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Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations
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SYSNO ASEP 0546326 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Prototyping a Generic Algorithm for Crop Parameter Retrieval across the Season Using Radiative Transfer Model Inversion and Sentinel-2 Satellite Observations Author(s) Tomíček, J. (CZ)
Misurec, J. (CZ)
Lukeš, Petr (UEK-B) ORCID, SAI, RIDNumber of authors 3 Article number 3659 Source Title Remote Sensing. - : MDPI
Roč. 13, č. 18 (2021)Number of pages 29 s. Language eng - English Country CH - Switzerland Keywords 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 Subject RIV EH - Ecology, Behaviour OECD category Remote sensing R&D Projects TH02030248 GA TA ČR - Technology Agency of the Czech Republic (TA ČR) EF16_019/0000797 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Research Infrastructure CzeCOS III - 90123 - Ústav výzkumu globální změny AV ČR, v. v. i. Method of publishing Open access Institutional support UEK-B - RVO:86652079 UT WOS 000702053900001 EID SCOPUS 85115106837 DOI 10.3390/rs13183659 Annotation 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. Workplace Global Change Research Institute Contact Nikola Šviková, svikova.n@czechglobe.cz, Tel.: 511 192 268 Year of Publishing 2022 Electronic address https://www.mdpi.com/2072-4292/13/18/3659
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