upper central parts of the state, and relatively longer lag-times in northwestern areas (Fig. 7). Study results revealed that the lag-time between STI and NDVI was significantly correlated with precipitation. For the standard condition, the correlation coefficient between the lag-time and the first Julian day of SMC 40% (or lower) was r = 0.58, and that between the lag-time and the number of days with SMC 40% (or lower) was r = 0.60. Since the lag-time became longer as the first Julian day of SMC 40% started earlier or the number of days below the threshold value increased, the lag-time is believed to be inversely related with precipitation. The observed deviation map of first Julian day had a positive relationship (r = 0.49) with the lag-time. This means that as the first Julian day with SMC of 40% was observed later than that of the standard condition, or as WHC increased, the lag-time became longer. Conversely, the deviation map of the number of days below the SMC 40% threshold had a negative relationship (r = 0.68) with the lag-time. This means that as the observed number of
Table 3 The relationship between the number of weeks for which the STI was 0.2 or higher and the number of days below SMC 40% for the standard condition, the observed case, and deviation from the standard condition. Mean, median, and standard deviation of the number of days below SMC 40% are calculated for each class of the number of weeks where the STI remained 0.2 or higher. An NDVI deviation from the mean is also shown for each class No. of weeks Std_mean Std_med S.D. Obs_mean Obs_med S.D. Ht_mean Ht_med S.D. NDVI** 1 2 3 4 5 6 7 r*
123.9 134.0 140.8 142.3 144.0 147.9 151.9 0.96
128 134 141 140 138 143 155 0.89
22.8 18.4 17.2 18.4 19.6 19.3 16.3 –
86.9 94.8 98.2 101.9 105.3 109.6 124.9 0.96
* Pearson product-moment correlation coefficient. ** NDVI deviation from the mean in period 38, 9/15/00 – 9/21/00 (%).
90 94 97 99 102 109 126 0.92
17.0 15.4 12.5 13.2 14.5 16.1 12.5 –
39.1 42.5 45.5 43.1 41.3 40.5 28.8 0.56
39 42 44 40 37 38 29 0.73
11.9 13.9 15.6 17.6 18.4 18.2 14.2 –
11.7 10.9 13.7 14.3 14.7 18.7 17.3 0.92
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with soil WHC. Since soil WHC is soil depth-weighted, those areas that had deep soils and lost soil moisture more slowly due to high WHC had a relatively longer lag-time compared to locations with low WHC. These relationships among hydrologic soil properties, threshold SMC, and satellitederived thermal drought detection have important implications for early detection of drought. Possible misleading thermal signals for soil moisture conditions due to rainfall events with varying amounts and frequencies during a drought will be minimized by using threshold soil moisture levels and soil WHC as supplementary references to drought in addition to the thermal emission pattern. Fig. 7. Lag-time between STI and NDVI deviations from a 10-year mean (1990 – 1999).
days where SMC stayed below 40% became smaller than that of the standard condition, or as WHC increased, the lagtime increased. Both of these results indicated that the lagtime was positively correlated with soil WHC. Actual total WHC had a similar correlation with the lag-time with r = 0.55 (significant at the 0.1 level). As indicated by the correlation coefficients, the relationship between the lagtime and WHC was better described by using the number of days below the threshold compared to the first Julian day of SMC 40%. This is probably because (1) WHC is a soil depth-weighted variable, (2) the total column of soil functions as a buffer throughout the growing season, and (3) a soil –NDVI response relationship can change according to the hydrologic characteristics of soils (Farrar et al., 1994).
4. Conclusions Remotely sensed thermal infrared data, the STATSGO database, and a climatic water budge model were effectively integrated using GIS tools to determine the influence of hydrologic soil characteristics on SMC, thermal emission from land surfaces, and NDVI’s response time to the thermal signals. As secondary factors in controlling soil moisture content, hydrologic soil properties were analyzed to determine how they contributed to soil moisture changes. Study results showed that although precipitation governed overall soil moisture conditions, soil WHC had additional impacts. It was observed that higher WHC made SMC reach its threshold value more slowly and experience fewer days of soil moisture falling below the 40% of WHC threshold. The spatial patterns of the STI, in both the first time when STI first reached 0.2 and the number of weeks with STI values of 0.2 or higher, were also correlated with WHC. WHC also played a role in thermal drought detection. Low WHC makes SMC decrease to the threshold level earlier, and therefore, it increased surface temperatures more quickly. NDVI’s response time to the thermal signals was also affected by soil WHC. This lag-time turned out to be positively correlated
Acknowledgements This work was supported by NASA Headquarters under an Earth System Science Fellowship Grant NGT5-30404. The Kansas Applied Remote Sensing (KARS) Program at the University of Kansas provided image processing facilities. We thank the NASA Goddard Space Flight Center, Greenbelt, MD, and the USGS Eros Data Center, Souix Falls, SD for providing us MODIS data. We also thank the High Plains Regional Climate Center at the University of Nebraska-Lincoln for allowing us to use climatic data for the study area. References Brady, N. C., & Weil, P. R. (2002). The nature and properties of soils ( pp. 206 – 210). Upper Saddle River: Prentice Hall. Choudhury, B. J. (1989). Estimating evaporation and carbon assimilation using infrared temperature data: Vistas in modelling. In G. Asrar (Ed.), Theory and applications of optical remote sensing. New York: Wiley. Cihlar, J., Manak, D., & D’iorio, M. (1994). Evaluation of compositing algorithms for AVHRR data over land. IEEE Transactions on Geoscience and Remote Sensing, 32, 427 – 437. Davenport, M. L., & Nicholson, S. E. (1993). On the relation between rainfall and the Normalized Difference Vegetation Index for diverse vegetation types in East Africa. International Journal of Remote Sensing, 14, 2369 – 2389. De Jong, R., Shields, J. A., & Sly, W. K. (1984). Estimated soil water reserves applicable to a wheat-fallow rotation for generalized soil areas mapped in southern Saskatchewan. Canadian Journal of Soil Science, 64, 667 – 680. Ehrler, W. L. (1973). Cotton leaf temperatures as related to soil water depletion and meteorological factors. Agronomy Journal, 65, 404 – 409. Farrar, T. J., Nicholson, S. E., & Lare, A. R. (1994). The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana: II. NDVI response to soil moisture. Remote Sensing of Environment, 50, 121 – 133. Goodin, D. G., Mitchell, J. E., Knapp, M. C., & Biven, R. E. (1995). Climate and weather atlas of Kansas—an introduction. Educational series, vol. 12. Lawrence: Kansas Geological Survey. Idso, S. B., Jackson, R. D., Ehrler, W. L., & Mitchell, S. T. (1977). Infrared emittance determination of leaves. Ecology, 50, 899 – 902. Idso, S. B., Jackson, R. D., Pinter Jr., P. J., Reginato, R. J., & Hatfield, J. L. (1981). Normalizing the stress-degree-day parameter for environmental variability. Agricultural Meteorology, 24, 45 – 55. Inoue, Y., & Moran, M. S. (1997). A simplified method for remote sensing of daily canopy transpiration—a case study with direct measurements of
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canopy transpiration in soybean canopies. International Journal of Remote Sensing, 18, 139 – 152. Jackson, R. D., Idso, S. B., Reginato, R. J., & Pinter Jr., P. J. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research, 17, 1133 – 1138. Jackson, R. D., Reginato, R. J., & Idso, S. B. (1977). Wheat canopy temperature: A practical tool for evaluating water requirements. Water Resources Research, 13, 651 – 656. Justice, C. O., Holben, B. N., & Gwynne, M. D. (1986). Monitoring East African vegetation using AVHRR data. International Journal of Remote Sensing, 7, 1453 – 1474. Kogan, F. N. (2000). Contribution of remote sensing to drought early warning. In D. A. Wilhite, & D. A. Wood (Eds.), Early warning systems for drought preparedness and drought management ( pp. 75 – 87). Geneva: World Meteorological Organization. Kromm, D., & White, S. (1992). Groundwater issues in the high plains. Lawrence: University Press of Kansas. Legates, D. R. (2000). Remote sensing in hydroclimatology: An introduction. The Professional Geographer, 52, 233 – 234. Lhomme, J. P., & Monteny, B. (1993). Estimates of convective fluxes over sparse canopy from infrared temperature. In H. J. Bolle, R. A. Feddes, & J. D. Kalma (Eds.), Exchanges processes at the land surface for a range of space and time scales. Yokohama: Proceedings of International Association of Hydrological Sciences, Japan, July 13 – 16. IAHS Publication, vol. 212 (pp. 437 – 443). Lozano-Garcia, D. F., Fernandez, R. N., Gallo, K. P., & Johannsen, C. J. (1995). Monitoring the 1988 severe drought in Indiana, U.S.A. using AVHRR data. International Journal of Remote Sensing, 16, 1327 – 1340. Malo, A. R., & Nicholson, S. E. (1990). A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index. Journal of Arid Environments, 19, 1 – 24. Mather, J. R. (1978). The climatic water budget in environmental analysis. Washington, DC: DC Heath and Company. Moran, M. S., & Jackson, R. D. (1991). Assessing the spatial distribution of evapotranspiration using remotely sensed inputs. Journal of Environmental Quality, 20, 725 – 737. Moran, M. S., Humes, K. S., & Pinter Jr., P. J. (1997). The scaling characteristics of remotely-sensed variables for sparsely-vegetated heterogeneous landscapes. Journal of Hydrology, 190, 337 – 362. Moran, M. S., Rahman, A. F., Washburne, J. C., Goodrich, D. C., Weltz, M. A., & Kustas, W. P. (1996). Combining the Penman – Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland. Agricultural and Forest Meteorology, 80, 87 – 109. Nicholson, S. E., & Farrar, T. J. (1994). The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana: I. NDVI response to rainfall. Remote Sensing of Environment, 50, 107 – 120. Ogallo, L. A. (1994). Drought and desertification: An overview. Bulletin of the World Health Organization, 43, 18 – 22. Park, S. (2003). Determination of MODIS thermal response patterns from land surfaces and their relationship with climatic factors for drought monitoring in Central Great Plains. Unpublished dissertation. Lawrence, KS: University of Kansas. Park, S., Feddema, J. J., & Egbert, S. L. (2002a). Hydroclimatological
parameters and their relationship with land surface temperatures and NDVI anomalies. Proceedings, ACSM-ASPRS Conference and Technology Exhibition, April 19 – 26. Washington, DC: ASPRS. Park, S., Feddema, J. J., & Egbert, S. L. (2002b). Thermal response patterns of MODIS from land surfaces and their coupling with the climatic water budget in western – central Kansas. Proceedings, Thermosense XXIV, 16th Annual International Symposium on AeroSense, April 1 – 4. Orlando: SPIE. Peters, A. J., Rundquist, D. C., & Wilhite, D. A. (1991). Satellite detection of the geographic core of the 1988 Nebraska drought. Agricultural and Forest Meteorology, 57, 35 – 47. Reed, B. C. (1993). Using remote sensing and Geographic Information Systems for analysing landscape/drought interaction. International Journal of Remote Sensing, 14, 3489 – 3503. Rundquist, B. C., & Harrington Jr., J. A. (2000). The effects of climatic factors on vegetation dynamics of tallgrass and shortgrass cover. Geocarto International, 15, 31 – 36. Thornthwaite, C. W. (1948). An approach toward a rational classification of climate. Geographical Review, 38, 55 – 94. Thornthwaite, C. W., & Mather, J. R. (1955). The water balance. Publications in Climatology, 8, 8 – 104. Thornthwaite, C. W., & Mather, J. R. (1957). Instructions and tables for computing potential evapotranspiration and the water balance. Publications in Climatology, 10, 185 – 311. Timlin, D., Loechel, S., Pachepsky, Y., & Walthal, C. (2001). The use of a water budget model and yield maps to characterize water availability in a landscape. Soil and Tillage Research, 58, 219 – 231. Troufleau, D., Vidal, A., Beaudoin, A., Moran, M. S., Weiltz, M. A., Goodrich, D. C., Washburn, J., & Rahman, A. F. (1997). Optical-microwave synergy for estimating surface sensible heat flux over a semi-arid rangeland. Remote Sensing Reviews, 15, 113 – 132. Unganai, L. S., & Kogan, F. N. (1998). Drought monitoring and corn yield estimation in southern Africa from AVHRR data. Remote Sensing of Environment, 63, 219 – 232. U.S. Department of Agriculture (1995). State Soil Geographic (STATSGO) data base: Data use information. Fort Worth: Natural Resources Conservation Service, Miscellaneous Publication Number 1492. U.S. Soil Conservation Service (1972). National engineering handbook. Hydrology, section 4. Washington, DC: U.S. Department of Agriculture. Wang, J. (2000). Relations between productivity, climate, and Normalized Difference Vegetation Index in the central Great Plains. Lawrence: University of Kansas (PhD dissertation). Wang, J., Price, K. P., & Rich, P. M. (2001). Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. International Journal of Remote Sensing, 22, 3827 – 3844. Wilhite, D. A. (Ed.) (1993). Drought assessment, management, and planning: Theory and case studies. Natural resource management and policy series (p. 4). Boston: Kluwer Academic Publishing. Wright, R. J., Boyer, D. G., Winant, W. M., & Perry, H. D. (1990). The influence of soil factors on yield differences among landscape positions in an Appalachian cornfield. Soil Science, 149, 375 – 382. Yang, X., Zhou, Q., & Melville, M. (1997). Estimating local sugarcane evapotranspiration using Landsat TM image and a VITT concept. International Journal of Remote Sensing, 18, 453 – 459.
Impacts of hydrologic soil properties on drought detection with MODIS thermal data Sunyurp Park a,*, Johannes J. Feddema b, Stephen L. Egbert a a
Kansas Applied Remote Sensing Program, University of Kansas, 2101 Constant Avenue, Lawrence, KS 66047, USA b Department of Geography, Lindley Hall, University of Kansas, 1475 Jayhawk Blvd., Lawrence, KS 66045, USA Received 14 July 2003; received in revised form 26 September 2003; accepted 1 October 2003
Abstract Remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), a climatic water budget model, and the STATSGO database were used within a GIS environment to determine the influences of hydrologic soil properties on soil moisture and thermal emission in western – central Kansas for a dry year, 2000. Two important variables, water-holding capacity (WHC) and hydrologic soil group (HSG), were controlled in our water budget experiment to evaluate their impacts on soil moisture content (SMC) changes throughout the period. Results showed that HSG affected drought detection and occurrence very little, but WHC variations explained most local variations of soil moisture content. As a strong indicator of relative soil moisture deficit, the Standardized Thermal Index (STI) patterns were also influenced by WHC. Generally, the earlier the soil moisture content drops below 40%, the earlier the STI reaches a threshold value of 0.2 or higher. Vegetation responses to thermal detection lagged behind the STI by up to 8 weeks, which was computed by comparing the STI and Normalized Difference Vegetation Index (NDVI) deviation from a 10-year mean. The spatial pattern of lag-times was not apparent, but lag-times were correlated with a WHC component. D 2003 Elsevier Inc. All rights reserved. Keywords: MODIS; STATSGO; Thermal emission; Water-holding capacity; Hydrologic soil group; Standardized Thermal Index
1. Introduction Innovations in remote sensing technology have provided new solutions to environmental problems in the earth sciences. In natural hazard monitoring, such as with drought events, remote sensing techniques are especially crucial for timely decision-making because they provide prompt geospatial data and a better spatial footprint of environmental phenomena characterizing an entire area compared to point locations, such as weather stations (Kogan, 2000; Legates, 2000; Unganai & Kogan, 1998). Formulated from remote sensing data, the Normalized Difference Vegetation Index (NDVI) is a well-known measure linked to biophysical variables and has been widely used for vegetation monitoring. However, NDVI is not always an appropriate tool for ‘‘real-time’’ drought monitoring. Due to a lagged vegetation response to drought, NDVI cannot detect drought events * Corresponding author. Tel.: +1-785-864-1524; fax: +1-785-8641534. E-mail address: [email protected] (S. Park). 0034-4257/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2003.10.003
instantaneously (Lozano-Garcia, Fernandez, Gallo, & Johannsen, 1995; Peters, Rundquist, & Wilhite, 1991; Reed, 1993; Rundquist & Harrington, 2000). Earlier drought detection may be possible using thermal emission patterns from remote sensors. Land surface temperature (LST) is an important biophysical indicator because it is directly linked to the net radiation flux and surface moisture conditions. Interacting with the soil – plant –air system, LST represents the instantaneous state of the energy flux for a land surface. Therefore, spatially and temporally well-sampled surface temperatures should theoretically help identify waterstressed vegetation canopies. It is believed that by using thermal emission patterns in combination with meteorological observations, the relationship between surface temperature and the moisture regime on the ground will detect drought areas before biomass degradation occurs. With high radiometric and temporal resolution, thermal infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS) allow us to more accurately infer changes in surface thermal regimes and assist in improved drought detection.
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Drought is a temporary condition resulting from prolonged absence, deficiency, or poor distribution of precipitation (Ogallo, 1994; Wilhite, 1993). Although precipitation is the main controlling factor in drought events in the central Great Plains region (Wang, 2000), vegetation growth is dependent upon a number of additional environmental factors, such as high temperature, high winds, low soil moisture content, or low relative humidity. In particular, hydrologic soil properties play an important role in affecting vegetation growth (De Jong, Shields, & Sly, 1984; Farrar, Nicholson, & Lare, 1994; Nicholson & Farrar, 1994; Timlin, Loechel, Pachepsky, & Walthal, 2001). The availability of soil moisture for plant uptake is influenced by soil type, soil texture, water-holding capacity, infiltration rate, etc. These characteristics determine how long water will be held in soil layers and how much water will be held there, and they control changes in soil moisture content (SMC). Among the soil properties that affect vegetation growth, water-holding capacity (WHC) is an important factor for plant productivity. Since the total amount of water available for plant growth in a field is a function of the depth and WHC of the soil, WHC is considered one of the most influential hydrologic soil variables in calculating the amount of water storage in a soil profile (Brady & Weil, 2002; De Jong et al., 1984; Timlin et al., 2001; Wright, Boyer, Winant, & Perry, 1990). Estimation of SMC is often done with the use of water budget models. The initial step in a water budget model is the calculation of potential evapotranspiration. Empirical methods for estimating potential evapotranspiration have proven useful and are generally accepted because they require only limited climatic data (Thornthwaite, 1948; Thornthwaite & Mather, 1955, 1957). Although these empirical methods are often less accurate than other, more complicated, methods, their reliance on few data inputs has proven advantageous for large-scale applications, and this outweighs their inability to reflect short-period changes in wind or humidity conditions (Mather, 1978). The advantage of using a water budget model is that it integrates various environmental aspects in calculating soil moisture content, such as WHC, hydrologic conditions, and land use. Therefore, by controlling one of these input variables, the theoretical influence of the variable on soil moisture conditions can be evaluated with a climatic water budget model. The objective of this study is to determine the influences of hydrologic soil characteristics on early detection of drought with MODIS thermal emission data and on the response time of NDVI to the thermal signals. Although a few previous studies have shown that NDVI responses lag behind antecedant precipitation by up to 3 months (Davenport & Nicholson, 1993; Farrar et al., 1994; Justice, Holben, & Gwynne, 1986; Malo & Nicholson, 1990; Nicholson & Farrar, 1994; Wang, 2000; Wang, Price, & Rich, 2001), no documentation has been observed regarding NDVI’s response time to satellite thermal signals and the impact of hydrologic soil factors on the lag-time. In recent research
conducted in Kansas, Wang et al. (2001) suspected that soil moisture might affect NDVI patterns in the Konza Prairie, but they did not find a rigorous relationship due to varying lag-times between soil moisture and NDVI. A hydrologic approach to this question may help incorporate hydrologic soil attributes into better drought monitoring schemes in the future.
2. Methodology 2.1. Study area Western and central Kansas were selected as the study areas because of frequently occurring droughts and their potential impact on the local agricultural economy and natural grassland/rangeland management practices (Fig. 1). Due to rapid conversion from grassland to cropland or rangeland since the late 1800s, the region’s economy is based upon grazing, feeding of livestock, dryland farming, and increasingly, irrigated agriculture. Due to limited water supplies and water deficits resulting from high levels of evapotranspiration, the agricultural economy of western and central Kansas is very susceptible to drought conditions, and as a result has become dependent upon the High Plains aquifer as a water resource. Water availability from the aquifer has transformed much of the semi-arid shortgrass prairie region into one of the largest irrigated agricultural regions in the nation (Kromm & White, 1992). Since this region has relatively flat topography and is mostly vegetated by grasses or crops, the thermal signals from the surface are assumed to be relatively uncontaminated by slope, aspect, or shadows.
Fig. 1. Study area includes western and central parts of Kansas. The locations of weather stations are shown as solid squares, and internal lines represent boundaries of climatic divisions for the state.
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2.2. Satellite and meteorological data 2.2.1. MODIS thermal infrared data LST data from MODIS were obtained from the USGS EROS Data Center, Sioux Falls, South Dakota. Since LST data before July 2000 were not available, only data acquired from July to October 2000 were analyzed in this study. In all, 67 daily MODIS LST images were obtained for the study area. To synchronize LST data with weekly NDVI values and to avoid cloud interference, the daily LST values were combined into weekly composite data with the same weekly interval as the NDVI data set. In creating weekly composites, the highest LST in each weekly period was included because it has been found that the maximum temperature composite data most closely represented land surface conditions (Cihlar, Manak, & D’iorio, 1994; Park, Feddema, & Egbert, 2002a; Park, Feddema, & Egbert, 2002b). It was hypothesized that areas suffering from a water shortage would have lower NDVI values and higher temperature gradients between the land surface and the air compared to drought-free areas. Therefore, a LST deviation from air temperature, defined as LST – air temperature, was expected to have an inverse relationship with soil moisture and evapotranspiration, but a positive relationship with moisture deficit. LST deviation is an adequate indicator of surface moisture conditions because it is believed to have a linear inverse relationship with vapor pressure deficit of air, and a positive relationship with sensible heat flux, which increases as water is restricted (Choudhury, 1989; Ehrler, 1973; Idso, Jackson, Ehrler, & Mitchell, 1977; Idso, Jackson, Pinter, Reginato, & Hatfield, 1981; Inoue & Moran, 1997; Jackson, Idso, Reginato, & Pinter, 1981; Jackson, Reginato, & Idso, 1977; Lhomme & Monteny, 1993; Moran & Jackson, 1991; Moran, Humes, & Pinter, 1997; Moran et al., 1996; Troufleau et al., 1997; Yang, Zhou, & Melville, 1997). In a previous study, time-integrated, or cumulative LST deviations had significant negative relationships with soil moisture content (SMC) and the actual/potential evapotranspiration ratio (AE/PE), while they had a positive relationship with the moisture deficit/potential evapotranspiration ratio (MD/PE) (Park et al., 2002a,b). This indicated that LST deviation increased with soil moisture deficit. A formulated index, called the Standardized Thermal Index (STI), was proposed to rescale the LST deviations from 0 to 1. The STI is defined as the cumulative mean of LST standardized with mean air temperature ([LST meanTair]cum) divided by the cumulative mean of the sum of the LST and the mean air temperature ([LST + meanTair]cum): STI ¼ ½LST meanTair cum =½LST þ meanTair cum It was found that STI values of 0.2 corresponded to 15% or more decline in NDVI (Park, 2003). Therefore, the value
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of 0.2 was considered as a threshold for thermal detection of drought. 2.2.2. NDVI Weekly AVHRR maximum-NDVI composite data for the growing season (March – October) in 2000 and 10-year (1990 –1999) weekly mean NDVI data were obtained from archives at the Kansas Applied Remote Sensing (KARS) Program at the University of Kansas. To measure NDVI degradation in the dry year 2000, NDVI departures from the 10-year mean were computed for each week. The NDVI departure, or drought severity, is defined as the target NDVI value minus the 10-year mean NDVI for each week as follows: Drought severity ¼ NDVI2000 10 -year mean NDVI Positive departure values represent above-normal vegetation growth, whereas negative ones represent below-normal conditions. Oftentimes, surface thermal emission data and hydroclimatological parameters do not have a significant relationship with NDVI over short periods of time because plant response to changes in a soil moisture regime may not be instantaneous. Once drought severity was computed from the NDVI data, image pixels with significant NDVI decreases were identified to determine how long it took for vegetation to experience abnormal degradation following a thermal indication of drought. Since hydrologic soil properties control the amount and duration of soil moisture storage, they were expected to have a significant relationship with the lag-time. 2.2.3. Meteorological data Meteorological data including daily maximum and minimum air temperatures and precipitation data for the year of 2000 were acquired from the High Plains Regional Climate Center (HPRCC) at the University of Nebraska. Air temperature was interpolated between observations from weather station networks using an inverse-distance weighted method. 2.3. Analysis 2.3.1. Water budget experiment In addition to WHC, hydrologic soil group (HSG), which represents infiltration rates of soils, is another important hydrologic input variable required for running a water budget program coupled with a runoff model. These variables are readily available from the State Soil Geographic (STATSGO) database (USDA, 1995). The HSG for each map unit, the fundamental graphic feature in STATSGO, falls into one of four major groups, ranked A (high infiltration rates) through D (very slow infiltration rates) on the basis of their runoff potential (Mather, 1978; SCS, 1972). To determine the impacts of hydrologic soil properties on soil moisture changes, two main factors, namely WHC and
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HSG, were selected as control factors. By comparing simulated results based on these variables with satellitederived measures of drought, the influence of soil factors on drought can be evaluated. Our preliminary study showed that SMC decreased to 40% or less of WHC before NDVI declined below a normal value (Park, 2003). If the hydrologic soil factors affect the timing and duration of the soil moisture threshold, it is expected that the thermal emission pattern will be correlated with the spatial pattern of the time required for soil moisture to fall below 40% of WHC, or with the duration that conditions below this threshold persist. These two aspects of soil moisture changes were considered in terms of Julian day calculations: (1) the time required for soil moisture conditions to fall below 40% was represented by the Julian day when soil moisture content (SMC) first declined below this threshold and (2) the duration of drought was represented by the number of succeeding days where SMC stayed below the 40% threshold. Spatial representation of the influences of the hydrologic soil factors on these two measurements was performed by running four different water budget experiments at the 18 weather stations (Fig. 1). These experiments were based on different combinations of the two soil factors as follows: 1. A standard condition: Used a standard WHC of 150 mm and the most dominant HSG in the study area, group B, for water budget calculations at all locations. 2. HSG controlled: Used observed WHC at each station and the dominant HSG B for all locations. 3. WHC controlled: Used a standard WHC of 150 mm at all locations and observed HSG at each station. 4. Observed: Used observed values for both WHC and HSG at each station. For each experiment, the first Julian day when SMC dropped below 40% of WHC, and the number of succeeding days where SMC stayed below this threshold were calculated. An inverse-distance weighted interpolation method was performed to create the maps of these variables. The impacts of WHC and HSG on SMC can then be evaluated in two ways. First, maps from the resulting four experiment conditions can be compared to each other to determine which of these two factors contributes most to the observed conditions. The other simple way to determine the influence of WHC and HSG on soil moisture changes is to calculate deviation values of experiments (2) and (3) from the standard condition. These two deviation measures were termed ‘WHC component’ and ‘HSG component,’ respectively. This differencing method shows which factor affects soil moisture conditions most and where that factor is most influential across the landscape. It was also hypothesized that the arrival time and the frequency of the threshold SMC were closely correlated with thermal emission.
2.3.2. Hydrologic soil factors and surface thermal emission If hydrologic soil factors affect surface thermal emission, it is expected that thermal emission patterns will resemble the spatial patterns of the first Julian day when SMC declined to 40% and the number of succeeding days below the threshold SMC of 40%. For this comparison, two additional STI maps were created and compared to the water budget experiment maps. First, using weekly STI maps, the first week when the STI reached 0.2 was identified for each image pixel. Second, the number of weeks when the STI was 0.2 or higher was also computed for each image pixel. These two maps represented how early the thermal emission signal reached the threshold and how long it persisted above the threshold. Two evaluation methods were used to compare these STI maps with the water budget experiment maps. First, a comparison was conducted based on the descriptive statistics of the experiment maps and their relationships with STI values in terms of the time when STI first reached 0.2 and the number of weeks where STI stayed 0.2 or higher. Mean, median, and standard deviation values for each STI level, which were categorized with an interval of 0.05, were computed for both the arrival time and the frequency of STI 0.2. Additionally, pixel-to-pixel-based correlation coefficients were computed between the STI maps and the water budget experiment maps. The temporal correlation of WHC and HSG components with STI was also determined using the deviations of experiments (2) and (3) from the control (1) with respect to the Julian day when SMC first reached 40% for each composite period. 2.3.3. Vegetation response time and WHC It has been demonstrated that WHC controls the amount and time of soil moisture release, mainly by soil texture and pore space (Brady & Weil, 2002). Generally, the lower the WHC the greater the runoff, and the quicker drought sets in and the longer the duration of drought. Based on these observations, we hypothesize that there should be a relationship between soil WHC and the lag-time of vegetation response to thermal signals. To test this hypothesis, the time lag between the detection of thermal signals indicating drought and the time of plant response to drought was calculated. Time of onset of drought was determined from the first of occurrence of STI values of 0.2 or higher. Knowing that this threshold STI was followed by 15% or greater declines in NDVI declines below a 10-year mean (1990 – 1999), a conservative value of a 20% decline of NDVI below normal was used to determine how long it took for plants to respond to the thermal drought indication for each pixel on a weekly basis. A map representing this lag-time was compared against the water budget derived Julian day when SMC reached 40% and the number of days where SMC remained below 40% for both the standard and the HSG-controlled conditions to see if the lag-time was correlated with precipitation and the WHC factor. In addition, to avoid possible interference by precipitation on the correlation, a deviation map of the days to
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reach 40% SMC and duration below the 40% threshold was derived by subtracting the standard condition from the HSG-controlled condition. Since WHC is the only condition changed in the HSG-controlled condition, the deviation values represent the effect of WHC on temporal changes in the SMC threshold. Correlation coefficients were calculated between the lag-time and Julian day of WHC 40% and the number of days below WHC 40% for the standard condition, the HSG-controlled condition, and the deviation of the HSG-controlled condition, respectively.
3. Results and discussion 3.1. Water budget experiment 3.1.1. Soil moisture content changes The results from the experiment showed that, for the year 2000, the WHC-controlled case was very similar to the standard condition, and the observed case was almost identical to the HSG-controlled case for both the Julian day when WHC first reached 40% and the number of succeeding days where SMC was below 40%. This suggests that WHC has a significant impact on drought onset and duration while HSG has relatively little influence on drought conditions. The spatial pattern of the standard condition clearly showed that there was a northwest – southeast precipitation-deficit gradient with higher precipitation in the northwest. This can be surmised because WHC and HSG were standardized across the study area, and therefore,
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precipitation is the main factor influencing water balance conditions. This result somewhat deviates from the normal annual precipitation pattern, which typically has an increasing west –east gradient (Goodin, Mitchell, Knapp, & Biven, 1995). Estimated SMC reflected this pattern. St. Francis, a northwestern site, was an extreme case for the wet condition, where SMC did not drop below 40% until mid-August. In contrast, McPherson, a central site, represented very dry conditions, where SMC reached 40% in mid-May (Fig. 2, locations are shown in Fig. 1). The number of days with SMC of 40% or lower was greatest in the northwestern area (130 days or more), while the south – central area had the fewest number of days (70 days or less, Fig. 3). Wang et al. (2001) recently confirmed that precipitation is a strong predictor of spatial patterns of NDVI in Kansas. Since SMC is strongly governed by precipitation, this is the primary factor affecting drought. However, changes in SMC due to internal soil factors may play an important secondary role in drought susceptibility, and this additional information could improve drought monitoring at specific locations. Overall, hydroclimatological parameters, including SMC, AE/PE, and MD/PE, had statistically significant correlations with STI. Their mean correlation coefficients calculated at six major weather stations across the study area were 0.64, 0.65, and 0.66, respectively (Table 1). 3.1.2. Impacts of WHC and HSG Since the observed pattern of the first Julian day of SMC 40% corresponded well with the HSG-controlled experiment, WHC is believed to be a dominant hydrologic
Fig. 2. Climatic water budget experiment setting for Julian day of WHC 40%. (a) The standard condition, (b) HSG-controlled at group B, (c) WHC-controlled at 150 mm, and (d) WHC and HSG are observed values. Numbers represent the Julian day when WHC 40% is reached.
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Fig. 3. Climatic water budget experiment setting for the number of days below WHC 40%: (a) the standard condition, (b) HSG-controlled at group B, (c) WHC-controlled at 150 mm, and (d) WHC and HSG are observed values. Numbers represent the number of days when WHC was lower than 40%.
factor in affecting SMC. A map of the WHC component illustrated that, in the southwestern region of the study area, SMC reached 40% about 10 to 20 days later than the standard condition due to low WHC. In contrast, it took 40 – 80 days longer than the standard condition in the central region to reach the same SMC level due to relatively high WHC (Fig. 4a). The observed pattern of the number of days where SMC fell below 40% was very similar to that of the HSGcontrolled case (Fig. 4b). This confirmed that WHC played a role in changing SMC. The WHC component map also showed that the southwestern region did not deviate much from the standard condition, having less than a 30-day difference from the standard condition due to low WHC. The most affected areas were in the northern part of the state. The high WHC of these areas made the soil layers hold more water and have fewer days with dry soils compared to the theoretical standard condition. This characteristic made a large difference in the number of days with
SMC of 40% or lower between the standard and the HSGcontrolled experiment. In the central region, despite a relatively high WHC, the distribution of SMC simulated with the observed WHC was not different significantly from that computed with the standard WHC of 150 mm. The weaker impact of WHC on this region was caused by the fact that the central region had several significant rainfall events during the growing season. This resulted in there being little difference in the number of days where SMC was under 40% between the standard and the HSG-controlled conditions. In other words, the rainfall events may not have allowed soil moisture levels to drop below 40% very often, even for the shallow standard WHC condition (150 mm). 3.2. Hydrologic factor –thermal signal relations Because there were no thermal data available before week 28, the relationship between the arrival time of STI
Table 1 Relationships between the Standardized Thermal Index and three hydroclimatological parameters Relationships
Dodge City
Goodland
Healy
Salina
Washington
Wichita
Meany
STI:SM STI:AE/PE STI:MD/PE Meany
0.82** 0.85** 0.81** 0.83
0.71** 0.73** 0.74** 0.73
0.77** 0.77** 0.81** 0.78
0.50* 0.50* 0.51* 0.50
0.49* 0.50* 0.52* 0.50
0.57* 0.58* 0.57* 0.57
0.64 0.65 0.66 0.65
* Significant at the 0.05 level. ** Significant at the 0.01 level. y Mean values of absolute numbers of correlation coefficients.
S. Park et al. / Remote Sensing of Environment 89 (2004) 53–62
Fig. 4. Maps of the WHC component, the deviation of the HSG-controlled experiment from the standard condition. They are expressed by the first Julian day of SMC 40% (a) and the number of days where SMC stayed below 40% (b). Numbers describe how the WHC component has changed the first Julian day of SMC 40% and the number the days where SMC stayed below 40% compared to the standard condition.
0.2 and the first Julian day when SMC fell below 40% was not fully analyzed. However, descriptive statistics of areas with different weekly arrival times of STI 0.2 show that the chronological order of STI 0.2 conforms most closely to the observed Julian day (mean and median days); and the standard deviation of the observed Julian day for each weekly STI class is smaller than that of the standard condition. The chronological order of locations where STI first reached 0.2 and SMC first reached 40%
59
agreed well with each other. Their correlation coefficients were 0.62 for the standard condition, 0.64 for the observed case, and 0.47 for the WHC component (Table 2). These results demonstrated that the arrival of STI 0.2 was more similar to that of the first observed Julian day of SMC 40%, indicating that WHC was a factor for the time of thermal detection of drought conditions. Pixel-to-pixel correlation coefficients also showed that the first week of STI 0.2 was more strongly correlated with the observed Julian day of SMC 40% (r = 0.49) compared to the standard condition (r = 0.31), demonstrating that the time when STI reaches 0.2 is influenced by soil hydrologic factors in addition to precipitation. Comparing the number of weeks where the STI was 0.2 or higher with the experiment results, it is clear that the spatial distribution of the STI parameter resembles that of the number of days below SMC 40% (Fig. 5). Areal mean and median values of the observed number of days with SMC of 40% or lower had stronger correlations with the number of weeks with STI 0.2 or higher (r = 0.96 and 0.92) compared to those of the standard condition (r = 0.96 and 0.89). The WHC component also had a significant correlation with the number of days of STI value of 0.2 or higher (Table 3). A pixel-to-pixel correlation analysis showed that the observed number of days with SMC of 40% or lower had the highest correlation with the number of weeks where the STI was 0.2 or higher (r = 0.67) followed by the standard condition (r = 0.40) and the WHC component (deviation from the standard condition, r = 0.30). From these results, it is demonstrated that the STI threshold had correlations with the different experimental conditions in the decreasing order of observed>standard>WHC component>HSG component (not significant). A negative relationship between the WHC component and the STI indicated that those areas with a relatively low WHC reached the threshold SMC earlier and had higher STI values compared to areas with high WHC. During the growing season, the mean correlation coefficient between STI (0.2 or higher) and WHC was 0.62 (range, 0.39 to 0.72). However, this value was not as high as that
Table 2 The relationship between the week when the STI reached 0.2 and the Julian day when SMC declined to 40% for the standard condition, the observed case, and deviation from the standard condition. Mean, median, and standard deviation of Julian day are calculated for each weekly category when the STI reached 0.2. An NDVI deviation from a 10-year mean is also shown for each week Week Std_mean Std_med S.D. Obs_mean Obs_med S.D. Dev_mean Dev_med S.D. NDVI** 28 30 34 35 36 37 38 r*
124.6 131.3 122.0 125.5 132.1 138.2 148.0 0.62
129 131 129 130 132 140 146 0.69
17.6 13.3 20.8 16.5 14.5 14.3 18.8 –
150.5 164.2 153.3 156.1 162.3 167.2 181.6 0.64
148 165 156 158 162 166 179 0.69
* Pearson product-moment correlation coefficient. ** NDVI deviation from the mean in period 38, 9/15/00 – 9/21/00 (%).
13.2 12.9 15.2 12.7 13.3 16.3 18.4 –
26.7 33.3 31.9 31.3 31.1 29.7 34.0 0.47
34 36 45 37 32 31 36 0.10
10.7 6.2 10.9 8.7 7.5 9.1 8.7 –
15.7 22.6 16.0 18.0 13.6 11.1 11.0 0.68
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Fig. 5. The number of weeks for which the STI was 0.2 or higher from week 28 (July 7 – 13) to week 38 (September 15 – 21). Fig. 6. Relationships between STI for each composite period and NDVI declines in mid-September.
between thermal emission and precipitation (r = 0.72). Therefore, it is concluded that, as expected, thermal signals for land surface conditions are partly influenced by soil WHC in addition to the primary control by precipitation. 3.3. Lag-time between STI and NDVI Mean STI values for each STI class ranged from 0.03 to 0.31 during the study period. As expected, these STI values had significant positive relationships with NDVI deviations in each composite period, and the correlation coefficients increased from 0.85 to 0.98 as time progressed. These results clearly show that higher STI correlates with more severe drought. This relationship in each composite period is represented in Fig. 6, and an overall regression line was fit to the data. Comparing class mean values of STI and NDVI deviations, STI values higher than 0.2 corresponded to 15% or greater declines in NDVI in September. Lag-times of up to 8 weeks were calculated. An apparent spatial pattern was not observed in the lag-time distribution, but patches of pixels with short lag-times were found in the
southern, western, and upper central parts of the state, and relatively longer lag-times in northwestern areas (Fig. 7). Study results revealed that the lag-time between STI and NDVI was significantly correlated with precipitation. For the standard condition, the correlation coefficient between the lag-time and the first Julian day of SMC 40% (or lower) was r = 0.58, and that between the lag-time and the number of days with SMC 40% (or lower) was r = 0.60. Since the lag-time became longer as the first Julian day of SMC 40% started earlier or the number of days below the threshold value increased, the lag-time is believed to be inversely related with precipitation. The observed deviation map of first Julian day had a positive relationship (r = 0.49) with the lag-time. This means that as the first Julian day with SMC of 40% was observed later than that of the standard condition, or as WHC increased, the lag-time became longer. Conversely, the deviation map of the number of days below the SMC 40% threshold had a negative relationship (r = 0.68) with the lag-time. This means that as the observed number of
Table 3 The relationship between the number of weeks for which the STI was 0.2 or higher and the number of days below SMC 40% for the standard condition, the observed case, and deviation from the standard condition. Mean, median, and standard deviation of the number of days below SMC 40% are calculated for each class of the number of weeks where the STI remained 0.2 or higher. An NDVI deviation from the mean is also shown for each class No. of weeks Std_mean Std_med S.D. Obs_mean Obs_med S.D. Ht_mean Ht_med S.D. NDVI** 1 2 3 4 5 6 7 r*
123.9 134.0 140.8 142.3 144.0 147.9 151.9 0.96
128 134 141 140 138 143 155 0.89
22.8 18.4 17.2 18.4 19.6 19.3 16.3 –
86.9 94.8 98.2 101.9 105.3 109.6 124.9 0.96
* Pearson product-moment correlation coefficient. ** NDVI deviation from the mean in period 38, 9/15/00 – 9/21/00 (%).
90 94 97 99 102 109 126 0.92
17.0 15.4 12.5 13.2 14.5 16.1 12.5 –
39.1 42.5 45.5 43.1 41.3 40.5 28.8 0.56
39 42 44 40 37 38 29 0.73
11.9 13.9 15.6 17.6 18.4 18.2 14.2 –
11.7 10.9 13.7 14.3 14.7 18.7 17.3 0.92
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with soil WHC. Since soil WHC is soil depth-weighted, those areas that had deep soils and lost soil moisture more slowly due to high WHC had a relatively longer lag-time compared to locations with low WHC. These relationships among hydrologic soil properties, threshold SMC, and satellitederived thermal drought detection have important implications for early detection of drought. Possible misleading thermal signals for soil moisture conditions due to rainfall events with varying amounts and frequencies during a drought will be minimized by using threshold soil moisture levels and soil WHC as supplementary references to drought in addition to the thermal emission pattern. Fig. 7. Lag-time between STI and NDVI deviations from a 10-year mean (1990 – 1999).
days where SMC stayed below 40% became smaller than that of the standard condition, or as WHC increased, the lagtime increased. Both of these results indicated that the lagtime was positively correlated with soil WHC. Actual total WHC had a similar correlation with the lag-time with r = 0.55 (significant at the 0.1 level). As indicated by the correlation coefficients, the relationship between the lagtime and WHC was better described by using the number of days below the threshold compared to the first Julian day of SMC 40%. This is probably because (1) WHC is a soil depth-weighted variable, (2) the total column of soil functions as a buffer throughout the growing season, and (3) a soil –NDVI response relationship can change according to the hydrologic characteristics of soils (Farrar et al., 1994).
4. Conclusions Remotely sensed thermal infrared data, the STATSGO database, and a climatic water budge model were effectively integrated using GIS tools to determine the influence of hydrologic soil characteristics on SMC, thermal emission from land surfaces, and NDVI’s response time to the thermal signals. As secondary factors in controlling soil moisture content, hydrologic soil properties were analyzed to determine how they contributed to soil moisture changes. Study results showed that although precipitation governed overall soil moisture conditions, soil WHC had additional impacts. It was observed that higher WHC made SMC reach its threshold value more slowly and experience fewer days of soil moisture falling below the 40% of WHC threshold. The spatial patterns of the STI, in both the first time when STI first reached 0.2 and the number of weeks with STI values of 0.2 or higher, were also correlated with WHC. WHC also played a role in thermal drought detection. Low WHC makes SMC decrease to the threshold level earlier, and therefore, it increased surface temperatures more quickly. NDVI’s response time to the thermal signals was also affected by soil WHC. This lag-time turned out to be positively correlated
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