Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection

Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture RADAR-SAR) opened new opportunities to develop agro-hydrological applications. Indeed, it represents a valuable source of data for operational use, due to the high spatial and temporal resolutions. Although X-band is not the most suitable to model agricultural and hydrological processes, an assessment of vegetation development can be achieved combing optical vegetation indices (VIs) and SAR backscattering data. In this paper, a correlation analysis has been performed between the crossed horizontal-vertical (HV) backscattering (s°HV) and optical VIs (VIopt) on several plots. The correlation analysis was based on incidence angle, spatial resolution and polarization mode. Results have shown that temporal changes of s°HV (Δs°HV) acquired with high angles (off nadir angle; θ > 40°) best correlates with variations of VIopt (ΔVI). The correlation between ΔVI and Δs°HV has been shown to be temporally robust. Based on this experimental evidence, a model to infer a VI from s° (VISAR) at the time, ti + 1, once known, the VIopt at a reference time, ti, and Δs°HV between times, ti + 1 and ti, was implemented and verified. This approach has led to the development and validation of an algorithm for coupling a VIopt derived from DEIMOS-1 images and s°HV. The study was carried out over the Sele plain (Campania, Italy), which is mainly characterized by herbaceous crops. In situ measurements included leaf area index (LAI), which were collected weekly between August and September 2011 in 25 sites, simultaneously to COSMO-SkyMed (CSK) and DEIMOS-1 imaging. Results confirm that VISAR obtained using the combined model is able to increase the feasibility of operational satellite-based products for supporting agricultural practices. This study is carried out in the framework of the COSMOLAND project (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI).

[1]  Francesco Mattia,et al.  On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[2]  G. Foody,et al.  Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions , 2012 .

[3]  Thuy Le Toan,et al.  Multitemporal ERS SAR analysis applied to forest mapping , 2000, IEEE Trans. Geosci. Remote. Sens..

[4]  Mahta Moghaddam,et al.  Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia , 2006 .

[5]  Antonino Maltese,et al.  Comparing actual evapotranspiration and plant water potential on a vineyard , 2011, Remote Sensing.

[6]  G. La Loggia,et al.  Surface soil humidity retrieval by means of a semi-empirical coupled SAR model , 2010, Remote Sensing.

[7]  Thuy Le Toan,et al.  Relating forest biomass to SAR data , 1992, IEEE Trans. Geosci. Remote. Sens..

[8]  G. La Loggia,et al.  Sensitivity analysis on the relationship between vegetation growth and multi-polarized radar data , 2009, Remote Sensing.

[9]  Antonino Maltese,et al.  Surface soil humidity retrieval using remote sensing techniques: a triangle method validation , 2010, Remote Sensing.

[10]  Yousef A. Al-Rumkhani,et al.  Use of remote sensing for irrigation scheduling in arid lands of Saudi Arabia , 2004 .

[11]  M. Minacapilli,et al.  Thermal Inertia Modeling for Soil Surface Water Content Estimation: A Laboratory Experiment , 2012 .

[12]  Yrjö Rauste,et al.  Multi-temporal JERS SAR data in boreal forest biomass mapping , 2005 .

[13]  D. Aubert,et al.  Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall-runoff model , 2003 .

[14]  Antonino Maltese,et al.  Mapping evapotranspiration on vineyards: a comparison between Penman-Monteith and energy balance approaches for operational purposes , 2012, Remote Sensing.

[15]  F. Henderson,et al.  Principles and Applications of Imaging Radar , 1998 .

[16]  Paul D. Bates,et al.  Flood Detection in Urban Areas Using TerraSAR-X , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Imen Gherboudj,et al.  Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data , 2011 .

[18]  G. La Loggia,et al.  A sensitivity analysis of a surface energy balance model to LAI (Leaf Area Index) , 2008, Remote Sensing.

[19]  Alex C. Lee,et al.  Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia , 2006 .

[20]  François Houllier,et al.  Retrieval biomass of a large Venezuelan pine plantation using JERS-1 SAR data. Analysis of forest structure impact on radar signature , 2002 .

[21]  J. Norman,et al.  Instrument for Indirect Measurement of Canopy Architecture , 1991 .

[22]  Mario Minacapilli,et al.  Comparative analysis of surface energy balance models for actual evapotranspiration estimation through remotely sensed images , 2012 .

[23]  João Roberto dos Santos,et al.  Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest , 2003 .

[24]  Richard K. Moore,et al.  Microwave Remote Sensing, Active and Passive , 1982 .

[25]  Yer bilimleri,et al.  Global Monitoring for Environment and Security , 2010 .

[26]  Marc L. Imhoff,et al.  Radar backscatter and biomass saturation: ramifications for global biomass inventory , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Antonino Maltese,et al.  Critical analysis of the thermal inertia approach to map soil water content under sparse vegetation and changeable sky conditions , 2012, Remote Sensing.

[28]  Antonino Maltese,et al.  Daily evapotranspiration assessment by means of residual surface energy balance modeling: A critical analysis under a wide range of water availability , 2012 .

[29]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[30]  Rajat Bindlish,et al.  Parameterization of vegetation backscatter in radar-based, soil moisture estimation , 2001 .

[31]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[32]  Emanuele Santi,et al.  The potential of multi-temporal Cosmo-Skymed SAR images in monitoring soil and vegetation , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[33]  Paul D. Bates,et al.  Remote sensing and flood inundation modelling , 2004 .

[34]  P. Moran.  Notes on continuous stochastic phenomena. , 1950, Biometrika.

[35]  P. Bates,et al.  Critical analysis of thermal inertia approaches for surface soil water content retrieval , 2013 .