Impacts of Interannual Radiometric Calibration Differences on Vegetation Indices and Solar-Induced Chlorophyll Fluorescence Retrieval from Ground-Based Spectral Observations.
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| Title: | Impacts of Interannual Radiometric Calibration Differences on Vegetation Indices and Solar-Induced Chlorophyll Fluorescence Retrieval from Ground-Based Spectral Observations. |
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| Authors: | Wang, Zhengjun1 (AUTHOR), Zhang, Fan2 (AUTHOR), Wang, Rui1,3 (AUTHOR), Wang, Tie1,4 (AUTHOR), Shi, Wentian1,5 (AUTHOR), Wang, Bo1,3 (AUTHOR), Zhang, Qian1,2,4,5 (AUTHOR) zhangqianzh@njtech.edu.cn |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 15, p2528. 26p. |
| Subjects: | Spectral sensitivity, Normalized difference vegetation index, Chlorophyll spectra, Spectral reflectance, Reflectance measurement |
| Abstract: | Highlights: What are the main findings? Interannual radiometric calibration coefficients showed clear wavelength-dependent differences, with the largest discrepancies occurring in the blue (450–500 nm) and red-edge (700–800 nm) regions, and these differences propagated into both VI and SIF retrievals from field spectra. PRI exhibited the largest relative sensitivity to calibration changes, whereas SIF also showed high sensitivity and strong channel dependence because of its weak signal and reliance on wavelength-specific information. NDVI and ARVI were the most robust products overall, and broadband convolution changed error propagation in an index-dependent manner: it generally reduced the sensitivity of EVI, SAVI, and PRI, but slightly increased that of NDVI. What are the implications of the main findings? Calibration uncertainty is not uniform across spectral products, indicating that long-term field spectroscopy should adopt channel-specific and year-specific calibration strategies rather than assuming stable coefficients over time. High-sensitivity products such as PRI and SIF require stricter radiometric control and explicit uncertainty assessment, whereas more robust indices such as NDVI and ARVI are better suited for long-term monitoring and satellite validation under imperfect calibration stability. Vegetation indices (VIs) are widely used as efficient proxies for canopy structure and pigment dynamics, whereas solar-induced chlorophyll fluorescence (SIF) provides a direct indicator of photosynthetic activity. Accurate monitoring of these variables is essential for ecosystem assessment, agricultural management, and satellite product validation. However, long-term field spectroscopy is often influenced by instrument aging and environmental variability, altering radiometric calibration coefficients and introducing uncertainty. In this study, we systematically evaluated the impact of interannual calibration coefficients derived in 2023 and 2024 on six commonly used VIs and SIF. These variables were retrieved from ground-based hyperspectral observations acquired continuously using FLAME-T and QEPRO+ spectrometers over a full rice-growing season. Furthermore, MODIS-like broadband indices were simulated through spectral convolution with sensor response functions. Results revealed clear interannual differences in calibration coefficients, particularly in the blue (450–500 nm) and red-edge (700–800 nm) regions. PRI exhibited the highest sensitivity to calibration changes. SIF also showed pronounced sensitivity due to its intrinsically weak signal and strong channel dependence. Conversely, NDVI and ARVI were highly robust, with the calibration-induced bias of NDVI remaining near zero, benefiting from its normalized formulation and strong near-infrared reflectance. Broadband simulation modified the propagation of uncertainty in an index-dependent manner. Compared with narrowband counterparts, broadband EVI, SAVI, and PRI showed reduced sensitivity, whereas NDVI exhibited a slight sensitivity increase. These findings demonstrate a hierarchy of calibration sensitivity governed by signal strength, spectral band selection, and algorithm design. They emphasize the necessity of frequency-dependent calibration strategies for reliable products in satellite validation studies. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Interannual radiometric calibration coefficients showed clear wavelength-dependent differences, with the largest discrepancies occurring in the blue (450–500 nm) and red-edge (700–800 nm) regions, and these differences propagated into both VI and SIF retrievals from field spectra. PRI exhibited the largest relative sensitivity to calibration changes, whereas SIF also showed high sensitivity and strong channel dependence because of its weak signal and reliance on wavelength-specific information. NDVI and ARVI were the most robust products overall, and broadband convolution changed error propagation in an index-dependent manner: it generally reduced the sensitivity of EVI, SAVI, and PRI, but slightly increased that of NDVI. What are the implications of the main findings? Calibration uncertainty is not uniform across spectral products, indicating that long-term field spectroscopy should adopt channel-specific and year-specific calibration strategies rather than assuming stable coefficients over time. High-sensitivity products such as PRI and SIF require stricter radiometric control and explicit uncertainty assessment, whereas more robust indices such as NDVI and ARVI are better suited for long-term monitoring and satellite validation under imperfect calibration stability. Vegetation indices (VIs) are widely used as efficient proxies for canopy structure and pigment dynamics, whereas solar-induced chlorophyll fluorescence (SIF) provides a direct indicator of photosynthetic activity. Accurate monitoring of these variables is essential for ecosystem assessment, agricultural management, and satellite product validation. However, long-term field spectroscopy is often influenced by instrument aging and environmental variability, altering radiometric calibration coefficients and introducing uncertainty. In this study, we systematically evaluated the impact of interannual calibration coefficients derived in 2023 and 2024 on six commonly used VIs and SIF. These variables were retrieved from ground-based hyperspectral observations acquired continuously using FLAME-T and QEPRO+ spectrometers over a full rice-growing season. Furthermore, MODIS-like broadband indices were simulated through spectral convolution with sensor response functions. Results revealed clear interannual differences in calibration coefficients, particularly in the blue (450–500 nm) and red-edge (700–800 nm) regions. PRI exhibited the highest sensitivity to calibration changes. SIF also showed pronounced sensitivity due to its intrinsically weak signal and strong channel dependence. Conversely, NDVI and ARVI were highly robust, with the calibration-induced bias of NDVI remaining near zero, benefiting from its normalized formulation and strong near-infrared reflectance. Broadband simulation modified the propagation of uncertainty in an index-dependent manner. Compared with narrowband counterparts, broadband EVI, SAVI, and PRI showed reduced sensitivity, whereas NDVI exhibited a slight sensitivity increase. These findings demonstrate a hierarchy of calibration sensitivity governed by signal strength, spectral band selection, and algorithm design. They emphasize the necessity of frequency-dependent calibration strategies for reliable products in satellite validation studies. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18152528 |
