{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:10:55Z","timestamp":1760112655705,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["SKLNBC2023-01"],"award-info":[{"award-number":["SKLNBC2023-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (\u0394PRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (SIFTOC_760) was downscaled to the broadband photosystem-level SIF for photosystem II (SIFTOT_FULL_PSII). Our results show that GPP can be accurately estimated using a multi-linear model with SIFTOT_FULL_PSII and \u0394PRI. The ratio of GPP measured using the eddy covariance (EC) method (GPPEC) to GPP estimated using SIFTOT_FULL_PSII and \u0394PRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using SIFTOT_FULL_PSII and \u0394PRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 \u03bcmol\u2022m\u22122\u2022s\u22121; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 \u03bcmol\u2022m\u22122\u2022s\u22121). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using SIFTOT_FULL_PSII and \u0394PRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions.<\/jats:p>","DOI":"10.3390\/rs16162874","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T15:24:16Z","timestamp":1722957856000},"page":"2874","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Soybean Gross Primary Productivity Modeling Using Solar-Induced Chlorophyll Fluorescence and the Photochemical Reflectance Index by Accounting for the Clearness Index"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5536-1896","authenticated-orcid":false,"given":"Jidai","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China"}]},{"given":"Jiasong","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of NBC Protection for Civilian, Beijing 102205, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113365","DOI":"10.1016\/j.rse.2022.113365","article-title":"Tracking Diurnal to Seasonal Variations of Gross Primary Productivity Using a Geostationary Satellite, GK-2A Advanced Meteorological Imager","volume":"284","author":"Jeong","year":"2023","journal-title":"Remote Sens. 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