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001045821 1001_ $$0P:(DE-HGF)0$$aYang, Peiqi$$b0$$eCorresponding author
001045821 245__ $$aMitigating the black-soil problem in the reflectance-to-fluorescence (R2F) relationship: A soil-adjusted reflectance-based approach for downscaling SIF
001045821 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2025
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001045821 520__ $$aSolar-induced chlorophyll fluorescence (SIF) is an effective probe for photosynthesis, but this remote sensingsignal is affected by multiple factors, including radiation intensity, canopy structure, sun-observer geometry, andleaf physiological status. The complex interplay among these factors causes substantial discrepancies among topof-canopy (TOC) SIF, leaf-level average SIF and actual photosynthetic activity. Downscaling TOC SIF to the leafleveland decoupling structural and physiological information remain major challenges in the use of SIF signalsfor remote sensing of photosynthesis. To address these challenges, the R2F (reflectance-to-fluorescence) theorywas developed, grounded in the similarity in radiative transfer processes governing SIF and reflectance. Thistheory establishes a physical relationship between near-infrared reflectance (Rnir) and the far-red SIF scatteringcoefficient (σF). On this basis, SIF signals can be scaled from the canopy to the leaf level by normalizing σF,estimated from reflectance as σF = Rnir/i0, where i0 denotes canopy interceptance. However, the original R2Fformulation assumes a non-reflective soil. This simplification breaks down in sparse canopies, where soil contributionsare non-negligible—an issue referred to as the “black-soil problem”. Soil enhances both Rnir and σF,distorting their intrinsic relationship. In this study, we show that soil effects manifest through two mainmechanisms: (1) direct soil reflection, which significantly increases Rnir but has minimal impact on σF, and (2)soil–vegetation multiple scattering, which affects both Rnir and σF but tends to have compensatory effects.Consequently, the dominant source of bias in the original R2F relationship is direct soil reflection that contributesto Rnir—a mechanism that had not been explicitly isolated in previous studies. This finding allows us tonarrow down the “black-soil problem” in the R2F framework to the specific impact of soil single scattering onRnir. To mitigate this bias, we propose a soil-adjusted R2F (saR2F) method, which estimates the direct soilcontribution of Rnir using TOC red and blue reflectance. Correcting Rnir for the direct soil reflection results in arobust relationship between σF and soil-adjusted Rnir (saRnir), notably σF = saRnir/i0.We evaluated the saR2F relationship using one field and two simulated datasets. In the field study, saR2Fimproved the estimation of σF from TOC reflectance, with R2 increasing ranging from 0.21 to 0.31 compared to the original R2F. In the two simulations, saR2F consistently outperformed the original R2F, especially under sparse canopy conditions. We also compared saR2F with NDVI-based (NIRv) and FCVI-based R2F approaches. In the available field observations collected under specific conditions (i.e., varying viewing azimuth angles), the three approaches showed similar performance and were better than the original R2F in explaining the viewing- angle dependence of σF. However, across the broader range of simulated scenarios and for estimating the exact σF, saR2F demonstrated better stability than NIRv and FCVI-based R2F methods. The NIRv-based and FCVI-based R2F methods yielded relatively low RMSE (0.092 and 0.075, respectively) but weak explanatory power, with R2 values below 0.41 for canopies with LAI <3. In contrast, saR2F achieved a much stronger relationship (R2 =0.80) and a low RMSE of 0.044. Furthermore, compared to the NIRv or FCVI-based approaches for R2F corrections, saR2F offers a more physically plausible and interpretable solution that can be applied to angular correction and total SIF estimation. The effective mitigation of the black-soil problem facilitates interpretation of raw SIF observations and enhances the monitoring of photosynthetic activity using SIF.
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001045821 7001_ $$aLiu, Zhigang$$b1
001045821 7001_ $$aHan, Dalei$$b2
001045821 7001_ $$aZhang, Runfei$$b3
001045821 7001_ $$0P:(DE-Juel1)172711$$aSiegmann, Bastian$$b4$$ufzj
001045821 7001_ $$aLiu, Jing$$b5
001045821 7001_ $$aZhao, Huarong$$b6
001045821 7001_ $$0P:(DE-Juel1)129388$$aRascher, Uwe$$b7$$ufzj
001045821 7001_ $$aChen, Jing M.$$b8
001045821 7001_ $$avan der Tol, Christiaan$$b9
001045821 773__ $$0PERI:(DE-600)1498713-2$$a10.1016/j.rse.2025.114998$$gVol. 330, p. 114998 -$$p114998 -$$tRemote sensing of environment$$v330$$x0034-4257$$y2025
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