Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations

0546326 - ÚVGZ 2022 RIV CH eng J - Článek v odborném periodiku
Tomíček, J. - Misurec, J. - Lukeš, Petr
Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations.
Remote Sensing. Roč. 13, č. 18 (2021), č. článku 3659. E-ISSN 2072-4292
Grant CEP: GA TA ČR TH02030248; GA MŠMT(CZ) EF16_019/0000797
Výzkumná infrastruktura: CzeCOS III - 90123
Institucionální podpora: RVO:86652079
Klíčová slova: leaf-area index * chlorophyll content * water-content * canopy * wheat * instrument * corn * Sentinel-2 * prosail * radiative transfer * leaf area index * leaf chlorophyll content * leaf water content * artificial neural network * look-up table
Obor OECD: Remote sensing
Impakt faktor: 5.349, rok: 2021
Způsob publikování: Open access
https://www.mdpi.com/2072-4292/13/18/3659

In this study, Sentinel-2 data were used for the retrieval of three key biophysical parameters of crops: leaf area index (LAI), leaf chlorophyll content (LCC), and leaf water content (LWC) for dominant crop types in the Czech Republic, including winter wheat (Triticum aestivum), spring barley (Hordeum vulgare), winter rapeseed (Brassica napus subsp. napus), alfalfa (Medicago sativa), sugar beet (Beta vulgaris), and corn (Zea mays subsp. Mays) in different stages of crop development. Artificial neural networks were applied in combination with an approach using look-up tables that is based on PROSAIL simulations to retrieve the biophysical properties tailored for each crop type. Crop-specific PROSAIL model optimization and validation were based upon a large dataset of in situ measurements collected in 2017 and 2018 in lowland of Central Bohemia region. For LCC and LAI, respectively, low relative root mean square error (rRMSE, 25%, 37%) was achieved. Additionally, a relatively strong correlation with in situ measurements (r = 0.80) was obtained for LAI. On the contrary, the results of the LWC parameter retrieval proved to be unsatisfactory. We have developed a generic tool for biophysical monitoring of agricultural crops based on the interpretation of Sentinel-2 satellite data by inversion of the radiation transfer model. The resulting crop condition maps can serve as precision agriculture inputs for selective fertilizer and irrigation application as well as for yield potential assessment.
Trvalý link: http://hdl.handle.net/11104/0322860