{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:38:59Z","timestamp":1775839139691,"version":"3.50.1"},"reference-count":107,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T00:00:00Z","timestamp":1717718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AmericaView\/USGS","award":["AV23-OH-01"],"award-info":[{"award-number":["AV23-OH-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, the utilization of machine learning algorithms and advancements in unmanned aerial vehicle (UAV) technology have caused significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV\u2013satellite data fusion have emerged as two prominent approaches for the estimation of vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for the mapping of crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV\u2013satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV\u2013satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV\u2013RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root mean square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian process regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 \u00b5g\/cm2 and 9.65 \u00b5g\/cm2, respectively). Similar performance is observed for the UAV\u2013Landsat and UAV\u2013PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, the maximum performance is attained with the Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 \u00b5g\/cm2 and 42.91 \u00b5g\/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 \u00b5g\/cm2 for the PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, the RapidEye data yield the most stable performance, with the R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 \u00b5g\/cm2 to 33.07 \u00b5g\/cm2. The study highlights the importance of synergizing UAV and satellite data, which enables the effective monitoring of canopy chlorophyll in small agricultural lands.<\/jats:p>","DOI":"10.3390\/rs16122058","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T08:05:17Z","timestamp":1717747517000},"page":"2058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Retrieval of Crop Canopy Chlorophyll: Machine Learning vs. Radiative Transfer Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3619-6367","authenticated-orcid":false,"given":"Mir Md Tasnim","family":"Alam","sequence":"first","affiliation":[{"name":"School of Earth, Environment and Society, Bowling Green State University, Bowling Green, OH 43403, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1573-1925","authenticated-orcid":false,"given":"Anita","family":"Simic Milas","sequence":"additional","affiliation":[{"name":"School of Earth, Environment and Society, Bowling Green State University, Bowling Green, OH 43403, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2345-7882","authenticated-orcid":false,"given":"Mateo","family":"Ga\u0161parovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Geodesy, University of Zagreb, 10000 Zagreb, Croatia"}]},{"given":"Henry Poku","family":"Osei","sequence":"additional","affiliation":[{"name":"School of Earth, Environment and Society, Bowling Green State University, Bowling Green, OH 43403, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chakhvashvili, E., Siegmann, B., Muller, O., Verrelst, J., Bendig, J., Kraska, T., and Rascher, U. 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