Bingyu Li

Red solar-induced chlorophyll fluorescence (SIF_B) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIF_B 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 SIF_B using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PK_B) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PK_B and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPAR_chl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIF_B by the vegetation canopy. Consequently, we constructed an expression of PK_B to modify SIF_B. To evaluate the modified SIF_B (MSIF_B) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIF_B, MSIF_B, SIF_B-VIs (a fusion of the vegetation index and SIF_B), and MSIF_B-VIs (a fusion of the vegetation index and MSIF_B) with the severity level (SL), respectively. The results show that the correlation between MSIF_B and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIF_B. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIF_B, MSIF_B, SIF_B-VIs, and MSIF_B-VIs as independent variables. PK_B significantly improves the accuracy and robustness of models based on SIF_B and its fusion index SIF_B-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 MSIF_B-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIF_B-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIF_B modified by PK_B can weaken the effects of chlorophyll reabsorption and canopy architecture on SIF_B and improve the ability of SIF_B to detect stress information.

(1) Background: Vegetation is an important component of ecosystems. Investigating the spatio-temporal dynamic changes in vegetation in various Shaanxi Province regions is crucial for the preservation of the local ecological environment and sustainable development. (2) Methods: In this study, the KNDVI vegetation index over the 20-year period from 2003 to 2022 was calculated using MODIS satellite image data that was received from Google Earth Engine (GEE). Sen and MK trend analysis as well as partial correlation analysis were then utilized to examine the patterns in vegetation change in various Shaanxi Province regions. This paper selected meteorological factors, such as potential evapotranspiration (PET), precipitation (PRE), and temperature (TMP); human activity factors, such as land-use type and population density; and terrain factors, such as surface elevation, slope direction, and slope gradient, as the influencing factors for vegetation changes in the research area in order to analyze the driving forces of vegetation spatio-temporal changes. These factors were analyzed using a geo-detector. (3) Results: The vegetation in the research area presented a growth trend from 2003 to 2022, and the area of vegetation improvement was 189,756 km², accounting for 92.15% of the total area. Among them, the area of significantly improved regions was 174,262 km², accounting for 84.63% of the total area, and the area of slightly improved regions was 15,495 square kilometers, accounting for 7.52% of the total area. (4) Conclusions: The strengthening of bivariate factors and nonlinear enhancement were the main interaction types affecting vegetation changes. The combination of interaction factors affecting vegetation change in Shaanxi Province includes PRE ∩ PET as well as TMP ∩ PET. Therefore, climate conditions were the main driving force of KNDVI vegetation changes in Shaanxi Province. The data supported by this research are crucial for maintaining the region’s natural ecosystem.

Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.

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, SIF_yield (normalized by absorbed photosynthetically active radiation), fluorescence yield (Φ_F) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (Φ_F-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), Φ_F-r (R = 0.53) and SIF (R = 0.51), and the response of Φ_F (R = 0.45) and SIF_yield (R = 0.34) to SL was weak. Under lightly diseased conditions, Φ_F-r (R = 0.62) showed the strongest response to disease, followed by SIF_yield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between Φ_F 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, Φ_F-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for Φ_F) 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 Φ_F-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.