Is satellite Sun-Induced Chlorophyll Fluorescence more indicative than vegetation indices under drought condition?

Evaluation on spatiotemporal consistency between solar-induced chlorophyll fluorescence and vegetation indices in grassland ecosystems

Monitoring grassland productivity dynamics is essential for understanding the impacts of climate variation and human activities. Solar-induced chlorophyll fluorescence (SIF) has been validated as an effective indicator of gross primary productivity. Satellite-derived vegetation indices (VIs) have long been used as key proxies for vegetation productivity. However, the ability of different VIs to represent grassland productivity in relation to SIF, as well as their spatiotemporal consistency with SIF at various scales, remains unclear. In this study, we systematically compared the performance of the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Near-Infrared Reflectance of Vegetation (NIRv), using SIF as a benchmark in grassland areas of China. Utilizing TROPOMI SIF and MODIS VI datasets from 2018 to 2021, we analyzed the spatial and temporal consistency between VIs and SIF at a monthly scale and 0.05-degree resolution, employing Pearson correlation coefficients, paired-sample t-tests, and two-way Analysis of Variance (ANOVA). The results indicate that NIRv consistently demonstrates a higher capacity to capture variations in SIF compared to EVI and NDVI. In low-elevation areas with high-productivity grasslands, all three vegetation indices exhibit a stronger ability to represent vegetation productivity than in high-elevation areas with low-productivity vegetation types. These findings suggest that, at a monthly and regional spatiotemporal scale, NIRv can serve as a robust complement to SIF in monitoring vegetation productivity dynamics, particularly given the challenges in acquiring high-quality, long-term SIF data.

What to Choose for Estimating Leaf Water Status-Spectral Reflectance or In vivo Chlorophyll Fluorescence?

In the context of global climate change and the increasing need to study plant response to drought, there is a demand for easily, rapidly, and remotely measurable parameters that sensitively reflect leaf water status. Parameters with this potential include those derived from leaf spectral reflectance (R) and chlorophyll fluorescence. As each of these methods probes completely different leaf characteristics, their sensitivity to water loss may differ in different plant species and/or under different circumstances, making it difficult to choose the most appropriate method for estimating water status in a given situation. Here, we present a simple comparative analysis to facilitate this choice for leaf-level measurements. Using desiccation of tobacco (Nicotiana tabacum L. cv. Samsun) and barley (Hordeum vulgare L. cv. Bojos) leaves as a model case, we measured parameters of spectral R and chlorophyll fluorescence and then evaluated and compared their applicability by means of introduced coefficients (coefficient of reliability, sensitivity, and inaccuracy). This comparison showed that, in our case, chlorophyll fluorescence was more reliable and universal than spectral R. Nevertheless, it is most appropriate to use both methods simultaneously, as the specific ranking of their parameters according to the coefficient of reliability may indicate a specific scenario of changes in desiccating leaves.

The fusion of vegetation indices increases the accuracy of cotton leaf area prediction

Introduction

Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status.

Methods

In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI.

Results

The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively.

Discussion

Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.

Multifaceted insights into the environmental adaptability of Arnebia guttata under drought stress

Introduction

Global warming has led to increased environmental stresses on plants, notably drought. This affects plant distribution and species adaptability, with some medicinal plants showing enhanced drought tolerance and increased medicinal components. In this pioneering study, we delve into the intricate tapestry of Arnebia guttata, a medicinal plant renowned for its resilience in arid environments. By fusing a rich historical narrative with cutting-edge analytical methodologies, this research endeavors to demystify the plant's intricate response to drought stress, illuminating its profound implications for medicinal valorization.

Methods

The methodology includes a comprehensive textual research and resource investigation of A. guttata, regionalization studies, field sample distribution analysis, transcriptome and metabolome profiling, rhizosphere soil microbiome analysis, and drought stress experiments. Advanced computational tools like ArcGIS, MaxEnt, and various bioinformatics software were utilized for data analysis and modeling.

Results

The study identified significant genetic variations among A. guttata samples from different regions, correlating with environmental factors, particularly precipitation during the warmest quarter (BIO18). Metabolomic analysis revealed marked differences in metabolite profiles, including shikonin content, which is crucial for the plant's medicinal properties. Soil microbial community analysis showed variations that could impact plant metabolism and stress response. Drought stress experiments demonstrated A. guttata's resilience and its ability to modulate metabolic pathways to enhance drought tolerance.

Discussion

The findings underscore the complex interplay between genetic makeup, environmental factors, and microbial communities in shaping A. guttata's adaptability and medicinal value. The study provides insights into how drought stress influences the synthesis of active compounds and suggests that moderate stress could enhance the plant's medicinal properties. Predictive modeling indicates future suitable growth areas for A. guttata, aiding in resource management and conservation efforts. The research contributes to the sustainable development of medicinal resources and offers strategies for improving the cultivation of A. guttata.

Quantifying the drought sensitivity of grassland under different climate zones in Northwest China

Grassland is essential for maintaining the stability and functionality of terrestrial ecosystems. Although previous research has explored how grassland responds to drought, the drought sensitivity of grassland (DSG) across climate zones and aridity gradients remains uncertain. In this study, we conducted a comprehensive investigation spanning 1982 to 2015 in Northwest China. To assess the time-cumulative effect (TCE) and the time-lag effect (TLE) of drought on grassland, we employed Spearman rank correlation analysis, utilizing long-term datasets of the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI). This analysis allowed us to quantify the DSG in the region and further examine its variations across climate zones and aridity gradient. Our results revealed that 81.2 % and 99.7 % of the grassland in Northwest China was influenced by the TCE and TLE of drought, respectively, with 38.2 % and 60.9 % of these effects being statistically significant (p < 0.05). The mean accumulated and lagged timescales of drought on grassland were 7.89 and 9.41 months, respectively. Remarkably, the highest DSG was observed in the semi-arid zone (0.58), followed by the arid (0.54), sub-humid (0.51), and humid (0.44) zones. Furthermore, we identified significant nonlinear variation patterns of DSG along the aridity gradient, characterized by several discernible trend breaks. These findings contribute to our understanding of the impacts of drought on vegetation, particularly in ecologically fragile regions.

Water stress significantly affects the diurnal variation of solar-induced chlorophyll fluorescence (SIF): A case study for winter wheat

Remote sensing of Solar-induced chlorophyll fluorescence (SIF) has been widely used in estimating Gross Primary Productivity (GPP) and detecting stress in terrestrial ecosystems. Water stress adversely impacts the growth, development, and productivity of a plant. Recently, the characterizing and understanding of the diurnal cycling of plant functioning and ecosystem processes has been explored using SIF. However, the diurnal response of SIF to different levels of water stress remains unclear. This study conducted field experiments on winter wheat by subjecting it to different levels of water stress including well-watered (CK) and, mild, moderate, and severe water stress (D1, D2, D3), and collected the spectral data using an automated SIF measurement system. The results observed the strong SIF-PAR (photosynthetically active radiation) correlations and that these relationships gradually decoupled with increasing water stress, which further decreased the accuracy of temporal upscaling of far-red SIF from an instantaneous to daily scale. To quantify the characteristics of diurnal far-red SIF, five indices including peak time, peak value, curve opening coefficient (leading coefficient of the parabola), and left/right slopes of the peak were proposed. The results demonstrated that diurnal far-red SIF was characterized by an earlier peak time, decreasing peak value, wider curve opening, and flattening right slope from the CK plot to the D3 plot. There were certain mechanisms linking the different indices, for example, between peak size and opening coefficient. Furthermore, the response of far-red SIF to water stress was most pronounced at noon. SIF/PAR exhibited a more significant response to varying water stress compared to far-red SIF, which mitigated the negative influence of PAR variations on diurnal SIF. These findings contribute to the monitoring of plant water dynamics at fine temporal scales.

Detecting drought stress occurrence using synergies between Sun induced fluorescence and vegetation surface temperature spatial records

Drought stress occurrence and recovery from drought can be detected using a single spatial set of simultaneous observations of SIF and canopy temperature records. Temporal and spatial responses to drought and heat stresses by plant stands of a drought-adapted diverse grassland ecosystem were studied using sun induced fluorescence (SIF,O2A and O2B bands) and further ecophysiological (canopy temperature (Tsurf), spatially modeled evapotranspiration, vegetation reflectance spectra) variables collected along spatial sampling grids while also utilizing eddy covariance measured carbon dioxide (net ecosystem exchange: NEE, gross primary production: GPP) and water flux (evapotranspiration: ET) data. The grids were of 0.5 and 5 ha spatial extents and contained 78 sampling points. Data were collected in four spatial sampling campaigns, two under drought (early summer) and another two during and after recovery (midsummer) at both spatial resolutions. Small values of spatial SIF_A averages (around 0.5 mW m-2 nm-1 sr-1) under strong early summer drought increased (to around 2 mW m-2 nm-1 sr-1) due recovery upon rain arrivals, showing high (R2: 0.8-0.88) positive temporal correlations to eddy covariance measured carbon (GPP, NEE) and water (ET) fluxes. Spatial averages of LAI, vegetation indices (NDVI, NIRv) and modeled ET followed similar temporal patterns. While SIF was depressed by drought, it showed higher values in high canopy temperature vegetation patches than in vegetation patches with lower Tsurf. The spatial pattern of higher SIF in higher Tsurf patches was persistent (2 weeks) under drought. The positive SIF_A-Tsurf spatial correlation turned into negative/not significant after recovery of the grassland from the drought, while hot summer weather persisted. It is proposed that, by using a single set of simultaneously measured spatial SIF and Tsurf data it is possible to infer whether the studied vegetation is under drought (and heat) stress while it could not be decided on the base of SIF data alone. Evaluation of the slope of the above relationship seems therefore beneficial before e.g. starting the (stress) classification procedure based on SIF.

The multifaceted roles of Arbuscular Mycorrhizal Fungi in peanut responses to salt, drought, and cold stress

BACKGROUND

Arbuscular Mycorrhizal Fungi (AMF) are beneficial microorganisms in soil-plant interactions; however, the underlying mechanisms regarding their roles in legumes environmental stress remain elusive. Present trials were undertaken to study the effect of AMF on the ameliorating of salt, drought, and cold stress in peanut (Arachis hypogaea L.) plants. A new product of AMF combined with Rhizophagus irregularis SA, Rhizophagus clarus BEG142, Glomus lamellosum ON393, and Funneliformis mosseae BEG95 (1: 1: 1: 1, w/w/w/w) was inoculated with peanut and the physiological and metabolomic responses of the AMF-inoculated and non-inoculated peanut plants to salt, drought, and cold stress were comprehensively characterized, respectively.

RESULTS

AMF-inoculated plants exhibited higher plant growth, leaf relative water content (RWC), net photosynthetic rate, maximal photochemical efficiency of photosystem II (PSII) (Fv/Fm), activities of antioxidant enzymes, and K⁺: Na⁺ ratio while lower leaf relative electrolyte conductivity (REC), concentration of malondialdehyde (MDA), and the accumulation of reactive oxygen species (ROS) under stressful conditions. Moreover, the structures of chloroplast thylakoids and mitochondria in AMF-inoculated plants were less damaged by these stresses. Non-targeted metabolomics indicated that AMF altered numerous pathways associated with organic acids and amino acid metabolisms in peanut roots under both normal-growth and stressful conditions, which were further improved by the osmolytes accumulation data.

CONCLUSION

This study provides a promising AMF product and demonstrates that this AMF combination could enhance peanut salt, drought, and cold stress tolerance through improving plant growth, protecting photosystem, enhancing antioxidant system, and regulating osmotic adjustment.

Variability in Crop Response to Spatiotemporal Variation in Climate in China, 1980–2014

As the population increases and climate extremes become more frequent, the pressure on food supply increases. A better understanding of the influence of climate variations on crop yield in China would be of great benefit to global food security. In this study, gridded, daily meteorological data and county-level annual yield data were used to quantify the climate sensitivity of corn, rice, and spring wheat yields, and identify the spatiotemporal variation relationship between climate and yields from 1980 to 2014. The results showed that rice and corn were more sensitive to climate variations than spring wheat, both spatially and temporally. Photosynthetic active radiation (PAR) was found to be beneficial to rice in northeast China and the Yangtze River basin, as well as corn in the south and spring wheat in Xinjiang, but not to rice in the south of the Yangtze River and spring wheat in the southeast coast. The temperature centroid shift was the main driving factor causing the movement of the centroid of the three crops. For every 1 km shift of the temperature centroid, the corn and rice yield centroids moved 0.97 km and 0.34 km, respectively. These findings improve our understanding of the impacts of climate variations on agricultural yields in different regions of China.

Remote Sensing of Instantaneous Drought Stress at Canopy Level Using Sun-Induced Chlorophyll Fluorescence and Canopy Reflectance

Climate change amplifies the intensity and occurrence of dry periods leading to drought stress in vegetation. For monitoring vegetation stresses, sun-induced chlorophyll fluorescence (SIF) observations are a potential game-changer, as the SIF emission is mechanistically coupled to photosynthetic activity. Yet, the benefit of SIF for drought stress monitoring is not yet understood. This paper analyses the impact of drought stress on canopy-scale SIF emission and surface reflectance over a lettuce and mustard stand with continuous field spectrometer measurements. Here, the SIF measurements are linked to the plant’s photosynthetic efficiency, whereas the surface reflectance can be used to monitor the canopy structure. The mustard canopy showed a reduction in the biochemical component of its SIF emission (the fluorescence emission efficiency at 760 nm—ϵ760) as a reaction to drought stress, whereas its structural component (the Fluorescence Correction Vegetation Index—FCVI) barely showed a reaction. The lettuce canopy showed both an increase in the variability of its surface reflectance at a sub-daily scale and a decrease in ϵ760 during a drought stress event. These reactions occurred simultaneously, suggesting that sun-induced chlorophyll fluorescence and reflectance-based indices sensitive to the canopy structure provide complementary information. The intensity of these reactions depend on both the soil water availability and the atmospheric water demand. This paper highlights the potential for SIF from the upcoming FLuorescence EXplorer (FLEX) satellite to provide a unique insight on the plant’s water status. At the same time, data on the canopy reflectance with a sub-daily temporal resolution are a promising additional stress indicator for certain species.

Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R2 of 0.85 and RMSE of 0.78 t/ha 3–4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.