Remote Sensing of Vegetation Water Content using Shortwave Infrared Reflectances

E. Raymond Hunt, Jr. *,a, M. Tugrul Yilmaz a,b

aUSDA-ARS Hydrology and Remote Sensing Laboratory, Building 007 Room 104, 10300 Baltimore Avenue, Beltsville, MD, USA 20705

bEarth System and Geoinformation Sciences, George Mason University, Fairfax, VA, USA 22030

ABSTRACT

Vegetation water content is an important biophysical parameter for estimation of soil moisture from microwave radiometers. One of the objectives of the Soil Moisture Experiments in 2004 (SMEX04) and 2005 (SMEX05) were to develop and test algorithms for a vegetation water content data product using shortwave infrared reflectances. SMEX04 studied native vegetation in Arizona, USA, and Sonora, Mexico, while SMEX05 studied corn and soybean in Iowa, USA. The normalized difference infrared index (NDII) is defined as (R850 - R1650)/(R800 + R1650), where R850 is the reflectance in the near infrared and R1650 is the reflectance in the shortwave infrared. Simulations using the Scattering by Arbitrarily Inclined Leaves (SAIL) model indicated that NDII is sensitive to surface moisture content. From Landsat 5 Thematic Mapper and other imagery, NDII is linear with respect to foliar water content with R2 = 0.81. The regression standard error of the y estimate is 0.094 mm, which is equivalent to about a leaf area index of 0.5 m2 m-2. Based on modeling the dynamic water flow through plants, the requirement for detection of water stress is about 0.01 mm, so detection of water stress may not be possible. However, this standard error is accurate for input into the tau-omega model for soil moisture. Therefore, NDII may be a robust backup algorithm for MODIS as a standard data product. Keywords: Normalized Difference Infrared Index, Equivalent water thickness, SAIL model, Plant water stress, Soil Moisture Experiments, MODIS

1. INTRODUCTION Vegetation water content is an important biophysical parameter for the retrieval of soil moisture content from microwave data 1,2, and validation of soil moisture algorithms was one of the principal objectives for the Soil Moisture Experiments in 2002 (SMEX02) in Iowa, USA 2, 2004 (SMEX04) in Arizona, USA and Sonora, Mexico 3, and 2005 (SMEX05) again in Iowa, USA 4. If vegetation water content can be estimated independently using reflectances in the shortwave infrared (SWIR), then the retrievals of soil moisture content will be more accurate. The problem is that SWIR reflectances are dominated by foliar water content and are not affected by stem water content. However, plants often have allometric relationships (y = α x β) between foliar and stem mass, so estimation of foliar water content from SWIR reflectances would allow prediction of vegetation water content 4. Foliar water content (kg m-2) is often divided by the density of liquid water (1000 kg m-3) to derive the equivalent water thickness (EWT, mm). EWT is useful because the canopy EWT is equal to the leaf EWT multiplied by the leaf area index (LAI, m2 m-2) 5. Leaf reflectance at 1650 nm wavelength (R1650) increases linearly with respect to leaf reflectance at 850 nm (R850) for a decrease in leaf EWT 6. Hardisky et al. 7 defined the Normalized Difference Infrared Index (NDII) as:

NDII = (R850 - R1650)/(R850 + R1650) (1) which is represented by Landsat 5 Thematic Mapper bands 4 and 5 or MODIS bands 2 and 6, respectively. * Raymond.Hunt@ars.usda.gov; phone +1 301 504-5278; fax +1 301 504-8931

Invited Paper

Remote Sensing and Modeling of Ecosystems for Sustainability IV, edited by Wei Gao, Susan L. Ustin,Proc. of SPIE Vol. 6679, 667902, (2007) · 0277-786X/07/$18 · doi: 10.1117/12.734730

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Gao 8 defined the Normalized Difference Water Index (NDWI) as: NDWI = (R850 - R1240)/(R850 + R1240) (2) where R1240 is the reflectance at 1240 nm wavelength which is represented by MODIS band 5. Gao 8 has specific reasons for selecting R1240 over R1650 for calculation of NDWI such as photons at 850 nm and 1240 nm penetrate similarly into vegetation canopies and have similar atmospheric scattering. However, NDII can be calculated using imagery from most satellite sensors, whereas NDWI can only obtained using MODIS data, hyperspectral data, and future National Polar-orbiting Operational Environmental Satellite Systems (NPOESS). The first objective of this study is to examine how NDII changes with canopy EWT. One of the major limitations to accurate detection of canopy EWT is the amount of surface moisture (residue or soil), we study these effects using the Scattering by Arbitrarily Inclined Leaves (SAIL) model 9. If canopy EWT can be estimated with sufficient accuracy, then perhaps changes in canopy EWT can be detected during drought. Drought affects vegetation by several mechanisms, such a reduction of growth or senescence of current LAI, which could be detected with vegetation indices such as the Normalized Difference Vegetation Index (NDVI) or the Enhanced Vegetation Index (EVI) 10. Before a reduction in LAI, the initial stages of drought cause plant water stress, which is a reduction of water in the leaf as measured by either leaf relative water content (RWC) or leaf water potential (Ψleaf, MPa) 11. Leaf RWC is equal to leaf EWT divided by the EWT at full turgor. We use an electric circuit analog model 12 to predict changes of leaf EWT for levels of drought and compare the changes of leaf EWT to the accuracy of NDII to predict canopy EWT.

Fig. 1. Relationship between Normalized Difference Infrared Index (NDII) and canopy Equivalent Water

Thickness (EWT) for different ecosystems. Landcover types were: grasslands for Ceccato et al. 13 and Davidson et al. 14; various land-cover types in Arizona, USA and Sonora Mexico for Yilmaz et al. (SMEX05) 3; soybean, corn and deciduous hardwood woodlands for Yilmaz et al. (SMEX05) 4; and coniferous forests and woodlands for Hunt 5. The regression equation (line) is canopy EWT = 0.224 + 1.09 NDII.

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2. NDII AND CANOPY EWT

Most studies examining the relationship between EWT and SWIR reflectances are made at the leaf level 6. Besides the data collected during SMEX04 3 and SMEX05 4, three other studies were found where canopy EWT was estimated 5,13,14 For canopy EWT from about 0.0 to about 1.0 mm, NDII was linearly related to canopy EWT (Fig. 1). High values of canopy EWT (> 1.0 mm) were from coniferous forest stands in Oregon, USA with very high LAI 5, and show that the NDII-EWT relationship saturates (Fig. 1). Excluding the data from Hunt 5, there were no significant differences among the various studies based on dummy variable regression. Based on data from a number of species, leaf EWT varies from 0.15 to 0.25 mm (E. Raymond Hunt, Jr., unpublished data). Taking 0.20 mm as a reasonable value for leaf EWT, then the standard error of the y estimate of 0.094 mm (Fig. 1) is about equal to an LAI = 0.5 m2 m-2. Therefore, differences in canopy LAI greater than 0.5 m2 m-2 should be detectable using NDII, when leaf EWT is constant. However, this is a general rule of thumb, because leaf EWT may have considerable variation within a species depending on growth stage and environmental conditions 4.

Fig. 2. SAIL model output of canopy EWT for Leaf Area Index (LAI) from 0.1 to 3.0 m2 m-2. Surface

backgrounds were dry and wet soils and corn residues. The green regression line is from Fig. 1. There is large variation of NDII at low LAI, which is also seen in Fig. 1.

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3. EFFECT OF GROUND SURFACE BACKGROUND

Similar to other vegetation indices 15, it is expected that variation of ground surface reflectance would affect the relationship between NDII and canopy EWT. Since NDII estimates the amount of SWIR radiation absorbed by liquid water in the foliage, NDII would also be affected if the ground surface was moist. Hemispherical reflectances were measured for corn leaves at the Beltsville Agricultural Research Center using an Analytical Spectral Devices FieldSpec Pro Full Range spectroradiometer; immediately afterwards leaf fresh weight, area, and dry weights were measured. Leaf EWT was 0.19 mm and canopy EWT was calculated from leaf EWT multiplied by LAI. The SAIL model simulations from LAI of 0.1 to 3.0 m2 m-2 were conducted using background reflectances from wet and dry soils and corn residues 16. The dark Barnes soil was from Minnesota, USA, whereas the intermediate Codorus soil and light Othello soil were from Maryland, USA 16. Corn residue were either new (collected from the field 1 week after harvest) or old (collected from the field 8 months after harvest) 16. Figure 2 shows large variation of simulated NDII at very low LAI from the variation in ground surface reflectances. This effect is harder to see in Fig. 1, because the range in EWT is much larger. However the range in NDII at about EWT of 0 is 0.3 (Fig. 1) and for dry-surface NDII, the range is about 0.35 (Fig. 2). The regression line from the data fit through the middle of the dry ground surfaces. There is an large increase in NDII for wet ground surfaces because of lower R1650, except at low LAI for the Barnes and Othello soils, where wetting made the soil darker overall. Wet, new residue had about the same NDII for all LAI (Fig. 2). During SMEX04, NDII for each of the soil moisture validation sites was measured on the ground using a CropScan MSR16 multispectral sensor; a few sites were measured each day. We compared the ground NDII with the NDII from atmospherically corrected Landsat 5 Thematic Mapper imagery obtained during SMEX04 3. Most of the points followed a 1:1 line with scatter of 0.1 NDII (Fig. 3). However, there were 6 points that had much higher ground NDII compared to the Landsat TM NDII (Fig. 3, circled in red). The day before these 6 sites were measured, a large convective thunderstorm passed overhead, wetting the ground surface and increasing ground NDII. Therefore, the wetness of the soil may increase the variation for estimating canopy EWT from satellite NDII. However, most of the time when the ground surface is wet, there is increased evaporation, which will form clouds obscuring the land surface. Thus, days that are clear for satellite data acquisition most likely have dry ground surfaces.

Fig. 3. Relationship between NDII for ground data and Landsat 5 Thematic Mapper imagery during the

SMEX04 experiment. Sites that are circled on lower right had ground NDII measured after a thunderstorm.

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4. VEGETATION WATER CONTENT

4. VEGETATION WATER CONTENT

Total Vegetation Water Content (VWC) is the sum of the water in the leaves and stems. Soil moisture content can not be retrieved when VWC is greater than 5 kg m-2 17. During SMEX05, there was a strong linear relationship between VWC and EWT for corn and soybean (Fig. 4), but not for deciduous hardwood woodlands 4. A linear relationship is an allometric relationship where the coefficient exponent (β) has a value of 1.0. With a linear allometric relationship between VWC and EWT (Fig. 4) and the linear between EWT and NDII (Fig. 1), there is a non-causal linear relationship between NDII and VWC for corn and soybean. In Fig. 1, the R2 between canopy EWT and NDII for the SMEX05 corn and soybean data was 0.85. The R2 for regressions between VWC and NDII are similar, 0.89 and 0.87 for corn and soybean respectively. Whereas the relationships of NDII and EWT for corn and soybean were not significantly different (P = 0.14 using dummy variable regression), the relationships of NDII and VWC for corn and soybean showed strong significant differences (P < 0.001; Fig. 4). The Soil Moisture Experiment 2002 (SMEX02) was also conducted in central Iowa. The data in Fig. 4 from SMEX05 was compared to the data from SMEX02 2. The regressions between NDII and VWC were not significantly different for corn (P = 0. 13) but were significantly different for soybean (P = 0.04) 4. This indicates that the allometric relationship between stems and leaves has year-to-year variation in soybean, which could be related to differences in weather between the two years affecting relative allocation patterns. Thus, future research should use crop and forest growth models to determine the relationships between leaf area index and stem size. Then the SAIL model can be used to determine NDII from LAI and soil background information. Then, NDII versus VWC regressions for different land-cover classes could be determined for multiple years at multiple locations from the simulation outputs. Land-cover classification for 2005 was obtained for Iowa from the USDA National Agricultural Statistics Service. The classification accuracy was tested using data collected during SMEX05 and was found to have a 92% overall accuracy. Based on land cover, NDII was used to determine VWC based on the regressions above. Deciduous hardwood woodlands did not have a significant regression between NDII and EWT 4, so an average value of 5 kg m-2 was used for

Fig. 4. Relationships between total Vegetation Water Content (VWC, kg m-2) and canopy Equivalent Water Thickness (EWT, mm) for corn and soybean during the Soil Moisture Experiment 2005 (SMEX05). An EWT of 1 mm equals a VWC of 1 kg m-2. The R2 for corn is 0.87 and the R2 for soybean is 0.48.

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this land-cover class. Whereas the VWC limit for determination of soil moisture content is 5 kg m-2, NDII at these VWC values for corn was very close to saturation, therefore the VWC limit was set to 4 kg m-2. For 23 June 2005, 12% of the area had VWC greater than 4 kg m-2 (Fig. 5), which represented the areas of woodlands in the land-cover classification. However, for 17 July 2005, 35% of the area had VWC greater than 4 kg m-2 because of the growth in corn (data not shown). Therefore, the NDII-VWC data could be used as a mask for soil moisture retrievals.

Fig. 5. Vegetation Water Contents (kg m-2) over central Iowa during SMEX05. NDII on 23 June 2005 was obtained

from atmospherically corrected AWiFS data and used with the USDA-NASS land-cover classification for 2005. On this date, the areas in cyan represent deciduous hardwood woodlands the occur along streams and rivers.

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5. DETECTION OF LEAF WATER STRESS

Water flow through plants follows an exact analogy of electrons flowing through a simple electric circuit 12. Charge is analogous to water volume, voltage is analogous to leaf and soil water potential (Ψleaf, Ψsoil), and electron flow is analogous to water flow resulting from a potential difference. Leaf RWC is defined as the leaf EWT divided by the leaf EWT at full turgor (assumed to be 0.15 mm), which by definition has Ψleaf = 0 MPa. Generally, RWC at 0.85 to 0.8 corresponds to Ψleaf at about -1.5 MPa. The electric analog model was run for various Ψsoil, from 0.0 to -0.5 MPa at 15 minute intervals to determine Ψleaf over a day. From the leaf capacitance, leaf EWT was calculated for the resulting Ψleaf 12. The same weather data, a summer’s day in Ames, Iowa, were used to drive stomatal conductance and transpiration.

In spite of large differences in Ψsoil, transpiration, and water flow, differences in Ψleaf, from about 0830 to 1200 hours were small (Fig. 6a), resulting in very small differences of leaf EWT (Fig. 6b) of about 0.002 mm. Even for LAI of about 5 m2 m-2, the differences in canopy EWT would be about 0.01 mm, about one-ninth the accuracy estimated in Fig. 1. The difference in canopy EWT between morning and afternoon overpasses of MODIS is about 0.01 mm, so for an LAI = 5 m2 m-2, a difference of canopy EWT of 0.05 mm is about one-half the accuracy estimated in Fig. 1. Therefore, detection of plant water stress is unlikely using SWIR-based indices such as NDII. Canopy inversion procedures offer alternative methods that have potential for detecting leaf water stress in the SWIR 18,19. For detection of water stress by remote sensing, the reduction in transpiration leads to less loss of latent heat and increased leaf temperatures which can be detected at multiple scales 20.

6. CONCLUSIONS The accuracy requirement for detection of leaf water stress is much less than the accuracy for estimating canopy EWT from NDII (Fig. 1), so it is unlikely that the incipient stages of plant water stress could be detected using shortwave infrared reflectances. The accuracy requirement for detection of soil moisture content from microwave data is vegetation water content < 1 kg m-2; so the accuracy in Fig. 1 indicates that vegetation water content can be estimated for soil moisture content retrievals using microwave data up to the VWC limit defined both by microwave properties and NDII. A third application of SWIR reflectances is the estimation of fuel moisture content for wildfire potential. Unfortunately, wildfire models use percent water content on a fresh weight or dry weight basis, so it is uncertain at this time if SWIR

Fig. 6. Change in (A) leaf water potential (Ψleaf ) and (B) leaf equivalent water thickness (EWT) over a day for

various levels of soil water potential (Ψsoil). Transpiration, water flow and Ψleaf were simulated using an electric circuit analog 12. The changes in leaf EWT are very small and indicate that based on the accuracy of the NDII-EWT relationship, leaf water stress is not detectable using SWIR reflectances.

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reflectances will be useful. The relationship between canopy EWT and NDII is affected by soil background reflectances, particularly if the surface is wet or dry. This may not be a factor in the analysis of actual images, because when the surface is wet, evaporation of moisture will cause clouds, that the land surface will be masked. Therefore, NDII may be a robust backup algorithm for MODIS as a standard data product.

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16. C. S. T. Daughtry, E. R. Hunt, Jr., and J. E. McMurtrey, III, “Assessing crop residue cover using shortwave infrared reflectances,” Remote Sens. Environ. 90, 126-134, (2004).

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18. Y.-B Cheng, P. J. Zarco–Tejada, D. Riano, C. A. Rueda and S. L. Ustin, “Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes,” Remote Sens.Environ. 105, 354–366, (2006).

19. M. Trombetti, D. Riano, M. A. Rubio, Y.-B. Cheng, and S. L. Ustin, “Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for continental USA,” Remote Sens.Environ.(in press).

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