Tropical tall forests are more sensitive and vulnerable to drought than short forests.

Our limited understanding of the impacts of drought on tropical forests significantly impedes our ability in accurately predicting the impacts of climate change on this biome. Here, we investigated the impact of drought on the dynamics of forest canopies with different heights using time-series records of remotely sensed Ku-band vegetation optical depth (Ku-VOD), a proxy of top-canopy foliar mass and water content, and separated the signal of Ku-VOD changes into drought-induced reductions and subsequent non-drought gains. Both drought-induced reductions and non-drought increases in Ku-VOD varied significantly with canopy height. Taller tropical forests experienced greater relative Ku-VOD reductions during drought and larger non-drought increases than shorter forests, but the net effect of drought was more negative in the taller forests. Meta-analysis of in situ hydraulic traits supports the hypothesis that taller tropical forests are more vulnerable to drought stress due to smaller xylem-transport safety margins. Additionally, Ku-VOD of taller forests showed larger reductions due to increased atmospheric dryness, as assessed by vapor pressure deficit, and showed larger gains in response to enhanced water supply than shorter forests. Including the height-dependent variation of hydraulic transport in ecosystem models will improve the simulated response of tropical forests to drought.

Climate impact from agricultural management practices in the Canadian Prairies: Carbon equivalence due to albedo change.

It is generally accepted that land use and land management practices impact climate change through sequestration of carbon in soils, but modulation of surface energy budget can also be important. Using Landsat data to characterize cropland albedos in Canada's three prairie soil zones, this study estimates the atmospheric carbon equivalent drawdown of albedo radiative forcing for three management practices: 1) moving from conventional tillage to no-till, 2) eliminating summer fallow in crop rotations, and 3) growing crops with higher albedos. In a 50-year time horizon, conversion from conventional tillage to no-till results in a total equivalent atmospheric CO2 (CO2-eq) drawdown of 1.0-1.5 kg m-2, and conversion from summer fallow to crops results in CO2-eq drawdown of 1.1-2.4 kg m-2. Conversion of summer fallow to crops results in different magnitudes of CO2-eq drawdown depending on specific crops. Lentils, peas, and canola have relatively higher albedo than that of spring wheat and flax; hence, a larger magnitude of CO2-eq drawdown results when they replace summer fallow in the rotation. For the management changes from 1990 to 2019 for the whole Canadian Prairies, albedo changes induced a CO2-eq drawdown of about 179.3 ± 20.9 Tg due to increased area of no-till, and 101.6 ± 9.5 Tg due to reduced area under fallow. The study shows that the magnitudes of CO2-eq drawdown due to albedo change are comparable to that due to soil carbon sequestration. Therefore, it is important to account for cropland albedo changes in assessing the potential of agricultural management practices to mitigate climate change.

Generating Time-Series LAI Estimates of Maize Using Combined Methods Based on Multispectral UAV Observations and WOFOST Model.

Green leaf area index (LAI) is an important variable related to crop growth. Accurate and timely information on LAI is essential for developing suitable field management strategies to mitigate risk and boost yield. Several remote sensing (RS) based methods have been recently developed to estimate LAI at the regional scale. However, the performance of these methods tends to be affected by the quality of RS data, especially when time-series LAI are required. For crop LAI estimation, supplementary growth information from crop model is helpful to address this issue. In this study, we focus on the regional-scale LAI estimations of spring maize for the entire growth season. Using time-series multispectral RS data acquired by an unmanned aerial vehicle (UAV) and the World Food Studies (WOFOST) crop model, three methods were applied at different crop growth stages: empirical method using vegetation index (VI), data assimilation method and hybrid method. The VI-based method and assimilation method were used to generate time-series LAI estimations for the whole crop growth season. Then, a hybrid method specially for the late-stage LAI retrieval was developed by integrating WOFOST model and data assimilation. Using field-collected LAI data in Hongxing Farm in 2014, the performances of these three methods were evaluated. At the early stage, the VI-based method (R2 = 0.63, RMSE = 0.16, n = 36) achieved higher accuracy than the assimilation method (R2 = 0.54, RMSE = 0.52, n = 36), whereas at the mid stage, the assimilation method (R2 = 0.63, RMSE = 0.46, n = 28) showed higher accuracy than the VI-based method (R2 = 0.41, RMSE = 0.51, n = 28). At the late stage, the hybrid method yielded the highest accuracy (R2 = 0.63, RMSE = 0.46, n = 29), compared with the VI-based method (R2 = 0.19, RMSE = 0.43, n = 28) and the assimilation method (R2 = 0.20, RMSE = 0.44, n = 29). Based on the results above, we considered a combination of the three methods, i.e., the VI-based method for the early stage, the assimilation method for the mid stage, and the hybrid method for the late stage, as an ideal strategy for spring-maize LAI estimation for the entire growth season of 2014 in Hongxing Farm, and the accuracy of the combined method over the whole growth season is higher than that of any single method.

Correction: Zhao et al. Integration of Transcriptome, Proteome, and Metabolome Provides Insights into How Calcium Enhances the Mechanical Strength of Herbaceous Peony Inflorescence Stems. Cells 2019, 8, 102.

The authors wish to make the following changes to their paper [...].

Using spatio-temporal fusion of Landsat-8 and MODIS data to derive phenology, biomass and yield estimates for corn and soybean.

The Simple Algorithm for Yield estimates (SAFY) is a crop yield model that simulates crop growth and biomass accumulation at a daily time step. Parameters in the SAFY model can be determined from literature, in situ measurements, or optical remote sensing data through data assimilation. For effective determination of parameters, optical remote sensing data need to be acquired at high spatial and high temporal resolutions. However, this is challenging due to interference of cloud cover and rather long revisiting cycles of high resolution satellite sensors. Spatio-temporal fusion of multi-source remote sensing data may represent a feasible solution. Here, crop phenology-related parameters in the SAFY model were derived using an improved Two-Step Filtering (TSF) model from remote sensing data generated through spatio-temporal fusion of Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Remaining parameters were determined through an optimization procedure using the same dataset. The SAFY model was then used for dry aboveground biomass and yield estimation at a subfield scale for corn (Zea mays) and soybean (Glycine max). The results show that the improved TSF method is able to determine crop phenology stages with an error of <5 days. After calibration, the SAFY model can reproduce daily Green Leaf Area Index (GLAI) effectively throughout the growing season and estimate crop biomass and yield accurately at a subfield scale using three Landsat-8 and 10 MODIS images acquired for the season. This approach improves the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE), compared with the SAFY model without forcing the phenology-related parameters. The RMSE of yield estimation is 146.33 g/m2 for corn and 82.86 g/m2 for soybean. The proposed framework is applicable for local-scale or field-scale phenology detection and yield estimation.

Integration of Transcriptome, Proteome, and Metabolome Provides Insights into How Calcium Enhances the Mechanical Strength of Herbaceous Peony Inflorescence Stems.

Weak stem mechanical strength severely restrains cut flowers quality and stem weakness can be alleviated by calcium (Ca) treatment, but the mechanisms underlying Ca-mediated enhancement of stem mechanical strength remain largely unknown. In this study, we performed a comparative transcriptomic, proteomic, and metabolomic analysis of herbaceous peony (Paeonia lactiflora Pall.) inflorescence stems treated with nanometer Ca carbonate (Nano-CaCO3). In total, 2643 differentially expressed genes (DEGs) and 892 differentially expressed proteins (DEPs) were detected between the Control and nano-CaCO3 treatment. Among the 892 DEPs, 152 were coregulated at both the proteomic and transcriptomic levels, and 24 DEPs related to the secondary cell wall were involved in signal transduction, energy metabolism, carbohydrate metabolism and lignin biosynthesis, most of which were upregulated after nano-CaCO3 treatment during the development of inflorescence stems. Among these four pathways, numerous differentially expressed metabolites (DEMs) related to lignin biosynthesis were identified. Furthermore, structural observations revealed the thickening of the sclerenchyma cell walls, and the main wall constitutive component lignin accumulated significantly in response to nano-CaCO3 treatment, thereby indicating that Ca can enhance the mechanical strength of the inflorescence stems by increasing the lignin accumulation. These results provided insights into how Ca treatment enhances the mechanical strength of inflorescence stems in P. lactiflora.

Quantifying the biophysical effects of forests on local air temperature using a novel three-layered land surface energy balance model.

The well-documented energy balance dynamics within forest ecosystems are poorly implemented in studies of the biophysical effects of forests. This results in limitations to the accurate quantification of forest cooling/warming on local air temperature. Taking into consideration the forest air space, this study proposes a three-layered (canopy, forest air space and soil [CAS]) land surface energy balance model to simulate air temperature within forest spaces (Taf) and subsequently to evaluate its biophysical effects on forest cooling/warming, i.e., the air temperature gradient (∆Ta) between the Taf and air temperature of open spaces (Tao) (∆Ta = Taf - Tao). We test the model using field data for 23 sites across 10 cities worldwide; the model shows satisfactory performance with the test data. High-latitude forests show greater seasonal dynamics of ∆Ta, generating considerable cooling of local air temperatures in warm seasons but minimal cooling or even warming effects during cool seasons, while low-latitude tropical forests always exert cooling effects with less interannual variability. The interannual dynamics of ∆Ta are significantly related to the seasonality of solar geometry and canopy leaf phenology. The differences between forest canopy temperature (Tc) and Tao, which are the two most important terms attributed by the CAS model in impacting Taf, explain a large part of forest cooling and warming (May-July: R2 = 0.35; November-January: R2 = 0.51). The novel CAS model provides a feasible way to represent the energy balance within forest ecosystems and to assess its impacts on local air temperatures globally.

Capability of crop water content for revealing variability of winter wheat grain yield and soil moisture under limited irrigation.

Winter wheat (Triticum aestivum L.) is a major crop in the Guanzhong Plain, China. Understanding its water status is important for irrigation planning. A few crop water indicators, such as the leaf equivalent water thickness (EWT: g cm-2), leaf water content (LWC: %) and canopy water content (CWC: kg m-2), have been estimated using remote sensing techniques for a wide range of crops, yet their suitability and utility for revealing winter wheat growth and soil moisture status have not been well studied. To bridge this knowledge gap, field-scale irrigation experiments were conducted over two consecutive years (2014 and 2015) to investigate relationships of crop water content with soil moisture and grain yield, and to assess the performance of four spectral process methods for retrieving these three crop water indicators. The result revealed that the water indicators were more sensitive to soil moisture variation before the jointing stage. All three water indicators were significantly correlated with soil moisture during the reviving stage, and the correlations were stronger for leaf water indicators than that of the canopy water indicator at the jointing stage. No correlation was observed after the heading stage. All three water indicators showed good capabilities of revealing grain yield variability in jointing stage, with R2 up to 0.89. CWC had a consistent relationship with grain yield over different growing seasons, but the performances of EWT and LWC were growing-season specific. The partial least squares regression was the most accurate method for estimating LWC (R2=0.72; RMSE=3.6%) and comparable capability for EWT and CWC. Finally, the work highlights the usefulness of crop water indicators to assess crop growth, productivity, and soil water status and demonstrates the potential of various spectral processing methods for retrieving crop water contents from canopy reflectance spectrums.

Combination of transcriptome sequencing and iTRAQ proteome reveals the molecular mechanisms determining petal shape in herbaceous peony (Paeonia lactiflora Pall.).

The molecular mechanisms controlling petal shape in a herbaceous peony, Paeonia lactiflora Pall., a popular high-grade cut flower worldwide, remain unclear. Here, we selected inner and outer petals from P. lactiflora 'ZiFengyu' with an anemone type as the study object. Using transcriptome sequencing and isobaric tags for relative and absolute quantitation proteome, 979 differentially expressed genes and 266 differentially expressed proteins were detected within the inner and outer petals. Of these, the present study identified a key gene APETALA2 that regulates flower shape development. In addition, we obtained a 1935 bp full-length cDNA sequence of APETALA2 by rapid amplification of cDNA ends amplification. Through further validation using quantitative real-time polymerase chain reaction and Western blot analysis, APETALA2 showed a markedly higher expression in outer than that in inner petals. Therefore, the present study indicates that the increased expression of APETALA2 contributes to the formation of petals in P. lactiflora.