Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.
Solar-induced chlorophyll fluorescence (SIF) shows potential in exploring plant responses to environmental changes caused by extreme climatic factors. However, how to accurately assess climate stresses (especially the low-temperature stress) suffered on crops at the regional scale in a systematic approach has not been extensively explored. In this study, we developed a climate vegetation stress index (CVSI) to assess and quantify the impacts of climate stress on crops at large scales by combining TROPOspheric Monitoring Instrument (TROPOMI) SIF and land surface temperature (LST) data through an easy-to-operate approach. This index was employed to identify low-temperature stress conditions in Henan Province's winter wheat in 2018. Results indicate that, influenced by climate characteristics, crops in the northern part of Henan Province experienced more severe low-temperature stress than those in the southern part. The daily average SIF values experienced reductions of 0.74, 0.45, 0.61, and 0.86 mW $\cdot ~\text{m}^{-2}~\cdot $ sr $^{-1}~\cdot $ nm−1 during the four cooling episodes within the two phenological periods, respectively. As low-temperature stress intensified, winter wheat growth was hindered, reducing grain yield. Indeed, the CVSI provides an accurate depiction of crop stress levels and patterns. In areas with high-CVSI values, yield losses are particularly severe. In addition, the significant positive correlation between the CVSI and net primary productivity (NPP), along with the similar spatial intensity pattern, shows the effectiveness of CVSI in monitoring low-temperature stress. CVSI provides a new approach to understand the impacts of climate change on overwintering crops and offers a practical reference for climate stress effects monitoring at the regional scale.
Abstract. Satellite-derived solar-induced chlorophyll fluorescence (SIF) offers valuable opportunities for monitoring large-scale ecosystem functions. However, the inherent trade-off between satellite scan range and spatial resolution, along with incomplete spatial coverage and irregular temporal sampling, limits its broader application. In this study, we developed a 500-m spatial resolution monthly SIF dataset for the China region (CNSIF) from 2003 to 2022, using a data-driven deep learning approach based on high-resolution apparent reflectance and thermal infrared data. The results indicate that CNSIF effectively captures the spatial patterns of vegetation photosynthetic activity and exhibits a positive annual growth trend of 0.054. Comparisons with tower-based observations validated the ability of CNSIF to track changes in photosynthetic intensity over time across different ecosystems. Furthermore, the strong correlation (R2_2016 = 0.768, R2_2020 = 0.743; P<0.001) between CNSIF and the MODIS monthly Gross Primary Production (GPP) product demonstrates its potential for estimating carbon flux. CNSIF's higher-resolution estimation of photosynthetic activity offers a promising tool for monitoring vegetation dynamics across China and estimating fragmented agricultural production. It enables the incorporation of ecosystem fragmentation effects into earth observation and carbon cycle systems. The CNSIF dataset is available at https://doi.org/10.6084/m9.figshare.27075145 (Du et al., 2024).
Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIFcanopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIRVP, which can characterize the canopy structure, and SIFtot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIFcanopy, NIRVP, and SIFtot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIRVP was more sensitive to variation in SIFcanopy than SIFtot, and the correlation between SIFtot and SL was 6.6% higher than that of SIFcanopy. Using the decomposed SIFtot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% < SL ≤ 45%), the variation in SIFcanopy was affected by both NIRVP and SIFtot, and the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components. When the severity level (SL) of disease reached a severe level (SL > 45%), SIFtot was more sensitive to the variation in SIFcanopy, and NIRVP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIFcanopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress.
Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information.
The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust. The canopy spectrum of winter wheat infected by stripe rust was processed with the difference-in-differences (DID) algorithm used in econometrics. The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors, disease severity, and several simulated satellite sensors. The correlation between the normalized difference vegetation index processed by the DID algorithm (NDVI-DID) and the disease severity level (SL) increased in comparison with the NDVI before processing. The increase in precision in the natural disease area in the field in the presence of large differences in growth stage, growth, planting, and management of the crop was greater than that in the controlled experiment. For low disease levels (SL < 20%), the R2 of the regression of NDVI-DID on SL was 38.8% higher than that of the NDVI and the root mean square error (RMSE) was reduced by 11.1%. The increase in precision was greater than that for the severe level (SL > 40%). According to the measured hyperspectral data, the spectral reflectance of three satellite sensor levels was simulated. The wide-band NDVI was calculated. Compared with the wide-band NDVI and vegetation indexes (VIS) before DID processing, there were increases in the correlation between SL and the various types of VIS-DID, as well as in the correlation between SL and NDVI-DID. It is feasible to apply the DID algorithm to multispectral satellite data and diverse types of VIS for monitoring wheat stripe rust. Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum, increase the precision of remote sensing monitoring of wheat stripe rust, and provide a reference for remote sensing monitoring of other crop diseases.
The red band of solar-induced chlorophyll fluorescence (SIF) is closely related to the photosynthetically active radiation absorbed by chlorophyll, which is of great significance in stress monitoring. The scattering and reabsorption of red band SIF by the vegetation canopy caused significant changes in the spectral intensity and shape of SIF, which affected the relationship between SIF and crop stress. We proposed a method to modify the red band SIF by using SIF spectral shape characteristic parameters to reduce the influence of chlorophyll reabsorption and canopy geometry on the red band SIF intensity. We used the full-spectrum SIF spectral curve to calculate the red band pseudokurtosis (PKB) parameters that can characterize the spectral shape features. Based on the SCOPE model, the photosynthetic physiological mechanism of PKB was analyzed, and the expression of PKB to modify red-band SIF was constructed. To evaluate the performance of modified red band SIF in monitoring the severity of wheat stripe rust, the correlation between red band SIF and the disease severity level (SL) before and after PKB modification and its model accuracy were compared and analyzed by using measured data. The results showed that the introduction of PKB increased the Pearson correlation coefficient between the red band SIF and the SL in the plot control experiment and the natural disease field experiment (53.44% and 5.17%), and the root mean squared error between the predicted SL and the measured SL decreased from 0.166 to 0.148. We further verified the effectiveness of the shape feature parameter PKB on red band SIF revision by using the red band SIF after VI fusion. The results showed that the red band SIF modified by PKB could weaken the effect of chlorophyll reabsorption and canopy geometry on red band SIF and improve the ability of red band SIF to detect stress information.
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