{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:30:19Z","timestamp":1760243419318,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2013,3,14]],"date-time":"2013-03-14T00:00:00Z","timestamp":1363219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u00b0HV) 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\u00b0HV (\u0394s\u00b0HV) acquired with high angles (off nadir angle; \u03b8 &gt; 40\u00b0) best correlates with variations of VIopt (\u0394VI). The correlation between \u0394VI and \u0394s\u00b0HV has been shown to be temporally robust. Based on this experimental evidence, a model to infer a VI from s\u00b0 (VISAR) at the time, ti + 1, once known, the VIopt at a reference time, ti, and \u0394s\u00b0HV 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\u00b0HV. 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).<\/jats:p>","DOI":"10.3390\/rs5031389","type":"journal-article","created":{"date-parts":[[2013,3,14]],"date-time":"2013-03-14T12:22:04Z","timestamp":1363263724000},"page":"1389-1404","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection"],"prefix":"10.3390","volume":"5","author":[{"given":"Fulvio","family":"Capodici","sequence":"first","affiliation":[{"name":"Dipartimento di Agraria, Universit\u00e0 di Napoli \"Federico II\", Via Universit\u00e0 100, I-80055 Portici (NA), Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0251-4668","authenticated-orcid":false,"given":"Guido","family":"D'Urso","sequence":"additional","affiliation":[{"name":"Dipartimento di Agraria, Universit\u00e0 di Napoli \"Federico II\", Via Universit\u00e0 100, I-80055 Portici (NA), Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2778-4680","authenticated-orcid":false,"given":"Antonino","family":"Maltese","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Civile Ambientale, Aerospaziale, dei Materiali, Universit\u00e0 degli Studi di Palermo, viale delle Scienze, Ed. 8, I-90128 Palermo (PA), Italy"}]}],"member":"1968","published-online":{"date-parts":[[2013,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Maltese, A., Cammalleri, C., Capodici, C., Ciraolo, G., Colletti, F., La Loggia, G., and Santangelo, T. (2011, January 19\u201322). Comparing Actual Evapotranspiration and Plant Water Potential on a Vineyard. Prague, Czech Republic.","DOI":"10.1117\/12.899070"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ciraolo, G., Cammalleri, C., Capodici, F., D\u2019Urso, G., and Maltese, A. (2012, January 24\u201327). Mapping Evapotranspiration on Vineyards: A Comparison between Penman-Monteith and Energy Balance Approaches for Operational Purposes. Edinburgh, UK.","DOI":"10.1117\/12.974967"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Capodici, F., Maltese, A., Ciraolo, G., D\u2019Urso, G., and La Loggia, G. (2010, January 20\u201323). Surface Soil Humidity Retrieval by Means of a Semi-Empirical Coupled SAR Model. Toulouse, France.","DOI":"10.1117\/12.865096"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Maltese, A., Cammalleri, C., Capodici, F., Ciraolo, G., and La Loggia, G. (2010, January 20\u201323). Surface Soil Humidity Retrieval Using Remote Sensing Techniques: A Triangle Method Validation. Toulouse, France.","DOI":"10.1117\/12.865089"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"92","DOI":"10.2136\/sssaj2011.0122","article-title":"Thermal inertia modeling for soil surface water content estimation: A laboratory experiment","volume":"76","author":"Minacapilli","year":"2012","journal-title":"Soil Sci. Soc. Am. J"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maltese, A., Bates, P.D., Capodici, F., Cannarozzo, M., Ciraolo, G., and La Loggia, G. (2013). A critical analysis of thermal inertia approaches for surface soil water content retrieval. Hydrol. Sci. J., in press.","DOI":"10.1080\/02626667.2013.802322"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Maltese, A., Capodici, F., Corbari, C., Ciraolo, G., La Loggia, G., and Sobrino, J.A. (2012, January 24\u201327). Critical Analysis of the Thermal Inertia Approach to Map Soil Water Content under Sparse Vegetation and Changeable Sky Conditions. Edinburgh, UK.","DOI":"10.1117\/12.975676"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/BF03030879","article-title":"Use of remote sensing for irrigation scheduling in arid lands of Saudi Arabia","volume":"32","author":"Din","year":"2004","journal-title":"J. Indian Soc. Remote"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.jhydrol.2012.05.042","article-title":"Daily evapotranspiration assessment by means of residual surface energy balance modeling: a critical analysis under a wide range of water availability","volume":"452","author":"Cammalleri","year":"2012","journal-title":"J. Hydrol"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chang, N.-B., and Hong, Y. (2012). Multiscale Hydrologic Remote Sensing\u2014Perspectives and Applications, Taylor & Francis Group-CRC Press.","DOI":"10.1201\/b11279"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0022-1694(03)00229-4","article-title":"Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall runoff model","volume":"280","author":"Aubert","year":"2003","journal-title":"J. Hydrol"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1002\/hyp.5649","article-title":"Remote sensing and flood inundation modeling","volume":"18","author":"Bates","year":"2004","journal-title":"Hydrol. Process"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1109\/TGRS.2009.2029236","article-title":"Flood detection in urban areas using TerraSAR-X","volume":"48","author":"Mason","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_14","unstructured":"eoPortal Directory Available online: https:\/\/directory.eoportal.org\/web\/eoportal\/satellite-missions (accessed on 20 January 2013)."},{"key":"ref_15","unstructured":"Global Monitoring for Environment and Security (GMES)-Observing the Earth Available online: http:\/\/www.esa.int\/Our_Activities\/Observing_the_Earth\/GMES\/SAR_missions (accessed on 22 January 2013)."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1109\/TGRS.1995.8746034","article-title":"Radar backscatter and biomass saturation: ramifications for global biomass inventory","volume":"33","author":"Imhoff","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.rse.2005.05.002","article-title":"Multi-temporal JERS SAR data in boreal forest mapping","volume":"97","author":"Rauste","year":"2005","journal-title":"Remote Sens. Environ"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1080\/01431160500486732","article-title":"The potential and challenge of remote sensing-based biomass estimation","volume":"27","author":"Lu","year":"2006","journal-title":"Int. J. Remote Sens"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.isprsjprs.2012.03.011","article-title":"Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions","volume":"70","author":"Cutler","year":"2012","journal-title":"ISPRS J. Photogramm"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/S0034-4257(01)00236-X","article-title":"Retrieval biomass of a large Venezuelan pine plantation using JERS-1 SAR data\u2014Analysis of forest structure impact on radar signature","volume":"79","author":"Castel","year":"2002","journal-title":"Remote Sens. Environ"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.rse.2005.10.019","article-title":"Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia","volume":"100","author":"Lucas","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.rse.2005.09.020","article-title":"Integration of radar and Landsat-derived foliage projected cover for woody regrowth mapping, Queensland, Australia","volume":"100","author":"Lucas","year":"2006","journal-title":"Remote Sens. Environ"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.rse.2002.12.001","article-title":"Airborne P-band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest","volume":"87","author":"Santos","year":"2003","journal-title":"Remote Sens. Environ"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Santi, E., Pettinato, S., Paloscia, S., Brogioni, M., Fontanelli, G., Pampaloni, P., Macelloni, G., and Montomoli, F. (2011, January 24\u201329). The Potential of Multi-Temporal Cosmo-SkyMed SAR Images in Monitoring Soil and Vegetation. Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049360"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Balenzano, A., Satalino, G., Belmonte, G., D\u2019Urso, G., Capodici, F., Iacobellis, V., Gioia, A., Rinaldi, M., Ruggieri, S., and Mattia, F. (2011, January 24\u201329). On the Use of Multi-Temporal Series of Cosmo-SkyMed Data for Landcover Classification and Surface Parameter Retrieval over Agricultural Sites. Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6048918"},{"key":"ref_26","unstructured":"Available online: http:\/\/www.irrisat.it\/ (accessed on 15 July 2012)."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"818","DOI":"10.2134\/agronj1991.00021962008300050009x","article-title":"Instrument for indirect measurement of canopy architecture","volume":"83","author":"Welles","year":"1991","journal-title":"Agron. J"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"71040K","DOI":"10.1117\/12.800333","article-title":"A sensitivity analysis of a surface energy balance model to LAI (Leaf Area Index)","volume":"7104","author":"Maltese","year":"2008","journal-title":"Proc. SPIE"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/36.842003","article-title":"Multitemporal ERS SAR analysis applied to forest mapping","volume":"38","author":"Quegan","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on continuous stochastic phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"ref_31","unstructured":"Quegan, S., Le Toan, T., Yu, J.J., Ribbes, F., and Floury, N. (1998, January 21\u201323). Estimating Forest Area with Multitemporal ERS Data. Noordwijk, the Netherlands."},{"key":"ref_32","unstructured":"Le Toan, T. (2010, January 6\u201311). SAR Image Properties. Lanzhou, China."},{"key":"ref_33","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1986). Microwave Remote Sensing: Active and Passive, Artech House, Inc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.rse.2010.07.011","article-title":"Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data","volume":"115","author":"Gherboudj","year":"2011","journal-title":"Remote Sens. Environ"},{"key":"ref_35","unstructured":"Capodici, F., La Loggia, G., D\u2019Urso, G., Maltese, A., and Ciraolo, G. (September, January 31). Sensitivity Analysis on the Relationship between Vegetation Growth and Multi-Polarized Radar Data. Berlin, Germany."},{"key":"ref_36","first-page":"281","article-title":"Some Methods for Classification and Analysis of Multivariate Observations","volume":"1","author":"MacQueen","year":"1967","journal-title":"Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1109\/36.134089","article-title":"Relating forest biomass to SAR data","volume":"30","author":"Beaudoin","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_38","unstructured":"Leckie, D.G., and Ranson, K.J. (1998). Principles and Applications of Imaging Radar, John & Wiley."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/S0034-4257(00)00200-5","article-title":"Parameterization of vegetation backscatter in radar-based, soil moisture estimation","volume":"76","author":"Bindlish","year":"2001","journal-title":"Remote Sens. Environ"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/3\/1389\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:45:33Z","timestamp":1760219133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/5\/3\/1389"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,3,14]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2013,3]]}},"alternative-id":["rs5031389"],"URL":"https:\/\/doi.org\/10.3390\/rs5031389","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2013,3,14]]}}}