Using in-situ measurements to evaluate the new RapidEye TM satellite series for prediction of wheat nitrogen status

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1 International Journal of Remote Sensing Vol. 28, No., Month 7, 1 8 International Journal of Remote Sensing res1211.3d /6/7 14:1:12 The Charlesworth Group, Wakefield +44() Rev 7.1n/W (Jan 3) Using in-situ measurements to evaluate the new RapidEye TM satellite series for prediction of wheat nitrogen status J. U. H. EITEL{, D. S. LONG*{, P. E. GESSLER{ and A. M. S. SMITH{ {University Idaho, Forest Resources, Moscow, ID 83844, USA {USDA-ARS, CPCRC, PO Box 37, Pendleton, OR 9781, USA (Received 27 November 6; in final form 23 April 7 ) This study assessed whether vegetation indices derived from broadband RapidEye TM data containing the red edge region (69 7 nm) equal those computed from narrow band data in predicting nitrogen (N) status of spring wheat (Triticum aestivum L.). Various single and combined indices were computed from in-situ spectroradiometer data and simulated RapidEye TM data. A new, combined index derived from the Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) in ratio obtained the best regression relationships with relative SPAD chlorophyll and flag leaf N. For SPAD, r 2 values ranged from.4 to.69 (p,.1) for narrow bands and from.3 and.77 (p,.1) for broad bands. For leaf N, r 2 values ranged from.41 to.68 (p,.1) for narrow bands and.37 to.6 (p,.1) for broad bands. These results are sufficiently promising to suggest that MCARI/MTVI2 employing broadband RapidEye TM data is useful for predicting wheat N status. ; 1. Introduction Historically, leaf tissue tests, sap nitrogen tests and chlorophyll meters have been used to estimate the nitrogen (N) status of wheat (Triticum aestivum L.) and assist growers in deciding whether to apply supplemental fertilizer N during the growing season (Westcott et al ). Remote sensing has been proposed as a cost-effective < alternative to these ground based methods for rapidly detecting crop N deficiencies across farm fields (Wright et al. 3). Chlorophyll indices, derived from remote sensing information, exploit the strong effect that variations in crop chlorophyll concentration have on reflectance in the green ( 9 nm) and red edge (69 7 nm) wavelengths and the weak effect they have on reflectance in the red (6 68 nm) wavelengths (e.g. Daughtry et al., Haboudane et al. 2). The position of the red edge inflection point wavelength (l i ) has been shown to be correlated with chlorophyll concentration and to be insensitive to soil background and atmospheric effects (Demetriades-Shah et al. 199, Blackburn et al. 1998). = Ground-based sensing systems are commercially available that provide red edge information for determining crop N status at key growth stages and fine scales (( m) (Zillman et al. 6). However, many satellite systems are characterized by low temporal and spatial (. m) resolution, which limits their use for this purpose *Corresponding author. dan.long@oregonstate.edu International Journal of Remote Sensing ISSN print/issn online # 7 Taylor & Francis DOI: 1.18/

2 2 J. U. H. Eitel et al. International Journal of Remote Sensing res1211.3d /6/7 14:1:1 The Charlesworth Group, Wakefield +44() Rev 7.1n/W (Jan 3) By the end of 7, the commercial provider RapidEye TM plans to provide broadband satellite data including a red edge band (69 7 nm). Imagery will be available worldwide with a temporal resolution of. days (at nadir) and a pixel resolution of m. The availability of a red edge band combined with relatively high spatio-temporal resolution suggests that the new RapidEye TM series may provide useful information for crop N management. The objective of this study was to evaluate the suitability of chlorophyll indices derived from RapidEye TM data for predicting the relative chlorophyll and N status of spring wheat during mid-season. 2. Field measurements Hard red spring wheat was grown within three dryland fields, i.e. an 11 ha field (F1) near Potlatch, Idaho; and a 71 ha field (F2) and 49 ha field (F3) near Helix, Oregon, USA (table 1). Within each field, smaller areas of the crop were visually identified from the ground that differed in degree of greenness. Dark green areas tended to 1 have above average crop biomass whereas lighter green areas had below average biomass. The crop was sampled for N-related attributes before canopy closure (early heading stage). An equal number of sampling plots (363 m) were randomly placed within dark green or light green areas and totalled 42 in F1, in F2 and 2 in F3. Within each plot, relative leaf chlorophyll was measured in randomly selected flag leaves using a Minolta SPAD 2 chlorophyll meter. Thirty additional flag > leaves were randomly collected and analysed in the laboratory for N concentration using an automated dry combustion analyzer. The flag leaf N and SPAD values of a plot were taken as the average of these measurements. Canopy spectra were acquired under cloud-free conditions with an ASD 2 FieldSpecH Pro spectroradiometer that measures surface radiance in 1.4 nm increments between to 1 nm. To minimize effects of illumination geometry, spectra were taken between 11 and 1 h PDT. The fibre optic probe of the spectroradiometer with a 2u field of view was held approximately 1.2 m above the ground surface and pointed towards nadir. The spectral reflectance of each plot was the average of discrete spectra obtained with the fibre optic probe at each of points equally spaced on the circumference of a circle 2 m in diameter. A SpectralonH reference panel was used to convert radiance values to reflectance and ensure that spectroradiometer performance was consistent throughout the period of spectra collection Spectral indices The Transformed Chlorophyll Absorption Reflectance Index (TCARI) (Haboudane et al. 2) and Modified Chlorophyll Absorption Reflectance Index (MCARI) Table 1. Location of fields, mean annual rainfall, number of plots and mean SPAD values for? dark and light coloured plots. 1 4 Field Location (latitude/longitude) Mean annual rainfall (mm) Number of plots Mean SPAD value Dark Light F u N/116.86u W F2 4.81u N/118.67u W F3 4.84u N/118.68u W

3 International Journal of Remote Sensing res1211.3d /6/7 14:1:1 The Charlesworth Group, Wakefield +44() Rev 7.1n/W (Jan 3) Table 2. Single and combined indices used in this study. Narrow band (1.4 nm) indices are denoted by n and broadband RapidEye TM (. nm) indices by b. RapidEye TM bands band 2 ( 9 nm), band 3 (6 68 nm), band 4 (69 7 nm) and band (76 8 nm). A Vegetation index Equation{ Reference Normalized Differential NDVI(n)(R 8 R 67 )/(R 8 + R 67 ) Rouse et al. Vegetation Index (NDVI) NDVI(b)(R band R band3 )/(R band + R band3 ) (1974) Modified Chlorophyll Absorption in Reflectance MCARI(n)[((R 7 R 67 ).2) (R 7 R )](R 7 /R 67 ) Daughtry et al. () Index (MCARI) MCARI(b)[((R band4 R band3 ).2) (R band 4 R band 2 )] (R band 4 /R band 3 ) TCARI(n)3[((R 7 R 67 ).2) Haboudane et al. (R 7 R )(R 7 /R 67 )] (2) Transformed Chlorophyll Absorption in Reflectance Index (TCARI) (Daughtry et al. ) were designed to minimize the combined effects of soil reflectance and non-photosynthetic surfaces (table 2). Haboudane et al. (2) and Zarco-Tejada et al. (4) combined these indices in ratio with the Optimized Soil Adjusted Vegetation Index (OSAVI) (Rondeaux et al. 1996) to further reduce contributions from soil reflectance and increase sensitivity to chlorophyll. Haboudane et al. (4) found the second Modified Triangular Vegetation Index (MTVI2) to be a better predictor of leaf area index (LAI) than OSAVI, due to lower sensitivity to variation in plant chlorophyll and resistance to the saturation phenomena at higher LAI values. Following previous work (Haboudane et al. 2, Zarco-Tejada et al. 4), where a combined index was formed from MCARI or TCARI by division with OSAVI, this study further evaluates a new combined index derived from the ratio of MCARI and MTVI2. 4. Data analysis Remote sensing letters 3 TCARI(b)3[((R band4 R band3 ).2) (R band4 R band2 )(R band4 /R band3 )] Greenness Index (GI) GI(n)R 4 /R 677 GI(b)R band2 /R band3 Triangular Vegetation Index (TVI) TVI(n). [1 (R 7 R ) (R 67 R )] TVI(b). [1 (R band4 R band2 ) (R band3 R band2 )] Broge and Leblanc () Modified TVI-2 MTVI21. [1.2(R 8 R ) 2.(R 67 R )]/[(2R ) 2 (6R 8 R 1/2 67 ).] 1/2 Haboudane et al. (4) Combined Index TCARI(n)/OSAVI(h) Haboudane et al. TCARI(b)/OSAVI(m) MCARI(n)/MTVI(h) MCARI(b)/MTVI(m) (2) The band equivalent reflectance (BER) provides a diagnostic indication of potential sensor performance (Smith et al. ). The hyperspectral reflectance data were convolved with the spectral response functions of each RapidEye TM band to compute BER:, R x ~ Xl max X l max r i r i r i ð1þ i~l min i~l min where R x is BER for band x, l min is starting wavelength of the filter function of band x, l max is the ending wavelength of the filter function of band x, r i is the relative

4 4 J. U. H. Eitel et al. International Journal of Remote Sensing res1211.3d /6/7 14:1:16 The Charlesworth Group, Wakefield +44() Rev 7.1n/W (Jan 3) response for band x at wavelength i and r i is the reflectance measured by spectroradiometer at wavelength i. Various spectral indices were computed from both narrow band data and BER data (. nm spectral resolution) (table 2). Simple linear regression was used to determine the relationship between each spectral index and SPAD value, or flag leaf N. The coefficient of determination (r 2 ) was used to compare the performance of the vegetation indices. Equivalence testing (Robinson et al. ) was used to determine whether the predicted values from a regression model for a given field were similar to the observed values used to develop models for other fields. The following regions of equivalence for the regression intercept (I ) and slope (I 1 ) were chosen: 1. I ȳ % for intercept and 2. I 1 1. % for slope, where ȳ is the mean of the differences between predicted values and the mean of the series of predicted values, 1. is the value of the expected slope between predicted and observed values and % is the arbitrarily chosen region for both the intercept and slope. The null hypothesis of dissimilarity is rejected if the interval of equivalence contains the joint two one-sided 9% confidence intervals (a.) for the estimated slope or intercept parameters of a regression model.. Results and discussion For narrow band data, the best indices for correlation with SPAD values were TCARI (r 2..29), MCARI (r 2..23), TCARI/OSAVI (r 2..39) and MCARI/ MTVI2 (r 2..4) (table 3). For correlation with flag leaf N, the best indices were again TCARI (r 2..26), MCARI (r 2..), TCARI/OSAVI (r 2..37) and MCARI/MTVI2 (r 2..41). In contrast, regression results for conventional indices NDVI, GI and TVI showed poorer relationships (r 2,.12). Across all fields, the new combined index MCARI/MTVI2 (r 2 >.6) was more highly correlated with SPAD than TCARI/OSAVI (r 2..4). Within individual fields, MCARI/MTVI2 yielded r 2.69 in F1, r 2.6 in F2 and r 2.4 in F3 versus TCARI/OSAVI, which yielded r 2.39 in F1, r 2.1 in F2 and r 2.44 in F3. Similarly, across all fields, index MCARI/MTVI2 was more highly correlated with flag leaf N than TCARI/OSAVI (r 2.48 for MCARI/MTVI2; r 2.36 for TCARI/OSAVI). In individual fields, MCARI/MTVI2 yielded r 2.68 for F1, r 2.41 for F2 and r 2.42 for F3 versus TCARI/OSAVI, which yielded r 2.46 for F1, r 2.37 for F2 and r 2.38 for F3. For broadband data, the MCARI/MTVI2 index (r 2.2) explained more variance in SPAD values than the TCARI/OSAVI index (r 2,), apparently due to less sensitivity to soil background effects and variations in LAI, yielding r 2.77 for F1, r 2.62 for F2 and r 2.3 for F3. Overall, narrow band MCARI/MTVI2 was slightly less correlated than broadband MCARI/MTVI2 with SPAD values (r 2.6 versus.1) or flag leaf N (r 2.48 versus.31) thus suggesting that the information loss is relatively small if broadband instead of narrow band data are employed. When applied to broadband data, indices TCARI or MCARI were weakly related to SPAD or flag leaf N likely because of insufficient spectral resolution for discriminating narrow chlorophyll absorption features or greater susceptibility to confounding factors such as illumination geometry and canopy architecture (Broge and Leblanc ). B

5 International Journal of Remote Sensing res1211.3d /6/7 14:1:16 The Charlesworth Group, Wakefield +44() Rev 7.1n/W (Jan 3) Table 3. Number of observations (n), coefficient of determination (r 2 ), and p-value for various spectral indices used as regression estimators of SPAD value C and flag leaf nitrogen (N) in wheat fields. SPAD values Flag leaf nitrogen (%) Narrow band (1.4 nm) Broad band (. nm) Narrow band (1.4 nm) Broad band (. nm) r 2 p r 2 p r 2 p r 2 p Field Index n Remote sensing letters F1 NDVI TCARI 42.48,.1* ,.1*.8.41 MCARI 42.43,.1* GI TVI TCARI/OSAVI 42.39,.1* ,.1*.9.27 MCARI/MTVI 42.69,.1*.77,.1*.68,.1*.6,.1 F2 NDVI TCARI.1,.1* ,.1*.11.8 MCARI.,.1*.33,.1*.,.1*.18,.1 GI.32,.1*.29,.1* TVI ,.1* ,.1 TCARI/OSAVI.1,.1* ,.1* MCARI/MTVI.6,.1*.62,.1*.41,.1*.37,.1 F3 NDVI TCARI 2.29,.1*.2,.1*.32,.1*.22,.1 MCARI 2.23,.1* ,.1*.19,.1 GI TVI ,.1*.6..2,.1 TCARI/OSAVI 2.44,.1* ,.1* MCARI/MTVI 2.4,.1*.3,.1*.42,.1*.37,.1 All NDVI TCARI ,.1*.9,.1*.26,.1*.1,.1 MCARI 144.3,.1*.17,.1*.2,.1*.1,.1 GI 144.1,.1*.12,.1*.6,.1*.. TVI ,.1* ,.1 TCARI/OSAVI ,.1* ,.1*..26 MCARI/MTVI 144.6,.1*.2,.1*.48,.1*.31,.1 *significant at p,

6 6 J. U. H. Eitel et al. International Journal of Remote Sensing res1211.3d /6/7 14:1:17 The Charlesworth Group, Wakefield +44() Rev 7.1n/W (Jan 3) Figure 1. Regression relationship between broadband Modified Chlorophyll Absorption Ratio Index (MCARI)/Modified Triangular Vegetation Index (MTVI2) and SPAD values for each field (F1, F2 and F3) and all three fields (ALL). In general, weakest relationships between spectral indices and SPAD were shown in F3. The single SPAD value of approximately 8 noted in figure 1(c) is considerably greater than all other values and thus might be attributable to measurement error. Removing this outlier improved the r 2 from.3 to. for regression of SPAD on broadband MCARI/MTVI2. The apparently superior ability of MCARI/MTVI2 to estimate SPAD values and flag leaf N in this study may be attributed to the combined use of chlorophyll and structural indices because the use of the latter might account for variations in LAI (Daughtry et al., Haboudane et al. 2). Though the index TCARI/OSAVI also combines chlorophyll and structural indices, weaker relationships suggest that it is negatively affected by variations in LAI and soil background. The two one-sided 9% confidence intervals for the intercept were well inside of the associated intervals of equivalence for the equivalence tests of predictive models (data not shown). For slope, however, confidence intervals were not completely contained by intervals of equivalence for any test of equivalence. Thus, the observed EX EO

7 International Journal of Remote Sensing res1211.3d /6/7 14:1:18 The Charlesworth Group, Wakefield +44() Rev 7.1n/W (Jan 3) and predicted values of SPAD and flag leaf N can be treated as though they were significantly dissimilar between all models. Evidence against similarity is further indicated by regression models illustrating differences in intercepts and slopes (figure 1). Accordingly, the relationships obtained between spectral indices and ground reference data varied over space and were not transferable among fields. Lack of geographic transferability is a major problem in use of remote sensing (Foody et al. 3) and, in this study, may be attributed to soil reflectance differences among fields, which would have influenced the crop spectral indices. 6. Conclusion Remote sensing letters 7 The new index MCARI/MTVI2 derived from RapidEye TM data might be a suitable alternative to indices computed from narrow band data for predicting SPAD values or leaf N concentration in dryland wheat. Further research is needed to assess the transferability of the relationships among farm fields and to determine the effects of atmosphere and spatial and spectral resolution on crop reflectance measurements quantified from RapidEye TM data. Acknowledgements This research was supported by Research Grant Award No. IS from The USA Israel Binational Agricultural Research and Development Fund. Use of trade names does not constitute an official endorsement by the USDA. References BLACKBURN, G.A., 1998, Quantifying chlorophylls and carotenoids at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sensing of Environment, 66, pp BROGE, N.H. and LEBLANC, E.,, Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, pp DAUGHTRY, C.S., WALTHALL, C.L., KIM, M.S., BROWN DE COLSTOUN, E. and MCMURTREY, J.E.,, Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74, pp DEMETRIADES-SHAH, T.H., STEVEN, M.D. and CLARK, J.A., 199, High resolution derivative spectra in remote sensing. Remote Sensing of Environment, 33, pp. 64. FOODY, G.M., BOYD, D.S. and CUTLER, M.E.J., 3, Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 8, pp HABOUDANE, D., MILLER, J.R., TREMBLAY, N., ZARCO-TEJADAD, P.J. and DEXTRAZEC, L., 2, Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment, 81, pp HABOUDANE, D., MILLER, J.R., PATTEY, E., ZARCO-TEJADA, P.J. and STRACHAN, I.B., 4, Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 9, pp ROBINSON, A.P., DURSMA, R.A. and MARSHALL, J.D.,, A regression-based equivalence test for model validation: shifting the burden of proof. Tree Physiology, 2, pp RONDEAUX, G., STEVEN, M. and BARET, F., 1996, Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment,, pp EP

8 8 Remote sensing letters International Journal of Remote Sensing res1211.3d /6/7 14:1:19 The Charlesworth Group, Wakefield +44() Rev 7.1n/W (Jan 3) SMITH, A.M.S., WOOSTER, M., DRAKE, J.N., DIPOSTSO, F., FALKOWSKI, M. and HUDAK, A.T.,, Testing the potential of multi-spectral remote sensing for retrospectively estimating fire severity in African Savanna environments. Remote Sensing of Environment, 97, pp WESTCOTT, M., ECKHOFF, J., ENGEL, R., JACOBSEN, J., JACKSON, G. and STOUGAARD, B., 1997, Flag leaf diagnosis of grain protein response to late-season N application in irrigated spring wheat. Available online at FertilizerFacts/12_Flag_Leaf_Diagnosis_of_Grain_Protein.htm (accessed 17 April 7). EQ WRIGHT, D.L., RICHIE, G., RASMUSSEN, V.P., RAMSEY, R.D. and BAKER, D., 3, Managing grain protein in wheat using remote sensing. Online Journal of Space Communication, Available online at 1 (accessed 17 April 7). ZARCO-TEJADA, P.J., MILLER, J.R., MORALES, A., BERJON, A. and AGUERA, J., 4, Hyperspectral indices and model simulation for chlorophyll estimation in opencanopy crops. Remote Sensing of Environment, 9, pp ZILLMANN, E., GRAEFF, S., LINK, J., BATCHELOR, W.D. and CLAUPEIN, W., 6, Assessment 1 of cereal nitrogen requirements derived by optical on-the-go sensors on heterogeneous soils. Agronomy Journal, 98, pp