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The complementary uses of Sentinel-1A SAR and ECOSTRESS datasets to identify vineyard growth and conditions: a case study in Sonoma County, California

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Abstract

The launch of NASA’s ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the European Space Agency’s Sentinel-1A/B synthetic aperture radar (SAR) satellites provides the opportunity to advance a multi-sensor remote sensing approach to crop monitoring. While ECOSTRESS and Sentinel-1A/B have been used separately to assess vegetation conditions, a study that quantifies the synergistic usefulness of both to monitor crops has not been performed. This study assesses the complementary uses of Sentinel-1A SAR and ECOSTRESS land surface temperature (LST) and evapotranspiration (ET) datasets to assess vine growth and conditions in blocks located in Sonoma County, California for 2018. Results indicate Sentinel-1A SAR dual-polarization backscatter measurements (\(\sigma_{{{\text{VV}}}}^{0}\) and \(\sigma_{{{\text{VH}}}}^{0}\)) have different sensitivities to vine leafiness and moisture content, based on measured vineyard field data and radiometric modeling. SAR and modeled \(\sigma_{{{\text{VV}}}}^{0}\) backscatter suggest higher sensitivity to surface conditions and trunk and cane moisture, while SAR and modeled \(\sigma_{{{\text{VH}}}}^{0}\) backscatter indicate higher sensitivity to vine leafiness and canopy moisture. ECOSTRESS LST measurements were sharpened to a 30 m resolution using a data mining sharpener and ET measurements were generated with a retrieval algorithm approach for select dates. Spearman’s rank correlation and linear regressions analyses between SAR backscatter to ECOSTRESS datasets indicate stronger relationships between \(\sigma_{{{\text{VH}}}}^{0}\) backscatter to LST and ET relative to \(\sigma_{{{\text{VV}}}}^{0}\) backscatter. The results suggest Sentinel-1A SAR \(\sigma_{{{\text{VH}}}}^{0}\) backscatter can provide indications of vine leaf volume and moisture state that can be related to LST and ET measurements, providing useful information for vineyard management.

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References

  • Anderson MC, Yang Y, Xue J, Knipper KR, Yang Y, Gao F, Hain CR, Kustas WP, Cawse-Nicholson K, Hulley G, Fisher JB (2021) Interoperability of ECOSTRESS and Landsat for mapping evapotranspiration time series at sub-field scales. Remote Sens Environ 252:112189

    Article  Google Scholar 

  • Baghdadi N, Holah N, Dubois-Fernandez P, Dupuis X, Garestier F (2006) Evaluation of polarimetric L-and P-bands RAMSES data for characterizing Mediterranean vineyards. Can J Remote Sens 32(6):380–389

    Article  Google Scholar 

  • Bisson LF, Waterhouse AL, Ebeler SE, Walker MA, Lapsley JT (2002) The present and future of the international wine industry. Nature 418(6898):696–699

    Article  CAS  PubMed  Google Scholar 

  • Blatchford ML, Mannaerts CM, Zeng Y, Nouri H, Karimi P (2019) Status of accuracy in remotely sensed and in-situ agricultural water productivity estimates: A review. Remote Sens Environ 234:111413

    Article  Google Scholar 

  • Burini A, Minchella A, Solimini D (2005) SAR in agriculture: Sensitivity of backscattering to grapes. In: Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS’05., Vol. 3, pp. 1542–1545. IEEE

  • Cable JW, Kovacs JM, Jiao X, Shang J (2014) Agricultural monitoring in northeastern Ontario, Canada, using multi-temporal polarimetric RADARSAT-2 data. Remote Sensing 6(3):2343–2371

    Article  Google Scholar 

  • Chalmers Y (2012) Insights into the relationships between yield and water in wine grapes. Grape and Wine Research and Development Corporation, Department of Agriculture, Fisheries and Forestry of the Government of Australia, Canberra

    Google Scholar 

  • Choker M, Baghdadi N, Zribi M, El Hajj M, Paloscia S, Verhoest NE, Lievens H, Mattia F (2017) Evaluation of the Oh, Dubois and IEM backscatter models using a large dataset of SAR data and experimental soil measurements. Water 9(1):38

    Article  Google Scholar 

  • Cunha M, Marçal AR, Rodrigues A (2010) A comparative study of satellite and ground-based vineyard phenology. In: Proceeding of the 29th Symposium EARSeL, pp 68–77

  • David Ballester-Berman J, Garmendia-Lopez I, Lopez-Sanchez JM, Mangas-Martin VJ (2012) Analysis of the polarimetric response of vineyards at C-band. Can J Remote Sens 38(3):223–239

    Article  Google Scholar 

  • Davitt A (2020) Informing on crop water-use, stress, and growth with integrated satellite remote sensing and modeling. CUNY Academic Works. https://academicworks.cuny.edu/gc_etds/4029. Accessed 2021

  • Davitt A, Winter JM, McDonald K (2020) Integrated crop growth and radiometric modeling to support Sentinel synthetic aperture radar observations of agricultural fields. J Appl Remote Sens 14(4):044508

    Article  Google Scholar 

  • Della Vecchia A, Ferrazzoli P, Guerriero L, Blaes X, Defourny P, Dente L, Mattia F, Satalino G, Strozzi T, Wegmuller U (2006) Influence of geometrical factors on crop backscattering at C-band. IEEE Trans Geosci Remote Sens 44(4):778–790

    Article  Google Scholar 

  • ESA (2018) Sentinel-1 SAR User Guide Introduction. http://www.esa.int/, Online. Available: https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/applications. Accessed 2021

  • Fisher JB, Lee B, Purdy AJ, Halverson GH, Dohlen MB, Cawse-Nicholson K, Wang A, Anderson RG, Aragon B, Arain MA, Baldocchi DD (2020) ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the International Space Station. Water Resour Res 56(4):e2019WR026058

    Article  Google Scholar 

  • Flores-Anderson AI, Herndon KE, Thapa RB, Cherrington E (2019) The SAR handbook: comprehensive methodologies for forest monitoring and biomass estimation

  • Frolking S, Milliman T, McDonald K, Kimball J, Zhao M, Fahnestock M (2006) Evaluation of the SeaWinds scatterometer for regional monitoring of vegetation phenology. J Geophys Res Atmos 111(D17):D17302

    Article  Google Scholar 

  • Gao F, Kustas WP, Anderson MC (2012) A data mining approach for sharpening thermal satellite imagery over land. Remote Sensing 4(11):3287–3319

    Article  Google Scholar 

  • García M, Saatchi S, Ustin S, Balzter H (2018) Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery. Int J Appl Earth Obs Geoinf 66:159–173

    Google Scholar 

  • Hallikainen MT, Ulaby FT, Dobson MC, El-Rayes MA, Wu LK (1985) Microwave dielectric behavior of wet soil-part 1: empirical models and experimental observations. IEEE Trans Geosci Remote Sens 1:25–34

    Article  Google Scholar 

  • Hamberg LJ, Fraser RA, Robinson DT, Trant AJ, Murphy SD (2020) Surface temperature as an indicator of plant species diversity and restoration in oak woodland. Ecol Indic 113:106249

    Article  Google Scholar 

  • Hannah L, Roehrdanz PR, Ikegami M, Shepard AV, Shaw MR, Tabor G, Zhi L, Marquet PA, Hijmans RJ (2013) Climate change, wine, and conservation. Proc Natl Acad Sci 110(17):6907–6912

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW (2015) An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sens Environ 158:220–234

    Article  Google Scholar 

  • Hong S, Hendrickx JM, Allen RG (2008) Comparison of remote sensing energy balance models: Sebal VS Metric. In: AGU Fall Meeting Abstracts, Vol. 2008, pp H43G-1094

  • Huang W, DeVries B, Huang C, Lang MW, Jones JW, Creed IF, Carroll ML (2018) Automated extraction of surface water extent from Sentinel-1 data. Remote Sensing 10(5):797

    Article  Google Scholar 

  • Johnson LF, Bosch DF, Williams DC, Lobitz BM (2001) Remote sensing of vineyard management zones: Implications for wine quality. Appl Eng Agric 17(4):557

    Article  Google Scholar 

  • Johnson LF, Roczen DE, Youkhana SK, Nemani RR, Bosch DF (2003) Mapping vineyard leaf area with multispectral satellite imagery. Comput Electron Agric 38(1):33–44

    Article  Google Scholar 

  • Jones GV, Duff AA, Hall A, Myers JW (2010) Spatial analysis of climate in winegrape growing regions in the western United States. Am J Enol Vitic 61(3):313–326

    Google Scholar 

  • Khabbazan S, Vermunt P, Steele-Dunne S, Ratering Arntz L, Marinetti C, van der Valk D, Iannini L, Molijn R, Westerdijk K, van der Sande C (2019) Crop monitoringusing Sentinel-1 data: A case study from The Netherlands. Remote Sens 11(16):1887

    Article  Google Scholar 

  • Knipper KR, Kustas WP, Anderson MC, Alfieri JG, Prueger JH, Hain CR, Gao F, Yang Y, McKee LG, Nieto H, Hipps LE (2019a) Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrig Sci 37(3):431–449

    Article  Google Scholar 

  • Knipper KR, Kustas WP, Anderson MC, Alsina MM, Hain CR, Alfieri JG, Prueger JH, Gao F, McKee LG, Sanchez LA (2019b) Using high-spatiotemporal thermal satellite ET retrievals for operational water use and stress monitoring in a California vineyard. Remote Sens 11(18):2124

    Article  Google Scholar 

  • Knipper KR, Kustas WP, Anderson MC, Nieto H, Alfieri JG, Prueger JH, Hain CR, Gao F, McKee LG, Alsina MM, Sanchez L (2020) Using high-spatiotemporal thermal satellite ET retrievals to monitor water use over California vineyards of different climate, vine variety and trellis design. Agric Water Manag 241:

    Article  Google Scholar 

  • Kustas WP, Anderson MC, Alfieri JG, Knipper K, Torres-Rua A, Parry CK, Nieto H, Agam N, White WA, Gao F, McKee L (2018) The grape remote sensing atmospheric profile and evapotranspiration experiment. Bull Am Meteor Soc 99(9):1791–1812

    Article  Google Scholar 

  • Longo M, Saatchi S, Keller M, Bowman K, Ferraz A, Moorcroft PR, Morton DC, Bonal D, Brando P, Burban B, Derroire G (2020) Impacts of degradation on water, energy, and carbon cycling of the Amazon tropical forests. J Geophys Res Biogeosci 125(8):e2020JG005677

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • McDonald KC (1991) Modeling microwave backscatter from tree canopies (Doctoral dissertation, University of Michigan)

  • McDonald KC, Dobson MC, Ulaby FT (1990) Using MIMICS to model L-Band multiangle and multitemporal backscatter from a walnut orchard. IEEE Trans Geosci Remote Sens 28(4):477–491

    Article  Google Scholar 

  • McDonald KC, Dobson MC, Ulaby FT (1991) Modeling multi-frequency diurnal backscatter from a walnut orchard. IEEE Trans Geosci Remote Sens, p 29

  • McNairn H, Shang J (2016) A review of multitemporal synthetic aperture radar (SAR) for crop monitoring. In: Ban Y (ed) Multitemporal remote sensing. Remote sensing and digital image processing. Springer, Cham. vol 20. https://doi.org/10.1007/978-3-319-47037-5_15.

  • McShane RR, Driscoll KP, Sando R (2017) A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents. Scientific Investigations Report 2017–5087. Reston, VA: US Geological Survey, p 19

  • Melton FS, Johnson, et al (2012) Satellite irrigation management support with the terrestrial observation and prediction system: A framework for integration of satellite and surface observations to support improvements in agricultural water resource management. IEEE J Sel Top Appl Earth Observ Remote Sens 5(6):1709–1721

    Article  Google Scholar 

  • Mishra P, Singh D (2011) Role of polarimetric indices based on statistical measures to identify various land cover classes in ALOS PALSAR data. In 2011 3rd International Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, pp 1–4

  • Monsivais-Huertero A, Judge J (2010) Comparison of backscattering models at L-band for growing corn. IEEE Geosci Remote Sens Lett 8(1):24–28

    Article  Google Scholar 

  • Montandon LM, Small EE (2008) The impact of soil reflectance on the quantification of the green vegetation fraction from NDVI. Remote Sens Environ 112(4):1835–1845

    Article  Google Scholar 

  • Montero FJ, Meliá J, Brasa A, Segarra D, Cuesta A, Lanjeri S (1999) Assessment of vine development according to available water resources by using remote sensing in La Mancha, Spain. Agric Water Manag 40(2–3):363–375

    Article  Google Scholar 

  • Moran MS, Hymer, DC, Qi J, Kerr Y (1999) Radar imagery for precision crop and soil management. In Proceedings of the fourth international conference on precision agriculture. American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI, USA, pp. 1423–1434

  • Mozell MR, Thach L (2014) The impact of climate change on the global wine industry: challenges & solutions. Wine Econ Policy 3(2):81–89

    Article  Google Scholar 

  • Mukherjee S, Joshi PK, Garg RD (2014) A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape. Adv Sp Res 54(4):655–669

    Article  Google Scholar 

  • NASA JPL (2019) ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) Mission

  • Navarro A, Rolim J, Miguel I, Catalão J, Silva J, Painho M, Vekerdy Z (2016) Crop monitoring based on SPOT-5 Take-5 and sentinel-1A data for the estimation of cropwater requirements. Remote Sens 8(6):525

    Article  Google Scholar 

  • Norman JM, Anderson MC, Kustas WP, French AN, Mecikalski JOHN, Torn R, Diak GR, Schmugge TJ, Tanner BCW (2003) Remote sensing of surface energy fluxes at 101-m pixel resolutions. Water Resour Res 39(8):1221

    Article  Google Scholar 

  • Oh Y, Sarabandi K, Ulaby FT (1992) An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Trans Geosci Remote Sens 30(2):370–381

    Article  Google Scholar 

  • Panciera R, Tanase MA, Lowell K, Walker JP (2013) Evaluation of IEM, Dubois, and Oh radar backscatter models using airborne L-band SAR. IEEE Trans Geosci Remote Sens 52(8):4966–4979

    Article  Google Scholar 

  • Patel P, Srivastava HS, Navalgund RR (2006) Estimating wheat yield: an approach for estimating number of grains using cross-polarised ENVISAT-1 ASAR data. In: Microwave remote sensing of the atmosphere and environment V. International Society for Optics and Photonics, vol 6410, p 641009

  • Pathak TB, Maskey ML, Dahlberg JA, Kearns F, Bali KM, Zaccaria D (2018) Climate change trends and impacts on California agriculture: a detailed review. Agronomy 8(3):25

    Article  Google Scholar 

  • Picón-Toro J, González-Dugo V, Uriarte D, Mancha LA, Testi L (2012) Effects of canopy size and water stress over the crop coefficient of a “Tempranillo” vineyard in south-western Spain. Irrig Sci 30(5):419–432

    Article  Google Scholar 

  • Pinter PJ Jr, Hatfield JL, Schepers JS, Barnes EM, Moran MS, DaughtryCS Upchurch DR (2003) Remote sensing for crop management. Photogram Eng Remote Sen 69(6):647–664

    Article  Google Scholar 

  • Reidmiller DR, Avery CW, Easterling DR, Kunkel KE, Lewis KL, Maycock TK, Stewart BC (2017) Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II

  • Steele-Dunne SC, Friesen J, Van De Giesen N (2012) Using diurnal variation in backscatter to detect vegetation water stress. IEEE Trans Geosci Remote Sens 50(7):2618–2629

    Article  Google Scholar 

  • Sun L, Anderson MC, Gao F, Hain C, Alfieri JG, Sharifi A, McCarty GW, Yang Y, Yang Y, Kustas WP, McKee L (2017a) Investigating water use over the Choptank River Watershed using a multisatellite data fusion approach. Water Resour Res 53(7):5298–5319

    Article  Google Scholar 

  • Sun L, Gao F, Anderson MC, Kustas WP, Alsina MM, Sanchez L, Sams B, McKee L, Dulaney W, White WA, Alfieri JG (2017b) Daily mapping of 30 m LAI and NDVI for grape yield prediction in California vineyards. Remote Sensing 9(4):317

    Article  Google Scholar 

  • Toure A, Thomson KP, Edwards G, Brown RJ, Brisco BG (1994) Adaptation of the MIMICS backscattering model to the agricultural context-wheat and canola at L and C bands. IEEE Trans Geosci Remote Sens 32(1):47–61

    Article  Google Scholar 

  • Tuck B, Gartner W, Appiah G (2017) Economic contribution of vineyards and wineries of the north, 2015. University of Minnesota. Retrieved from the University of Minnesota Digital Conservancy. https://hdl.handle.net/11299/197808

  • Ulaby FT, El-Rayes MA (1987) Microwave dielectric spectrum of vegetation-Part II: Dual-dispersion model. IEEE Trans Geosci Remote Sens 5:550–557

    Article  Google Scholar 

  • University of California Cooperative Extension (2020). Sonoma County Climatic Zones [Homepage of University of California], [Online]. http://cesonoma.ucdavis.edu/files/27208.pdf. Accessed 2021

  • van Emmerik T, Steele-Dunne SC, Judge J, van de Giesen N (2015) Impact of diurnal variation in vegetation water content on radar backscatter from maize during water stress. IEEE Trans Geosci Remote Sens 53(7):3855–3869

    Article  Google Scholar 

  • Van Leeuwen C, Destrac-Irvine A (2017) Modified grape composition under climate change conditions requires adaptations in the vineyard. Oeno One 51(2–3):147–154

    Article  Google Scholar 

  • Veloso A, Mermoz S, Bouvet A, Le Toan T, Planells M, DejouxJF Ceschia E (2017) Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-likedata for agricultural applications. Remote Sens Environ 199:415–426

    Article  Google Scholar 

  • Vreugdenhil M, Wagner W, Bauer-Marschallinger B, Pfeil I, Teubner I, Rüdiger C, Strauss P (2018) Sensitivity of Sentinel-1 backscatter to vegetation dynamics: an Austrian case study. Remote Sensing 10(9):1396

    Article  Google Scholar 

  • Wheeler SJ, Pickering GJ (2003) Optimizing grape quality through soil management practices. Food Agric Environ 1(2):190–197

    Google Scholar 

  • White WA, Alsina MM, Nieto H, McKee LG, Gao F, Kustas WP (2019) Determining a robust indirect measurement of leaf area index in California vineyards for validating remote sensing-based retrievals. Irrig Sci 37(3):269–280

    Article  Google Scholar 

  • Whitt MW, Ulaby FT (1994) Radar response of periodic vegetation canopies. Int J Remote Sens 15(9):1813–1848

    Article  Google Scholar 

  • Wiseman G, McNairn H, Homayouni S, Shang J (2014) RADARSAT-2 polarimetric SAR response to crop biomass for agricultural production monitoring. IEEE J Sel Top Appl Earth Observ Remote Sens 7(11):4461–4471

    Article  Google Scholar 

  • Xia T, Kustas WP, Anderson MC, Alfieri JG, Gao F, McKee L, Prueger JH, Geli HM, Neale CM, Sanchez L, Alsina MM (2016) Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one-and two-source modeling schemes. Hydrol Earth Syst Sci 20(4):1523–1545

    Article  Google Scholar 

  • Xue J, Anderson MC, GaoF Hain C, Sun L, Yang Y, Knipper KR, Kustas WP, Torres-Rua A, Schull M (2020) Sharpening ECOSTRESS and VIIRS land surface temperature using harmonized landsat-sentinel surface reflectances. Remote Sens Environ 251:

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors wish to thank the GRAPEX team and E. & J. Gallo Winery for access to the experimental site. Additionally, Dr. Kerry Cawse-Nicholson for advice and support on ECOSTRESS data. Lastly, Dr. Andrew Reinmann, Dr. Nir Krakauer, and Dr. Naresh Devineni for advice and comments. Portions of this work were conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract to the National Aeronautics and Space Administration. Portions of this research was supported in part by the U.S. Department of Agriculture, Agricultural Research Service. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture. U.S. Department of Agriculture is an equal opportunity provider and employer.

Funding

Portions of this work were supported by an HBCU/MSI award from the Jet Propulsion Laboratory, California Institute of Technology, through a partnership with The City College of New York, City University of New York under the Maximizing Student Potential (MSP) in STEM program.

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Authors and Affiliations

  1. Department of Earth and Atmospheric Science, The City College of New York, New York, NY, USA

    Aaron Davitt, Derek Tesser & Kyle McDonald

  2. Department of Earth and Environmental Sciences, CUNY The Graduate Center, New York, NY, USA

    Aaron Davitt, Derek Tesser & Kyle McDonald

  3. CUNY Guttman Community College, New York, NY, USA

    Derek Tesser

  4. Department of Mechanical Engineering, CUNY The City College of New York, New York, NY, USA

    Harold Gamarro

  5. Hydrology and Remote Sensing Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, USA

    Martha Anderson, Jie Xue & William Kustas

  6. Sustainable Agricultural Water Systems Unit, Agricultural Research Service, U.S. Department of Agriculture, Davis, CA, USA

    Kyle Knipper

  7. Department of Winegrowing Research, E & J Gallo, Modesto, CA, USA

    Maria Mar Alsina

  8. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Erika Podest & Kyle McDonald

  9. WattTime, Oakland, CA, USA

    Aaron Davitt

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Davitt, A., Tesser, D., Gamarro, H. et al. The complementary uses of Sentinel-1A SAR and ECOSTRESS datasets to identify vineyard growth and conditions: a case study in Sonoma County, California. Irrig Sci 40, 655–681 (2022). https://doi.org/10.1007/s00271-022-00781-3

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