Using the proper means to improve seed germination is of great significance in agriculture and forestry. Here, a meta-analysis was used to examine whether metal nanoparticle treatments have a specific effect on the seed germination and seedling growth of agricultural species. Using the Web of Science (1950–2021), PubMed (1950–2021), and Scopus (1950–2021) databases, a paper search was conducted using the following items (“nanoparticles” and “seed germination”, “nanomaterials” and “seed germination”) to filter the references in the title, abstract, and keywords of the published articles. The results indicated that nanoparticle (NP) treatments had a significantly positive effect on the final germination percentage (FGP), with a mean difference (MD) (that is, the overall effect) of 1.97 (0.96, 2.98) for the silver (Ag)-NP subgroup, 1.21 (0.34, 2.09) for the other-NP subgroup, 1.40 (0.88, 1.92) for the total based on the NP types, 1.47 (0.85, 2.09) for the “Concentrations: 50 mg/L” subgroup, and 0.82 (0.72, 0.93) for the total based on the NP concentrations. However, there was no statistical correlation between the nanoparticle concentrations and shoot length (SL), due to the inclusion of zero in the 95% CI of the overall effect. Therefore, Ag-NPs could increase the FGP more than other-NPs, while Zn-NPs enhanced RL more. Moreover, NPs at lower concentrations could improve the FGP and RL of crop species to a larger extent than NPs at higher concentrations. This meta-analysis can provide a reference for the nanoparticle treatment technology utilization in agricultural and forest seeds.

Sun-induced chlorophyll fluorescence (SIF) is a proxy for plant photosynthesis. However, available SIF products either have a low spatial resolution or are spatially discontinuous. This paper proposes an improved downscaling method to generate a continuous 0.05-degree SIF dataset from GOME-2 retrievals, covering the period from February 2007 to March 2019. First, a random forest model was developed to predict SIF using training samples from GOME-2 SIF and explanatory variables with a resolution of 0.5 degrees (including reflectance at visible and near-infrared bands, vegetation index, temperature, and cosine values of the sun-zenith angle). Then, a 0.05-degree SIF dataset was predicted using the trained model and the corresponding explanatory variables at a resolution of 0.05 degrees. Subsequently, the predicted 0.05-degree SIF dataset was used as the weighting coefficient to redistribute the original 0.5-degree GOME-2 SIF to a downscaled 0.05-degree SIF dataset (DSIF) based on the energy conservation principle. The results showed that DSIF was more consistent with the original 0.5-degree retrievals than the similar GOME-2 derived spatially extended SIF dataset, which produced spatial details with a resolution of 0.05 degrees. The structure and physiology of SIF information were well represented in the DSIF, which is imperative for assessing global photosynthetic activity.

Structural–physiological factors affect the accurate estimation of vegetation gross primary production (GPP) under various types of environmental stress. Solar-induced chlorophyll fluorescence (SIF), which is directly linked to photosynthesis, has been effectively used to estimate and monitor GPP. However, understanding of the physiological mechanism linking SIF to GPP under stress remains limited. In this article, the link between SIF and GPP at diurnal and seasonal timescales was explored for a maize field and its response to drought stress (as defined by the crop water stress index, CWSI) using three-years of continuous tower-based measurements was investigated. The results show that the ratio of GPP to SIF decreased with increasing drought stress levels, and the canopy stomata conductance (Gs) declined synchronously. Compared to two canopy structural factors (NDVI and NIRv), both the Pearson and partial correlation coefficients for the relationship between Gs and the ratio of GPP to the total SIF was higher (0.38, p < 0.01 and 0.32, p < 0.01 with photosynthetically active radiation as the control variable, respectively). We also found that ${{\bf \Phi }_F}$ tracked the changes in LUE well under drought conditions (CWSI > 0.6), which demonstrated that SIF can be a powerful parameter for estimating GPP under drought stress. However, there was a smaller drop in ${{\bf \Phi }_F}$ under drought stress (slope = –0.002) compared to the slope for the relationship between LUE and CWSI (–0.08). The response of light reactions to drought stress may be muted compared to the stomatal response. These findings confirm that the Gs is sensitive to drought and is important for the SIF-based GPP estimation model. It also provides reliable evidence that SIF data include a large amount of physiological information and can serve as a potential indicator for detecting drought and estimating GPP.

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF₇₆₀) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF₇₆₀ tracked GPP well at both diurnal and seasonal scales, and that SIF₇₆₀ was more linearly correlated to PAR than GPP was. Therefore, the SIF₇₆₀–GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R² value was 0.66 in 2017 and 2018. Based on daily mean observations, the R² value was 0.82 and 0.76 in 2017 and 2018, respectively. Secondly, it was found that the SIF₇₆₀–GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF₇₆₀ decreased noticeably as the CI increased. Finally, the SIF₇₆₀-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF₇₆₀-based GPP estimates based on daily mean observations: the value of R² increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF₇₆₀-based GPP estimation was improved greatly by including the CI, giving a higher R² and a lower RMSE. These values improved from R² = 0.66 and RMSE = 7.02 mw/m²/nm/sr to R² = 0.76 and RMSE = 6.36 mw/m²/nm/sr for the linear model, and from R² = 0.71 and RMSE = 4.76 mw/m²/nm/sr to R² = 0.78 and RMSE = 3.50 mw/m²/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF₇₆₀ is a reliable proxy for GPP and that SIF₇₆₀-based GPP estimation can be greatly improved by integrating the CI with SIF₇₆₀. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.

Reliable estimation of gross primary production (GPP) of terrestrial ecosystems is crucial in the global carbon cycle and ecosystem functioning studies. Solar-induced chlorophyll fluorescence (SIF) has emerged as an unprecedented proxy for terrestrial GPP estimation. TanSat SIF with a 2-km fine spatial resolution provides an excellent opportunity to investigate the relationships of satellite SIF and eddy covariance (EC) GPP at ecosystem or site scale, while has not been investigated. In this article, we investigated the relationship between TanSat SIF and EC GPP based on flux tower sites encompassing different biomes over the globe. TanSat SIF exhibited strong relationships with EC GPP at Fe and KI bands for instantaneous and daily timescales. The correlations between EC GPP and TanSat SIF ( R ² = 0.61) were much better than moderate resolution imaging spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)-corrected vegetation indices (VIs) ( R ² = 0.48–0.58). Further comparisons between SIF and GPP with absorbed photosynthetically active radiation (APAR) and two environmental factors (fTmin and fVPD) showed that SIF responded to APAR and was similarly affected by environmental factors as GPP. GPP derived from SIF ( R ² = 0.64) also performed slightly better than enhanced vegetation index and near-infrared reflectance of vegetation and even light use efficiency MODIS GPP algorithm ( R ² = 0.58–0.60). Therefore, our results demonstrated the feasibility of SIF on estimating GPP at sites or ecosystem levels; in addition, incorporating TanSat SIF with other satellite SIF products will open a new era for carbon cycling and ecosystem functioning studies.