Leaf chlorophyll content (LCC) is an indicator of leaf photosynthetic capacity. It is crucial for improving the understanding of plant physiological status. SPAD meters are routinely used to provide an instantaneous estimation of in situ LCC. However, the calibration of meter readings into absolute measures of LCC is difficult, and a generic approach for this conversion remains elusive. This study presents an evaluation of the approaches that are commonly used in converting SPAD readings into absolute LCC values. We compared these approaches using three field datasets and one synthetic dataset. The field datasets consist of LCC measured using a destructive method in the laboratory, as well as the SPAD readings measured in the field for various vegetation types. The synthetic dataset was generated with the leaf radiative transfer model PROSPECT-5 across different leaf structures. LCC covers a wide range from 1.40 μg cm⁻² to 86.34 μg cm⁻² in the field datasets, and it ranges from 5 μg cm⁻² to 80 μg cm⁻² in the synthetic dataset. The relationships between LCC and SPAD readings were examined using linear, polynomial, exponential, and homographic functions for the field and synthetic datasets. For the field datasets, the assessments of these approaches were conducted for (i) all three datasets together, (ii) individual datasets, and (iii) individual vegetation species. For the synthetic dataset, leaves with different leaf structures (which mimic different vegetation species) were grouped for the evaluation of the approaches. The results demonstrate that the linear function is the most accurate one for the simulated dataset, in which leaf structure is relatively simple due to the turbid medium assumption of the PROSPECT-5 model. The assumption of leaves in the PROSPECT-5 model complies with the assumption made in the designed algorithm of the SPAD meter. As a result, the linear relationship between LCC and SPAD values was found for the modeled dataset in which the leaf structure is simple. For the field dataset, the functions do not perform well for all datasets together, while they improve significantly for individual datasets or species. The overall performance of the linear ($\mathrm{LCC}=a\ast \mathrm{SPAD}+b$), polynomial ($\mathrm{LCC}=a\ast {\mathrm{SPAD}}^2+b\ast \mathrm{SPAD}+c$), and exponential functions ($\mathrm{LCC}=0.0893\ast \left({10}^{{\mathrm{SPAD}}^{\alpha }}\right))$ is promising for various datasets and species with the R² > 0.8 and RMSE <10 μg cm⁻². However, the accuracy of the homographic functions ($\mathrm{LCC}=a\ast \mathrm{SPAD}/\left(b-\mathrm{SPAD}\right)$) changes significantly among different datasets and species with R² from 0.02 of wheat to 0.92 of linseed (RMSE from 642.50 μg cm⁻² to 5.74 μg cm⁻²). Other than species- and dataset-dependence, the homographic functions are more likely to produce a numerical singularity due to the characteristics of the function per se. Compared with the linear and exponential functions, the polynomial functions have a higher degree of freedom due to one extra fitting parameter. For a smaller size of data, the linear and exponential functions are more suitable than the polynomial functions due to the less fitting parameters. This study compares different approaches and addresses the uncertainty in the conversion from SPAD readings into absolute LCC, which facilitates more accurate measurements of absolute LCC in the field.

The intensification of anthropogenic activities has led to the infiltration of enormous quantities of pollutants into rivers and lakes, resulting in significant deterioration in water quality and a more prominent occurrence of eutrophication. Poyang Lake, the largest freshwater lake in China, is facing a severe challenge related to eutrophication, which seriously threatens the delivery of the ecosystem service and the safety of drinking water. To address this challenge, Landsat-8 Operational Land Imager (OLI) data for the Poyang Lake Basin (PLB) from May 2013 to December 2020 were used. Since inland water bodies with complex optical characteristics, we developed a semi-analytical algorithm to assess the trophic state of the water based on two cruise field measurements in 2016 and 2019. Combining the semi-analytical trophic level index (TLI) with an atmospheric correction model is the most suitable model for OLI images of the PLB, this model was then applied to Landsat-8 time series observations. The trends of the trophic state of water bodies in PLB were revealed, and the annual, quarterly and monthly percentages of eutrophic water bodies were calculated. Natural and anthropogenic factors were then used to explain the changes in the trophic state of the PLB waters. The main findings are as follows: (1) From the 8-year observation results, it can be seen that the variation of trophic level of water in PLB showed obviously spatial and temporal variations, characterized by higher in the north than in the south and higher in winter than in summer. (2) Temperature promoted the growth of harmful algae and plays an essential role in affecting changes in the trophic level of the water. (3) Changes in the trophic level of water bodies in PLB were mainly affected by human activities. The results of spatial and temporal variation of the trophic level of water and the driving factors in PLB can extend our knowledge of water quality degradation and provide essential references for relevant policy-making institutions.

Non-photochemical quenching (NPQ) is an indicator of crop stress. Until now, only a limited number of studies have focused on how to estimate NPQ using remote sensing technology. The main challenge is the complicated regulatory mechanism of NPQ. NPQ can be divided into energy-dependent (qE) and non-energy-dependent (non-qE) quenching. The contribution of these two components varies with environmental factors, such as light intensity and stress level due to the different response mechanisms. This study aims to explore the feasibility of estimating NPQ using photosynthesis-related vegetation parameters available from remote sensing by considering the two components of NPQ. We concurrently measured passive vegetation reflectance spectra by spectrometer, as well as active fluorescence parameters by pulse-amplitude modulated (PAM) of rice (Oryza sativa) leaves. Subsequently, we explored the ability of the selected vegetation parameters (including the photochemical reflectance index (PRI), inverted red-edge chlorophyll index (IRECI), near-infrared reflectance of vegetation (NIR_v), and fluorescence quantum yield (ΦF)) to estimate NPQ. Based on different combinations of these remote sensing parameters, empirical models were established to estimate NPQ using the linear regression method. Experimental analysis shows that the contribution of qE and non-qE components varied under different illumination conditions. Under high illumination, the NPQ was attributed primarily to the qE component, while under low illumination, it was equally attributed to the qE and non-qE components. Among all tested parameters, ΦF was sensitive to the qE component variation, while IRECI and NIR_v were sensitive to the non-qE component variation. Under high illumination, integrating ΦF in the regression model captured NPQ variations well (R² > 0.74). Under low illumination, ΦF, IRECI, and NIR_v explained 24%, 62%, and 65% of the variation in NPQ, respectively, while coupling IRECI or NIR_v with ΦF considerably improved the accuracy of NPQ estimation (R² > 0.9). For all the samples under both low and high illumination, the combination of ΦF with at least one of the other parameters (including IRECI, NIR_v and PAR) offers a more versatile and reliable approach to estimating NPQ than using any single parameter alone. The findings of this study contribute to the further development of remote sensing methods for NPQ estimation at the canopy scale in the future.

The widely used SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer model (RTM) is designed for canopies that can be considered as homogeneous turbid media and thus should be inadequate for row canopies. However, numerous studies have employed the SAIL model for row crops (e.g., wheat and maize) to simulate canopy reflectance or retrieve vegetation properties with satisfactory accuracy. One crucial reason may be that under certain conditions, a row crop canopy can be considered as a turbid medium, fulfilling the assumption of the SAIL model. Yet, a comprehensive analysis about the performance of SAIL in row canopies under various conditions is currently absent. In this study, we employed field datasets of wheat canopies and synthetic datasets of wheat and maize canopies to explore the impacts of the vegetation cover fraction (fCover), solar angle and soil background on the performance of SAIL in row crops. In the numerical experiments, the LESS 3D RTM was used as a reference to evaluate the performance of SAIL for various scenarios. The results show that the fCover is the most significant factor, and the row canopy with a high fCover has a low soil background influence. For a non-black soil background, both the field measurement and simulation datasets showed that the SAIL model accuracy initially decreased, and then increased with an increasing fCover, with the most significant errors occurring when the fCover was between about 0.4 and 0.7. As for the solar angles, the accuracy of synthetic wheat canopy will be higher with a larger SZA (solar zenith angle), but that of a synthetic maize canopy is little affected by the SZA. The accuracy of the SAA (solar azimuth angle) in an across-row direction is always higher than that in an along-row direction. Additionally, when the SZA ranges from 65° to 75° and the fCover of wheat canopies are greater than 0.6, SAIL can simulate the canopy reflectance with satisfactory accuracy (rRMSE < 10%); the same accuracy can be achieved in maize canopies as long as the fCover is greater than 0.8. These findings provide insight into the applicability of SAIL in row crops and support the use of SAIL in row canopies under certain conditions (with rRMSE < 10%).

Daily water stress reflects the water stress status of crops on a specific day, which is crucial for studying drought progression and guiding precision irrigation. However, accurately monitoring the daily water stress remains challenging, particularly when eliminating the impact of historical stress and normal growth. Recent studies have demonstrated that the diurnal characteristics of the crop canopy obtained via remote sensing techniques can be used to assess daily water stress levels effectively. Remote sensing observations, such as the solar-induced chlorophyll fluorescence (SIF) and reflectance, offer information on the crop canopy structure, physiology, or their combination. However, the sensitivity of different structural, physiological, or combined remote sensing variables to the daily water stress remains unclear. We investigated this issue via continuous measurements of the active fluorescence, leaf rolling, and canopy spectra of maize under different irrigation conditions. The results indicated that with increasing water stress, vegetation exhibited significant coordinated diurnal variations in both structure and physiology. The influence of water stress was minimal in the morning but peaked at noon. The morning-to-noon ratio (NMR) of the apparent SIF yield (SIFy), in which only the effect of the photosynthetically active radiation (PAR) is eliminated and in which both structural and physiological information is incorporated, exhibited the highest sensitivity to water stress variations. This NMR of the SIFy was followed by the NMR of the normalized difference vegetation index ($\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}$) and the NMR of the canopy fluorescence emission efficiency ($\mathsf{\Phi }{\mathrm{F}}_{\mathrm{c}\mathrm{a}\mathrm{n}\mathrm{o}\mathrm{p}\mathrm{y}}$) obtained via the fluorescence correction vegetation index (FCVI) method, which primarily reflect structural and physiological information, respectively. This study highlights the advantages of utilizing diurnal vegetation structural and physiological variations for monitoring daily water stress levels.

Daily water stress reflects the water stress status of crops on a specific day, which is crucial for studying drought progression and guiding precision irrigation. However, accurately monitoring the daily water stress remains challenging, particularly when eliminating the impact of historical stress and normal growth. Recent studies have demonstrated that the diurnal characteristics of the crop canopy obtained via remote sensing techniques can be used to assess daily water stress levels effectively. Remote sensing observations, such as the solar-induced chlorophyll fluorescence (SIF) and reflectance, offer information on the crop canopy structure, physiology or their combination. However, the sensitivity of different structural, physiological or combined remote sensing variables to the daily water stress remains unclear. We investigated this issue via continuous measurements of active fluorescence, leaf rolling, and canopy spectra of maize under different irrigation conditions. The results indicated that with increasing water stress, vegetation exhibited significant coordinated diurnal variations in both structure and physiology. The influence of water stress was minimal in the morning but peaked at noon. The morning-to-noon ratio (NMR) of the apparent SIF yield (SIFy), in which only the effect of the photosynthetically active radiation (PAR) is eliminated and in which both structural and physiological information is incorporated, exhibited the highest sensitivity to water stress variations. This NMR of the SIFy was followed by the NMR of the normalized difference vegetation index (NDVI) and the NMR of the canopy fluorescence emission efficiency (ΦFcanopy) obtained via the fluorescence correction vegetation index (FCVI) method, which primarily reflect structural and physiological information, respectively. This study highlights the advantages of utilizing diurnal vegetation structural and physiological variations for monitoring daily water stress levels.