Estimation of the net primary productivity of winter wheat based on the near-infrared radiance of vegetation.

Quantifying net primary productivity (NPP) is important for understanding the global carbon cycle and for assessing ecosystem carbon dynamics. However, uncertainties remain in NPP estimation. Using winter wheat data obtained from an experimental station in 2019, this study evaluated the ability of the near-infrared radiance of vegetation (NIRV,Rad) to estimate NPP at different time scales and established an estimation model based on NIRV,Rad, where NIRV,Rad was defined as the product of the normalized difference vegetation index (NDVI) and the near-infrared radiance. The results showed that the linear relationship between NIRV,Rad and NPP was superior to the relationship between NPP and NDVI, enhanced vegetation index-2 (EVI2), and near-infrared reflectance of vegetation (NIRV,Ref) at each time scale (hourly, daily, and growth period). The advantage of NIRV,Rad was more evident on the hourly scale, in which the R2 of NIRV,Rad and NPP reached 0.77, whereas the R2 values of the correlation of NDVI, EVI2, and NIRV,Ref with NPP were 0.30, 0.16, and 0.14, respectively. There existed a strong linear relationship between absorbed photosynthetically active radiation, net photosynthetic rate, leaf area index, and NIRV,Rad, which explained the good relationship between NIRV,Rad and NPP. Through a comparative analysis of the various models, the NIRV,Rad model was found to have the strongest ability to estimate NPP and the R2, with the measured NPP reaching 0.81. The accuracy of NIRV,Rad provides a new method for estimating NPP and a scientific basis for estimating NPP using high-resolution satellite remote sensing data on a regional scale.

Soil water deficit promotes the effect of atmospheric water deficit on solar-induced chlorophyll fluorescence.

Solar-induced chlorophyll fluorescence (SIF) is a novel optical signal that has been successfully used to track plant dynamics with the influence of soil water deficit. However, the effect of atmospheric water deficit on SIF under the impact of soil water deficit still remains unclear. Here, continuous measurements of SIF (at 760 nm, F760) of winter wheat under different soil water deficit were collected with a self-developed system. Additionally, soil moisture and atmosphere parameters [including air temperature (Ta), relative air humidity (Rh), and photosynthetically active radiation at 400-700 nm (PAR)] were also synchronously collected by common commercial devices. Vapor pressure deficit (VPD) was calculated based on the measurements of Ta and Rh. The results showed that the driving effect of PAR on F760 was obvious as we expected. Additionally, such effects of PAR on AF760 (F760/PAR) and Fy760 (F760/L685, L685 was canopy radiance at 685 nm) still existed when the PAR influences were partially removed by the calculation of F760/PAR and F760/L685. Furthermore, the relationship of PAR with AF760 or Fy760 was observed to be strengthened under the situation of water deficit through the analysis of Pearson correlations. With the influence of PAR, the accelerative effect of VPD on SIF under soil water deficit was not always observed in our study. Nevertheless, when the effect of PAR was removed by using partial correlation, VPD showed much stronger correlation with SIF in soil water stressed plot than that in unstressed one both at diurnal and seasonal scales. These results revealed that soil water deficit might promote the effect of atmospheric water deficit on SIF. This study has great significance for the application of SIF in drought monitoring and health assessments in terrestrial ecosystem.

Linking the molecular composition of autochthonous dissolved organic matter to source identification for freshwater lake ecosystems by combination of optical spectroscopy and FT-ICR-MS analysis.

Autochthonous dissolved organic matter (DOM) is increasingly released in lakes due to eutrophication, and thus affects the composition and environmental behaviors of DOM in eutrophic lakes. However, there are only limited studies on the molecular characteristics of autochthonous DOM and its influencing mechanisms. Herein, end-member DOM samples of macrophytes, algae, sediments and freshwater DOM samples in eutrophic lakes (Ch:Taihu and Dianchi) were collected and characterized by optical spectroscopy and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). The results revealed the chemical structures of autochthonous DOM were more aliphatic and less oxidized, which was marked by increases in lipid compounds and decreases in the lignin components as compared to the allochthonous DOM-dominated freshwaters. More specially, algae-derived DOM contains more lipid compounds, while macrophyte-derived DOM was dominated by lignin and tannin compounds according to Van Krevelen plots. Sediment-derived DOM contained more N-containing compounds. The traditional optical indices indicated the relative aromaticity covaried with polyphenolic and polycyclic aromatics, whereas those reflecting autochthonous DOM covaried with more aliphatic compounds. Multivariate analysis of FT-ICR-MS data of end-members and freshwaters revealed the predominant terrestrial input to Lake Taihu and greater contribution of algae released DOM to Dianchi. This study provides critical information about the characteristics of autochthonous DOM at a molecular level and confirmed autochthonous DOM was compositionally distinct from allochthonous DOM. Overall autochthonous DOM should be gained more attention in the eutrophic lakes.

Evaluating the utility of solar-induced chlorophyll fluorescence for drought monitoring by comparison with NDVI derived from wheat canopy.

Normalized Difference Vegetation Index (NDVI) has been extensively used in continuous and long-term drought monitoring over large-scale, but with late response to drought-related changes of photosynthesis. Instead, solar-induced chlorophyll fluorescence (SIF) is more closely related to photosynthesis and thus is proposed to track the impacts of drought on vegetation growth. However, the detailed difference between SIF and NDVI in responding to drought has not been thoroughly explored. Here we present continuous ground measurements of NDVI and SIF at 760nm over four plots of wheat with different intensities of drought (well-watered treatment, moderate drought, severe drought and extreme drought). The average values of seasonal SIF were significantly lower under severe drought and extreme drought, while NDVI means only showed significant reduction in extreme drought. In the seasonal patterns, daily SIF could clearly separate the difference of drought gradient, while the difference of daily NDVI was clearer in the end of the field campaign. Daily SIF also significantly and positively correlated with soil moisture, indicating that SIF could be considered as an estimator of soil moisture to detect the information about agricultural drought. Furthermore, in extreme drought plot, the correlation of SIF and soil moisture was higher than that of NDVI and soil moisture in a shorter time lag (<15-day) but weaker in a longer time lag (longer than 30-day). The relationships of growth parameters with SIF and NDVI were further analyzed, showing a saturation of NDVI and unsaturation of SIF at high values of leaf area index and relative water content. These results suggested that SIF is better fit in early drought monitoring, especially over closure canopy, while NDVI is more feasible when drought lasted over a long time scale. Our findings in the study might provide deep insight into the utility of SIF in drought monitoring.

Cation-induced coagulation of aquatic plant-derived dissolved organic matter: Investigation by EEM-PARAFAC and FT-IR spectroscopy.

Complexation and coagulation of plant-derived dissolved organic matter (DOM) by metal cations are important biogeochemical processes of organic matter in aquatic systems. Thus, coagulation and fractionation of DOM derived from aquatic plants by Ca(II), Al(III), and Fe(III) ions were investigated. Metal ion-induced removal of DOM was determined by analyzing dissolved organic carbon in supernatants after addition of these metal cations individually. After additions of metal ions, both dissolved and coagulated organic fractions were characterized by use of fluorescence excitation emission matrix-parallel factor (EEM-PARAFAC) analysis and Fourier transform infrared (FT-IR) spectroscopy. Addition of Ca(II), Fe(III) or Al(III) resulted in net removal of aquatic plant-derived DOM. Efficiencies of removal of DOM by Fe(III) or Al(III) were greater than that by Ca(II). However, capacities to remove plant-derived DOM by the three metals were less than which had been previously reported for humic materials. Molecular and structural features of plant-derived DOM fractions in associations with metal cations were characterized by changes in fluorescent components and infrared absorption peaks. Both aromatic and carboxylic-like organic matters could be removed by Ca(II), Al(III) or Fe(III) ions. Whereas organic matters containing amides were preferentially removed by Ca(II), and phenolic materials were selectively removed by Fe(III) or Al(III). These observations indicated that plant-derived DOM might have a long-lasting effect on water quality and organisms due to its poor coagulation with metal cations in aquatic ecosystems. Plant-derived DOM is of different character than natural organic matter and it is not advisable to attempt removal through addition of metal salts during treatment of sewage.

A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model.

The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding why the adoption of counter measures against drought is important and direct farmers to choose drought-resistant crops.

A new framework for evaluating the impacts of drought on net primary productivity of grassland.

This paper presented a valuable framework for evaluating the impacts of droughts (single factor) on grassland ecosystems. This framework was defined as the quantitative magnitude of drought impact that unacceptable short-term and long-term effects on ecosystems may experience relative to the reference standard. Long-term effects on ecosystems may occur relative to the reference standard. Net primary productivity (NPP) was selected as the response indicator of drought to assess the quantitative impact of drought on Inner Mongolia grassland based on the Standardized Precipitation Index (SPI) and BIOME-BGC model. The framework consists of six main steps: 1) clearly defining drought scenarios, such as moderate, severe and extreme drought; 2) selecting an appropriate indicator of drought impact; 3) selecting an appropriate ecosystem model and verifying its capabilities, calibrating the bias and assessing the uncertainty; 4) assigning a level of unacceptable impact of drought on the indicator; 5) determining the response of the indicator to drought and normal weather state under global-change; and 6) investigating the unacceptable impact of drought at different spatial scales. We found NPP losses assessed using the new framework were more sensitive to drought and had higher precision than the long-term average method. Moreover, the total and average losses of NPP are different in different grassland types during the drought years from 1961-2009. NPP loss was significantly increased along a gradient of increasing drought levels. Meanwhile, NPP loss variation under the same drought level was different in different grassland types. The operational framework was particularly suited for integrative assessing the effects of different drought events and long-term droughts at multiple spatial scales, which provided essential insights for sciences and societies that must develop coping strategies for ecosystems for such events.