ANALYSIS OF LEAF AREA INDEX AND SOIL WATER CONTENT RETRIEVAL FROM AGRISAR DATA SETS

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1 ANALYSIS OF LEAF AREA INDEX AND SOIL WATER CONTENT RETRIEVAL FROM AGRISAR DATA SETS D'Urso, G. () ; Dini, L. () ; Richter, K. () ; Palladino M. () () DIIAT, Faculty of Agraria, University of Naples Federico II via Università, Portici (Naples), Italy () Italian Space Agency, Earth Observation Unit, c.p., Matera, Italy Corresponding author: durso@unina.it ABSTRACT In this study, E-SAR data in bands X, L and C acquired during the AGRISAR campaign in July have been analysed to evaluate the spatial distribution of Leaf Area Index and soil water content in different types of crops. To this end, regular grids of measurements of soil water content have been acquired in coincidence to the flight acquisitions in two fields. LAI has been measured in distinct locations within the fields by means of the Licor LAI- optical analyser and maps of LAI have been derived from CASI data by using an inversion procedure of the PROSAILH canopy radiative transfer code. The resulting map has been used to assess the correlation between the LAI estimated by using optical data and the surface backscattering in bands X, C and L of AGRISAR. Diversely, in the case of soil water content, the analysis has been performed by using a grid of approximately measurements taken by using an FDR sensor to detect the volumetric water content in the upper - cm of soil in a vegetated field. SAR data in bands C and L have been considered to verify the possibility of detecting soil water content in presence of a vegetation cover. Successively, the application of semi-empirical models for LAI (X-band) and for soil water content (C and L bands) has been evaluated with and without on-site calibration. Keywords: AgriSAR, leaf area index, soil water content, backscattering. INTRODUCTION Leaf Area Index (LAI) and soil water content θ are two variables of outmost importance in the study of hydrological processes of land surfaces, due to their influences in the exchange of water and energy in the soil-plant system and their critical role in the application and validation of distributed hydrological models, i.e. for infiltration studies and run-off predictions []. Furthermore, the knowledge of canopy development in relation to soil water content is of great usefulness in precision-farming practices related to the management of soil and water resources. Various Earth Observation techniques have been widely used in recent years to monitor the temporal and spatial variability of LAI and θ. In the case of LAI, optical sensors with different spatial and spectral resolutions have been extensively exploited to provide an estimation of LAI with satisfactory accuracy for most applications []. However, cloud coverage may represent a strong limitation in using optical sensors for all the applications which require a frequent revisiting coverage. Active and passive microwave sensors have proven their potentiality for detecting soil water content in several recent studies []; in particular, space-borne active microwave imaging techniques are of special attractiveness thanks to their fine spatial resolution and the repetitiveness of measurements. Physically based methods have been developed to retrieve soil water content from radar backscattering [], but they generally require an accurate characterisation of soil surface roughness and its spatial variability, which is very difficult to obtain over large areas with enough detail. A possibility to circumvent this problem relies on the development of semi-empirical models of backscattering based on the use of multi-frequency and multi-polarised SAR data [, ]. In this paper, we address these two issues by analysing the multi-frequency and multi-polarised acquisitions made during the AGRISAR campaign by means of the E-SAR system operated by DLR, aiming at the development of possible operative products for the Sentinel- mission, developed by ESA in the framework of GMES (Global Monitoring for Environment and Security, [,].

2 LAI data. Mean value.±..±..±... field field field MAIZE SUGAR-BEET Fig.. Field measurements carried out during the intensive campaign on - th July : (left) distribution and main statistics of Leaf Area Index in fields (maize), and (sugar beet); (right) surface soil water content on field, July th, data points and spatial interpolation. MATERIAL AND METHODS In-situ data acquisition and image processing During the intensive field campaign in July, ground measurements have been carried out in three different plots in order to characterise the spatial variability of Leaf Area Index, volumetric soil water content () and soil temperature (T). Leaf Area Index has been measured by means of the portable canopy analyser Licor LAI-, by using a measurement procedure based on three consecutive series of readings covering an Elementary Surface Unit (ESU) of approximately x m. The average value of LAI, resulting from the set of readings, has been considered as representative for the considered ESU. In fig. (left) the set of LAI measurements is represented; the average development of the canopy in the three fields was similar, with a LAI value of approximately.. Soil water content and temperature have been monitored in the superficial soil horizon, simultaneously to the aircrafts overpass, by using a portable probe based on frequency domain technology. This type of probe, which prototype has been developed by IMAG-DLO [], is made of a metallic wave-guide of cm length, which allows an easy insertion in the soil and quick measurements. This feature allows for the acquisition of a set of - measurements during an interval of - hours around the flight time; as such, the influence of the diurnal variation of surface soil water content is minimised. The map of soil water content resulting from the spatial interpolation of the grid of measurements on field no. is shown in fig. (right); the average soil water content of the surface layer was about., corresponding to very dry conditions. Multi-look geocoded E-SAR images in bands C, L and X acquired during the flight of July th over the Demmin site have been considered for the present analysis. Images resolution has been degraded to m with pixel value corresponding to the mean. Image data analysis (): Leaf Area Index In order to investigate on the relationship between canopy development and radar backscattering, we have considered the LAI map derived from the inversion of a canopy-radiative transfer model applied to the image acquired over the Demmin site on July th by the Compact Airborne Spectral Imager (CASI), shown in fig.. Due to the quality of image data and the elaboration performed to derive the LAI map [], this map has been considered as the ground-truth for our subsequent analysis. In analogy with the radar images, the spatial resolution of the LAI map has been degraded to m; in addition, we have considered the value of NDVI from the same CASI image at the resolution of x m. The correlation analysis has been carried out by calculating the Pearson coefficient for several fields with a range of different crops. For fields no. (sugar beet) and (maize), which are representing our measurement sites, the results are shown in fig.. As it might have been expected, the correlation results for LAI and NDVI are similar, with slightly higher values of the Pearson coefficient for NDVI compared to LAI.

3 Fig.. (Left) Colour composite of CASI image used for the (Right) Map of Leaf Area Index, calculated using a Look-up table (LUT) based inversion of radiative transfer model Prospect+SAIL of CASI data; RMSE=. and R =. (based on ground LAI measurements from fields, ). Fig.. Results of correlation analysis between backscattering and vegetation parameters for two different fields/crops. Fig. (Left) LAI map of field (Maize) from LUT inversion on CASI data; (right) LAI map estimated from multiple linear regression on multi-polarised and multi-frequency E-SAR data: R =..

4 We notice that the best results have been obtained by using L-band at VH and VV polarisations. While in the case of maize (field ) the correlation values are similar for all the three bands, the highest correlation for the sugar beet field has been found in band L-VV. In the case of winter wheat fields, we obtained the best correlation in band L with HH polarisation. Only in the case of field (sugar beet) X-band data performed better other bands, probably due to the high moisture content of the plant leaves. Overall, these results suggest that L-band, especially in vertical polarisation, is more suitable than other SAR configurations to monitor canopy development. We have explored the possibility of LAI estimation from SAR data by using a multi-linear regression approach. In the case of multiple linear regression techniques, a set of site-specific empirical parameters {bi} may be found to minimise the sum of squared errors between ground reference LAI values and the corresponding estimation LAI ˆ given by: LAI ˆ = b + b x + b x b n x n () where xi represent the radar backscattering. An example of the output of this test is shown in fig., where a comparison is presented between the LAI derived from CASI and SAR for the field no.. In spite of the correlation found in fig., we notice that the results of the LAI estimation by means of the multi-linear regression are quite deluding, with a R of.; however, the spatial patter of canopy development are quite well reproduced in this case. Image data analysis (): Soil water content The second part of our analysis has been focused on the surface soil water content. To this end, for each ground measurement of soil water content, the corresponding value of the apparent dielectric permittivity has been derived by using the relationship: ε =. +.θ + θ.θ () meas The Fresnel reflection index Γ,meas has then been calculated from the apparent dielectric permittivity ε meas as follows:. ε meas Γ, meas =. () + ε meas The estimation of the soil apparent dielectric permittivity can be carried out by means of the semi-empirical model of Oh []. This model can be applied without a-priori information on soil roughness, which is a significant advantage compared to other methods []. In the Oh model, the following polarisation ratios p and q are introduced: AΓ hh α p = = exp vv π ( ks) hv q = B = Γ exp( ks) () vv where hv is the cross-polarisation backscattering coefficient and Γ is the Fresnel reflectivity of the surface at nadir. In Eqs.() and () k=π/λ is the wave-number, A and B two empirical calibration parameters which values are fixed to and. respectively on the basis of previous applications from space-borne SAR data [,] To eliminate ks, Eqs. () and () are combined together and the following equation is derived: A α Γ q p π + = () B Γ This equation is solved to derive Γ and then ε by means of Eq.(). Although the model has been conceived for bare soils, we have tested its application to fields and, having similar LAI values for vegetation cover. The summary statistics resulting from this test are presented in Tab.. Tab. Summary of statistics for estimation of apparent dielectric permittivity by using Oh s model on Agrisar data. field measur. estim. C -band estim. L -band mean... st.dev.... field measur. estim. C -band estim. L -band mean... st.dev.... ()

5 L-band data give a better estimation of the field mean value compared to C-band for both crops; however, the single values of ε are significantly scattered around the mean, as shown by the plots in figs. and. This large scatter, as indicated by the standard deviation values, is similar in both bands, and it is likely to be related to the water content of vegetation cover. C band - Field C band - Field Fig.. Comparison of soil dielectric apparent permittivity ε from field measurements and Oh s backscattering model with E-SAR C-band data; data refers to a spatial grid of x m. Fig.. Similar to Fig., L-band data. L band - Field L band - Field CONCLUSIONS The preliminary results conducted on AGRISAR data set for the estimation of Leaf Area Index and soil water content have confirmed the findings of previous studies on the suitability of L-band SAR data. When optical data are unavailable, multi-polarised L-band observations may represent a viable solution for the estimation of land surface parameters related to hydrological processes. However, the development of physical models to derive land products such as LAI map from SAR data is very limited, but empirical approaches, i.e. multi-regression techniques, can be found to provide information on the spatial variability of canopy development. Diversely from the LAI, several modelling approaches for soil water content estimation are available in literature. The main limitation in the application of this approach is the knowledge of surface roughness, which measurement techniques are complex and often inaccurate. However, the preliminary test carried out in this study has confirmed that

6 L-band data applied to the semi-empirical model of Oh may provide an estimation of the average value at field scale even in presence of a vegetation cover in different crop types. The limitations of the present study mainly rely on the small variability of field conditions during the July Agrisar campaign in Demmin, either for the canopy development, i.e. range of LAI values, either for the soil water content. However, the present analysis can be extended to other acquisitions carried out during Agrisar project. ACKNOWLEDGMENTS This work has been carried out with the support of ESA for the project AGRISAR,, and of Italian Ministry of Agriculture and Forestry Policies under contract n.// (AQUATER Project). REFERENCES. G. D'Urso, M. Menenti and A. Santini, Regional application of one-dimensional water flow models for irrigation management, Agricultural Water Management, vol., pp. -,.. K. Richter, F. Vuolo, G. D Urso, G. Fernandez, Retrieval of crop characteristics from high resolution airborne scanner data, in proceedings of: AGRISAR and EAGLE campaigns Final Workshop,.-.., ESA/ESTEC, Nordwijk, Netherlands (in press).. G. D'Urso, M. Minacapilli, A semi-empirical approach for surface soil water content estimation from radar data without a-priori information on surface roughness, Journal of Hydrology, vol., pp. -,.. T.E. Engman, Soil moisture. In G.A. Schultz and Engman T.E. (Eds.): Remote sensing in Hydrology and Water Management, Springer, pp: -,.. European Space Agency (ESA), GMES Sentinel- mission requirements document, EOP-SM//MR-dr, //.. A.K. Fung, Z.Li, K.S. Chen, Backscattering from a randomly rough dielectric surface, IEEE Transactions on Geoscience and Remote Sensing, vol.(), pp. -,.. I. Hajnsek, R. Bianchi, M. Davidson, M. Wooding and the AGRISAR Team, AgriSAR - Airborne SAR and Optics campaigns for an improved monitoring of agricultural processes and practices, Geophysical Research Abstracts, vol.,, European Geosciences Union,.. M.A. Hilhorst, Dielectric characterization of soil. Doctoral Thesis. Wageningen Agricultural University, pp.,.. S. Jacquemoud, F. Baret, B. Andrieu, F. M. Danson, K. Jaggard, Extraction of Vegetation Biophysical Parameters by Inversion of the PROSPECT + SAIL Models on Sugar Beet Canopy Reflectance Data. Application to TM and AVIRIS Sensors, Remote Sensing of Environment, vol., pp. -,.. Y. Oh, K. Sarabandi, F.T. Ulaby, An empirical Model and an inversion technique for radar scattering from bare soil surface, IEEE Transactions on Geoscience and Remote Sensing, vol. (), pp. -,.. K. Richter, F. Vuolo, G. D Urso, L. Dini, Evaluation of different methods for the retrieval of LAI using high resolution airborne data, In: SPIE s conference proceedings: Remote Sensing for Agriculture, Ecosystems, and Hydrology, ed. M. Owe, G. D'Urso, C. M. Neale, Florence, Italy, September, in press. F.T. Ulaby, P.C. Dubois, J. van Zyl, Radar Mapping of surface soil moisture, J. Hydrology, vol., pp. -,.. P.J. van Oevelen, D.H. Hoekman, Radar backscatter inversion techniques for estimation of surface soil moisture: EFEDA-Spain and HAPEX-Sahel case studies, IEEE Transactions on Geoscience and Remote Sensing, vol. (), pp. -,.