Estimating Grassland Parameters from Sentinel-2: A Model Comparison Study

PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science - Tập 88 Số 5 - Trang 379-390 - 2020
Marcel Schwieder, Marion Buddeberg1, Katja Kowalski2, Kira A. Pfoch2, Julia Bartsch2, Heike Bach1, Jürgen Pickert3, Patrick Hostert4
1VISTA Remote Sensing in Geosciences GmbH, Gabelsbergerstraße 51, 80333, Munich, Germany
2Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
3Leibniz Centre for Agricultural Landscape Research, Eberswalder Str. 84, 15374, Müncheberg, Germany
4Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany

Tóm tắt

AbstractGrassland plays an important role in German agriculture. The interplay of ecological processes in grasslands secures important ecosystem functions and, thus, ultimately contributes to essential ecosystem services. To sustain, e.g., the provision of fodder or the filter function of soils, agricultural management needs to adapt to site-specific grassland characteristics. Spatially explicit information derived from remote sensing data has been proven instrumental for achieving this. In this study, we analyze the potential of Sentinel-2 data for deriving grassland-relevant parameters. We compare two well-established methods to calculate the aboveground biomass and leaf area index (LAI), first using a random forest regression and second using the soil–leaf-canopy (SLC) radiative transfer model. Field data were recorded on a grassland area in Brandenburg in August 2019, and were used to train the empirical model and to validate both models. Results confirm that both methods are suitable for mapping the spatial distribution of LAI and for quantifying aboveground biomass. Uncertainties generally increased with higher biomass and LAI values in the empirical model and varied on average by a relative RMSE of 11% for modeling of dry biomass and a relative RMSE of 23% for LAI. Similar estimates were achieved using SLC with a relative RMSE of 30% for LAI retrieval, and a relative RMSE of 47% for the estimation of dry biomass. Resulting maps from both approaches showed comprehensible spatial patterns of LAI and dry biomass distributions. Despite variations in the value ranges of both maps, the average estimates and spatial patterns of LAI and dry biomass were very similar. Based on the results of the two compared modeling approaches and the comparison to the validation data, we conclude that the relationship between Sentinel-2 spectra and grassland-relevant variables can be quantified to map their spatial distributions from space. Future research needs to investigate how similar approaches perform across different grassland types, seasons and grassland management regimes.

Từ khóa


Tài liệu tham khảo

Ali I, Cawkwell F, Green S, Dwyer N (2014) Application of statistical and machine learning models for grassland yield estimation based on a hypertemporal satellite remote sensing time series. In: 2014 IEEE geoscience and remote sensing symposium, 13–18 July 2014. pp 5060–5063. https://doi.org/10.1109/IGARSS.2014.6947634

Ali I, Cawkwell F, Dwyer E, Barrett B, Green S (2016) Satellite remote sensing of grasslands: from observation to management. J Plant Ecol 9:649–671. https://doi.org/10.1093/jpe/rtw005

Asam S, Klein D, Dech S (2015) Estimation of grassland use intensities based on high spatial resolution LAI time series. Int Arch Photogramm Remote Sens Spat Inf Sci 40:285–291. https://doi.org/10.5194/isprsarchives-XL-7-W3-285-2015

Bach H (1995) Die Bestimmung hydrologischer und landwirtschaftlicher Oberflächenparameter aus hyperspektralen Fernerkundungsdaten. München

Bastiaanssen WGM, Molden DJ, Makin IW (2000) Remote sensing for irrigated agriculture: examples from research and possible applications. Agric Water Manag 46:137–155. https://doi.org/10.1016/S0378-3774(00)00080-9

Berlin-Brandenburg AfS (2017) Ernteberichterstattung über Feldfrüchte und Grünland im Land Brandenburg 2016. Potsdam, Germany

Blair J, Nippert J, Briggs J (2014) Grassland ecology. In: Monson RK (ed) Ecology and the environment. Springer, New York, pp 389–423. https://doi.org/10.1007/978-1-4614-7501-9_14

Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

Chen JM, Cihlar J (1996) Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens Environ 55:153–162

Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C, Corsi F, Cho M (2008) LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J Photogramm Remote Sens 63:409–426. https://doi.org/10.1016/j.isprsjprs.2008.01.001

Darvishzadeh R, Atzberger C, Skidmore AK, Abkar AA (2009) Leaf area index derivation from hyperspectral vegetation indices and the red edge position. Int J Remote Sens 30:6199–6218. https://doi.org/10.1080/01431160902842342

Darvishzadeh R, Atzberger C, Skidmore A, Schlerf M (2011) Mapping grassland leaf area index with airborne hyperspectral imagery: a comparison study of statistical approaches and inversion of radiative transfer models. ISPRS J Photogramm Remote Sens 66:894–906. https://doi.org/10.1016/j.isprsjprs.2011.09.013

Darvishzadeh R et al (2019) Analysis of Sentinel-2 and RapidEye for retrieval of leaf area index in a saltmarsh using a radiative transfer model. Remote Sens 11:671

Delegido J, Verrelst J, Alonso L, Moreno J (2011) Evaluation of Sentinel-2 red-edge bands for empirical estimation of green lai and chlorophyll content. Sensors 11:7063–7081

Destatis (2020) Statistisches bundesamt. Wiesbaden. https://www.destatis.de. Accessed 28 Jan 2020

Dierschke H, Briemle G (2008) Kulturgrasland: Wiesen Weiden und verwandte Staudenfluren. Ulmer, Stuttgart

Drusch M et al (2012) Sentinel-2: ESA's optical high-resolution mission for GMES operational services. Remote Sens Environ 120:25–36. https://doi.org/10.1016/j.rse.2011.11.026

European Space Agency (2017) Sentinel-2 spectral response functions. https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/document-library/-/asset_publisher/Wk0TKajiISaR/content/sentinel-2a-spectral-responses?redirect=https%3A%2F%2Fearth.esa.int%2Fweb%2Fsentinel%2Fuser-guides%2Fsentinel-2-msi%2Fdocument-library%3Fp_p_id%3D101_INSTANCE_Wk0TKajiISaR%26p_p_lifecycle%3D0%26p_p_state%3Dnormal%26p_p_mode%3Dview%26p_p_col_id%3Dcolumn-1%26p_p_col_count%3D1. Accessed 26 Jan 2020

Friedl M, Schimel D, Michaelsen J, Davis F, Walker H (1994) Estimating grassland biomass and leaf area index using ground and satellite data. Int J Remote Sens 15:1401–1420

Gao J (2006) Quantification of grassland properties: how it can benefit from geoinformatic technologies? Int J Remote Sens 27:1351–1365. https://doi.org/10.1080/01431160500474357

Hank T, Bach H, Spannraft K, Friese M, Frank T, Mauser W (2012) Improving the process-based simulation of growth heterogeneities in agricultural stands through assimilation of earth observation data. In: 2012 IEEE international geoscience and remote sensing symposium. IEEE, pp 1006–1009

Hank TB, Bach H, Mauser W (2015) Using a remote sensing-supported hydro-agroecological model for field-scale simulation of heterogeneous crop growth and yield: application for wheat in central Europe. Remote Sens 7:3934–3965

Havelland L (2018) Jahresbericht 2018. Amt für Landwirtschaft, Veterinär- und Lebensmittelüberwachung, DE

Herrmann A, Kelm M, Kornher A, Taube F (2005) Performance of grassland under different cutting regimes as affected by sward composition, nitrogen input, soil conditions and weather—a simulation study. Eur J Agron 22:141–158. https://doi.org/10.1016/j.eja.2004.02.002

Jacquemoud S, Baret F (1990) PROSPECT: a model of leaf optical properties spectra. Remote Sens Environ 34:75–91

Liaw A, Wiener M (2002) Classifiaction and regression by random forest. R News 2:18–22

Löpmeier F-J (1983) Agrarmeteorologisches Modell zur Berechnung der aktuellen Verdunstung (AMBAV). Dt. Wetterdienst, Zentrale Agrarmeteorologische Forschungsstelle Braunschweig

Muir JS, Schmidt M, Tindall D, Trevithick R (2011) Field measurement of fractional ground cover: a technical handbook supporting ground cover monitoring for Australia. Canberra, Australia

Nendel C (2014) MONICA: a simulation model for nitrogen and carbon dynamics in agro-ecosystems. In: Mueller L, Saparov A, Lischeid G (eds) Novel measurement and assessment tools for monitoring and management of land and water resources in agricultural landscapes of Central Asia. Springer International Publishing, Cham, pp 389–405. https://doi.org/10.1007/978-3-319-01017-5_23

Niggemann F, Appel F, Bach H, de la Mar J, Schirpke B (2014) The use of cloud computing resources for processing of big data from space for operational land surface monitoring in Germany. Paper presented at the Proceedings of the 2014 conferecnce on big data from space (BiDS'14), Frascati, Italy

Niggemann F et al (2015) Heterogeneous access and processing of EO-data on a cloud based infrastructure delivering operational products. Int Arch Photogramm Remote Sens Spat Inf Sci 40:663

Obermeier WA et al (2019) Grassland ecosystem services in a changing environment: the potential of hyperspectral monitoring. Remote Sens Environ 232:111273. https://doi.org/10.1016/j.rse.2019.111273

Punalekar SM, Verhoef A, Quaife TL, Humphries D, Bermingham L, Reynolds CK (2018) Application of Sentinel-2A data for pasture biomass monitoring using a physically based radiative transfer model. Remote Sens Environ 218:207–220. https://doi.org/10.1016/j.rse.2018.09.028

Quan X, He B, Yebra M, Yin C, Liao Z, Zhang X, Li X (2017) A radiative transfer model-based method for the estimation of grassland aboveground biomass. Int J Appl Earth Obs Geoinf 54:159–168. https://doi.org/10.1016/j.jag.2016.10.002

R Core Team (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Rayburn E, Lozier J (2003) A falling plate meter for estimating pasture forage mass. West Virginia University Extension Service

Sala OE, Paruelo JM (1997) Ecosystem services in grasslands. In: Daily GC (ed) Nature’s services: societal dependence on natural ecosystems. Island Press, Washington, DC, pp 237–251

Schellberg J, Verbruggen E (2014) Frontiers and perspectives on research strategies in grassland technology. Crop Pasture Sci 65:508–523. https://doi.org/10.1071/CP13429

Schellberg J, Hill MJ, Gerhards R, Rothmund M, Braun M (2008) Precision agriculture on grassland: applications, perspectives and constraints. Eur J Agron 29:59–71. https://doi.org/10.1016/j.eja.2008.05.005

Schindler U, Steidl J, Müller L, Eulenstein F, Thiere J (2007) Drought risk to agricultural land in Northeast and Central Germany. J Plant Nutr Soil Sci 170:357–362. https://doi.org/10.1002/jpln.200622045

Smit HJ, Metzger MJ, Ewert F (2008) Spatial distribution of grassland productivity and land use in Europe. Agric Syst 98:208–219. https://doi.org/10.1016/j.agsy.2008.07.004

Verhoef W (1985) Earth observation modeling based on layer scattering matrices. Remote Sens Environ 17:165–178

Verhoef W (1998) Theory of radiative transfer models applied in optical remote sensing of vegetation canopies. Wageningen Agrictultural University

Verhoef W, Bach H (2003) Simulation of hyperspectral and directional radiance images using coupled biophysical and atmospheric radiative transfer models. Remote Sens Environ 87:23–41

Verhoef W, Bach H (2007) Coupled soil–leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data. Remote Sens Environ 109:166–182

Wachendorf M, Fricke T, Möckel T (2018) Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands. Grass Forage Sci 73:1–14. https://doi.org/10.1111/gfs.12312

Wang J, Xiao X, Bajgain R, Starks P, Steiner J, Doughty RB, Chang Q (2019) Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS J Photogramm Remote Sens 154:189–201. https://doi.org/10.1016/j.isprsjprs.2019.06.007

ZALF (2020) Wetterstation Paulinenaue. Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF) e. V., unpublished

Zhao Y, Liu Z, Wu J (2020) Grassland ecosystem services: a systematic review of research advances and future directions. Landscape Ecology 35:793–814. https://doi.org/10.1007/s10980-020-00980-3

Thông tin
Thông tin xuất bản

PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

Tập 88 Số 5

379-390

Thông tin tác giả