In situ measurement techniques in remote sensing research over grasslands
References
- Al-Kaisi, M, Brun, LJ & Enz, JW 1989, ‘Transpiration and evapotranspiration from maize as related to leaf area index’, Agricultural and Forest Meteorology, vol. 48, no. 1–2, pp. 111–116.
- Allen, CT & Ulaby, FT 1984, ‘Modelling the polarization dependence of the attenuation in vegetation canopies’ in IGARSS 84 Symposium, Strasbourg, France, pp.119–124.
- Asner, GP 1998, ‘Biophysical and biochemical sources of variability in canopy reflectance’, Remote Sensing of Environment, vol. 64, no. 3, pp. 234–253.
- Baghdadi, N, El Hajj, M, Zribi, M & Bousbih, S 2017, ‘Calibration of the Water Cloud Model at C-Band for winter crop fields and grasslands’, Remote Sensing, vol. 9, no. 9.
- Balsamo, G & Zeng, X 2019, ‘Correction: Balsamo, G., et al. Satellite and in situ observations for advancing global Earth surface modelling: A review’, Remote Sensing, vol. 10, no. 12.
- Bartold, M, Wróblewski, K, Kluczek, M, Dąbrowska-Zielińska, K & Goliński, P 2024, ‘Examining the sensitivity of satellite-derived vegetation indices to plant drought stress in grasslands in Poland’, Plants, vol. 13, no. 16.
- Bochenek, Z, Dabrowska - Zielinska, K, Gurdak, R, Grzybowski, P, Bartold, M & Niro, F 2017, ‘Validation of the LAI biophysical product derived from Sentinel-2 and Proba-V images for winter wheat in western Poland’, Geoinformation Issues, vol. 9, no. 1, pp. 15–26.
- Bonan, G 1993, ‘Importance of leaf area index and forest type when estimating photosynthesis in boreal forests’, Remote Sensing of Environment, vol. 43, no. 3, pp. 303–314.
- Breda, NJJ 2003, ‘Ground-based measurements of leaf area index: a review of methods, instruments and current controversies’, Journal of Experimental Botany, vol. 54, no. 392, pp. 2403–2417.
- Chen, L, Xing, M, He, B, Wang, J, Xu, M, Song, Y & Huang, X 2022, ‘Estimating soil moisture over winter wheat fields during growing season using RADARSAT-2 data’, Remote Sensing, vol. 14, no. 9.
- Dąbrowska-Zielińska, K, Budzyńska, M, Kowalik, W, Małek, I, Gatkowska, M, Bartold, M & Turlej, K 2012, ‘Biophysical parameters assessed from microwave and optical data’, International Journal of Electronics and Telecommunications, vol. 58, no. 2.
- Dabrowska-Zielinska, K, Budzynska, M, Tomaszewska, M, Bartold, M & Gatkowska, M 2015, ‘The study of multifrequency microwave satellite images for vegetation biomass and humidity of the area under Ramsar convention’ in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium, pp.5198–5200. IEEE, Milan, Italy. Available from: <
https://ieeexplore.ieee.org/document/7327005/ >. [9 April 2024]. - Dąbrowska-Zielińska, K, Wróblewski, K, Goliński, P, Malińska, A, Bartold, M, Łągiewska, M, Kluczek, M, Panek-Chwastyk, E, Dariusz Ziółkowski, Golińska, B, Markowska, A & Paradowski, K 2024, ‘Integrating Copernicus LMS with Ground Measurements Data for Leaf Area Index and biomass assessment for grasslands in Poland and Norway’, International Journal of Digital Earth, vol. 8, no. 4.
- 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 Journal of Photogrammetry and Remote Sensing, vol. 63, no. 4, pp. 409–426.
- Dong, T, Liu, Jiangui, Qian, B, He, L, Liu, Jane, Wang, R, Jing, Q, Champagne, C, McNairn, H, Powers, J, Shi, Y, Chen, JM & Shang, J 2020, ‘Estimating crop biomass using leaf area index derived from Landsat 8 and Sentinel-2 data’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 168, pp. 236–250.
- Doughty, CL, Ambrose, RF, Okin, GS & Cavanaugh, KC 2021, ‘Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery’, Remote Sensing in Ecology and Conservation, vol. 7, no. 3, pp. 411–429.
- Dusseux, P, Hubert-Moy, L, Corpetti, T & Vertès, F 2015, ‘Evaluation of SPOT imagery for the estimation of grassland biomass’, International Journal of Applied Earth Observation and Geoinformation, vol. 38, pp. 72–77.
- Fernando, C, Sánchez-Zapero, J, Swinnen, E, Bonte, K & Martinez-Sánchez, E 2021, Preliminary Validation Report - Copernicus Land Monitoring Service, European Environment Agency, Denkmark. Available from: <
https://land.copernicus.eu/en/technical-library/validation-report-of-vegetation-indices/@@download/file >. [9 April 2024]. - Gorham, E 1991, ‘Northern peatlands, role in the carbon cycle and probable responses to climatic warming’, Ecological Applications, vol. 1, pp. 182–195.
- Guerini Filho, M, Kuplich, TM & Quadros, FLFD 2020, ‘Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data’, International Journal of Remote Sensing, vol. 41, no. 8, pp. 2861–2876.
- Gurdak, R & Bartold, M 2021, ‘Remote sensing techniques to assess chlorophyll fluorescence in support of crop monitoring in Poland’, Miscellanea Geographica, vol. 25, no. 4, pp. 226–237.
- Kiala, Z, Odindi, J, Mutanga, O & Peerbhay, K 2016, ‘Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data’, Journal of Applied Remote Sensing, vol. 10, no. 3.
- LAI-2200C- Plant Canopy Analyzer Instruction Manual, LI-COR Environmental, Nebraska, USA 2023.
- Li, J & Wang, S 2018, ‘Using SAR-derived vegetation descriptors in a water cloud model to improve soil moisture retrieval’, Remote Sensing, vol. 10, no. 9.
- Li, Z-L, Leng, P, Zhou, C, Chen, K-S, Zhou, F-C & Shang, G-F 2021, ‘Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future’, Earth-Science Reviews, vol. 218.
- Mundava, C, Schut, AGT, Helmholz, P, Stovold, R, Donald, G & Lamb, DW 2015, ‘A novel protocol for assessment of aboveground biomass in rangeland environments’, The Rangeland Journal, vol. 37, no. 2, p. 4.
- Naicker, R, Mutanga, O, Peerbhay, K & Odebiri, O 2024, ‘Estimating high-density aboveground biomass within a complex tropical grassland using Worldview-3 imagery’, Environmental Monitoring and Assessment, vol. 196, no. 4.
- Panek-Chwastyk, E, Ozbilge, CN, Dąbrowska-Zielińska, K & Wróblewski, K 2024, ‘Assessment of grassland biomass prediction using AquaCrop Model: Integrating Sentinel-2 Data and Ground Measurements in Wielkopolska and Podlasie Regions, Poland’, Agriculture, vol. 14, no. 6, p. 837.
- Pearse, GD, Watt, MS & Morgenroth, J 2016, ‘Comparison of optical LAI measurements under diffuse and clear skies after correcting for scattered radiation’, Agricultural and Forest Meteorology, vol. 221, pp. 61–70.
- 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 Sensing of Environment, vol. 218, pp. 207–220.
- Rouse, JW, Haas, RH, Schell, JA & Deering, DW 1974, ‘Monitoring vegetation systems in the Great Plains with ERTS’, Third Earth Resources Technology Satellite–1 Symposium, vol. volume 1, pp. 309–317.
- Sekertekin, A, Marangoz, AM & Abdikan, S 2020, ‘ALOS-2 and Sentinel-1 SAR data sensitivity analysis to surface soil moisture over bare and vegetated agricultural fields’, Computers and Electronics in Agriculture, vol. 171.
- Tucker, CJ 1979, ‘Red and photographic infrared linear combinations for monitoring vegetation’, Remote Sensing of Environment, vol. 8, no. 2, pp. 127–150.
- Upreti, D, Huang, W, Kong, W, Pascucci, S, Pignatti, S, Zhou, X, Ye, H & Casa, R 2019, ‘A comparison of hybrid machine learning algorithms for the retrieval of wheat biophysical variables from Sentinel-2’, Remote Sensing, vol. 11, no. 5.
- Wang, Z, Zhao, T, Qiu, J, Zhao, X, Li, R & Wang, S 2021, ‘Microwave-based vegetation descriptors in the parameterization of water cloud model at L-band for soil moisture retrieval over croplands’, GIScience & Remote Sensing, vol. 58, no. 1, pp. 48–67.
- Xing, M, Chen, L, Wang, J, Shang, J & Huang, X 2022, ‘Soil moisture retrieval using SAR Backscattering Ratio Method during the crop growing season’, Remote Sensing, vol. 14, no. 13.
- Xue, J & Su, B 2017, ‘Significant remote sensing vegetation indices: A review of developments and applications’, Journal of Sensors, vol. 2017, pp. 1–17.
Language: English
Page range: 282 - 290
Submitted on: Jul 22, 2024
Accepted on: Mar 10, 2025
Published on: May 17, 2025
Published by: Faculty of Geography and Regional Studies, University of Warsaw
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
© 2025 Magdalena Łągiewska, Radosław Gurdak, Dariusz Ziółkowski, Konrad Wróblewski, published by Faculty of Geography and Regional Studies, University of Warsaw
This work is licensed under the Creative Commons Attribution 4.0 License.