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Summary: The mechanisms that regulate the vast diversity of plant organ shapes such as the fruit remain to be fully elucidated. TONNEAU1 Recruiting Motif proteins (TRMs) have been implicated in the control of organ shapes in a number of plant species, including tomato. However, the role of many of them is unknown. TRMs interact with Ovate Family Proteins (OFPs) via the M8 domain. However, the in planta function of the TRM‐OFP interaction in regulating shape is unknown.We used CRISPR/Cas9 to generate knockout mutants in TRM proteins from different subclades and in‐frame mutants within the M8 domain to investigate their roles in organ shape and interactions with OFPs.Our findings indicate that TRMs impact organ shape along both the mediolateral and proximo‐distal axes of growth. Mutations in Sltrm3/4 and Sltrm5 act additively to rescue the elongated fruit phenotype of ovate/Slofp20 (o/s) to a round shape. Contrary, mutations in Sltrm19 and Sltrm17/20a result in fruit elongation and further enhance the obovoid phenotype in the o/s mutant.This study supports a combinatorial role of the TRM‐OFP regulon where OFPs and TRMs expressed throughout development have both redundant and opposing roles in regulating organ shape. [ABSTRACT FROM AUTHOR]

The sessile plants have evolved diverse intrinsic mechanisms to control their proper development under variable environments. In contrast to plastic vegetative development, reproductive traits like floral identity often show phenotypic robustness against environmental variations. However, it remains obscure about the molecular basis of this phenotypic robustness. In this study, we found that eg1 (extra glume1) mutants of rice (Oryza savita L.) showed floral phenotypic variations in different growth locations resulting in a breakdown of floral identity robustness. Physiological and biochemical analyses showed that EG1 encodes a predominantly mitochondria-localized functional lipase and functions in a high temperature-dependent manner. Furthermore, we found that numerous environmentally responsive genes including many floral identity genes are transcriptionally repressed in eg1 mutants and OsMADS1, OsMADS6 and OsG1 genetically act downstream of EG1 to maintain floral robustness. Collectively, our results demonstrate that EG1 promotes floral robustness against temperature fluctuation by safeguarding the expression of floral identify genes through a high temperature-dependent mitochondrial lipid pathway and uncovers a novel mechanistic insight into floral developmental control. [ABSTRACT FROM AUTHOR]

Using high-resolution remote sensing data to identify infected trees is an important method for controlling pine wilt disease (PWD). Currently, single-date image classification methods are widely used for PWD detection in pure stands of pine. However, they often yield false detections caused by deciduous trees, brown herbaceous, and sparsely vegetated regions in complex landscapes, resulting in low user accuracies. Due to the limitations on the bands of the high-resolution imagery, it is difficult to distinguish wilted pine trees from such easily confused objects when only using the optical spectral characteristics. This paper proposes a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The framework consisted of three parts, which represent the capture of spectral, temporal, and spatial features: (1) the Normalized Green–Red Difference Index (NGRDI) was calculated as a descriptor of canopy greenness; (2) two NGRDI images with similar dates in adjacent years were contrasted to obtain a bitemporal change index that represents the temporal behaviors of typical cover types; and (3) a spatial enhancement was performed on the change index using a convolution kernel matching the spatial patterns of PWD. Finally, a set of criteria based on the above features were established to extract the wilted pine trees. The results showed that the proposed method effectively distinguishes wilted pine trees from other easily confused objects. Compared with single-date image classification, the proposed method significantly improved user's accuracy (81.2% vs. 67.7%) while maintaining the same level of producer's accuracy (84.7% vs. 82.6%). [ABSTRACT FROM AUTHOR]

Summary Climate warming poses a significant threat to global crop production and food security. However, our understanding of the molecular mechanisms governing thermoresponsive development in crops remains limited. Here we report that the auxiliary subunit of N‐terminal acetyltransferase A (NatA) in rice OsNAA15 is a prerequisite for rice thermoresponsive growth. OsNAA15 produces two isoforms OsNAA15.1 and OsNAA15.2, via temperature‐dependent alternative splicing. Among the two, OsNAA15.1 is more likely to form a stable and functional NatA complex with the potential catalytic subunit OsNAA10, leading to a thermoresponsive N‐terminal acetylome. Intriguingly, while OsNAA15.1 promotes plant growth under elevated temperatures, OsNAA15.2 exhibits an inhibitory effect. We identified two glycolate oxidases (GLO1/5) as major substrates from the thermoresponsive acetylome. These enzymes are involved in hydrogen peroxide (H2O2) biosynthesis via glycolate oxidation. N‐terminally acetylated GLO1/5 undergo their degradation through the ubiquitin‐proteasome system. This leads to reduced reactive oxygen species (ROS) production, thereby promoting plant growth, particularly under high ambient temperatures. Conclusively, our findings highlight the pivotal role of N‐terminal acetylation in orchestrating the glycolate‐mediated ROS homeostasis to facilitate thermoresponsive growth in rice. [ABSTRACT FROM AUTHOR]

Electrohydrodynamic (EHD) jet printing is a promising direct writing method to produce micro- and nano-scale dots due to its easy manipulation, high resolution, and low cost. The effect of printing conditions on the diameter of printed dots was widely studied by both experiments and simulations. However, positional precision is also important for EHD printing. There is no published work on numerical simulation for the trajectory analysis of the ejected droplet. In the present work, a finite element model was established to investigate the droplet trajectory. The influencing factors, such as nozzle size, nozzle angle, applied voltage, ink density, and charge number in one droplet, were considered during numerical simulation. The influence of influencing factors on the electric intensity, droplet speed, and deposition direction was analyzed. The proposed simulation model provides a useful tool to analyze the droplet formation process and optimize the printing parameters to improve the positional precision of EHD printing. [ABSTRACT FROM AUTHOR]

EHD (Electrohydrodynamic) printing is a photomask-free and direct writing technique for alternative fabrication of high resolution micro- and nano-structures with low cost and simple equipment. It is critical to understand the motion trajectory of the ejected droplet under electric filed so as to precisely control the printing process. However, there is no work which analyzed this issue previously. Thus in this paper, a finite element model was established to study the electric field distribution near the nozzle and analyze the motion trajectory of the ejected droplet during EHD printing. By using established finite element method, the electric field distribution in the nozzle and near the nozzle tip was investigated. The influence of printing distance and applied potential on the maximum electric field intensity was analyzed. Based on the electric field distribution study, the droplet trajectory analysis was carried out to confirm the motion velocity and direction of the ejected droplets. [ABSTRACT FROM AUTHOR]

Heavy metal stress in crops is characterized by stability in space and time, which differs from other stressors that are typically more transient (e.g., drought, pests/diseases, and mismanagement). The objective of this study is to assess regional heavy metal stress in rice by integrating physiological function variability with spatio-temporal stability based on multi-temporal thermal infrared (TIR) remote sensing images. The field in which the experiment was conducted is located in Zhuzhou City, Hunan Province, China. HJ-1B images and in-situ measured data were collected from rice growing in heavy metal contaminated soils. A stress index ( S I ) was devised as an indicator for the degree of heavy metal stress of the rice in different growth stages, and a time-spectrum feature space (TSFS) model was used to determine rice heavy metal stress levels. The results indicate that (i) S I is a good indicator of rice damage caused by heavy metal stress. Minimum values of S I occur in rice subject to high pollution, followed by larger S I with medium pollution and maximum S I for low pollution, for the same growth stage. (ii) S I shows some variation for different growth stages of rice, and the minimum S I occurs at the flowering stage. (iii) The TSFS model is successful at identifying rice heavy metal stress, and stress levels in rice stabilized regardless of the model being applied in the two different years. This study suggests that regional heavy metal stress in crops can be accurately detected using TIR technology, if a sensitive indicator of crop physiological function impairment is used and an effective model is selected. A combination of spectrum and spatio-temporal information appears to be a very promising method for monitoring crops with various stressors. [ABSTRACT FROM AUTHOR]

Coal fires pose great threats to valuable energy resources, the ecological environment, and human safety. They are one of the most persistent fires on the Earth, which can burn for an extremely long-term period from decades to hundreds or even thousands of years. Remote sensing detection of coal fires is of significance for mitigating coal fire hazards. Nevertheless, short-term or temporal discrete land surface temperature (LST) data have limited capability in characterizing the persistent coal fire. This study proposed a methodology to monitor persistent coal fires using long-term Landsat thermal images and further to analyze spatiotemporal dynamics of coal fires. A total of 1118 high-quality Landsat images (each image containing $446\times446$ pixels) spanning 35 years from 1986 to 2020 in the Wuda coalfield area (China) were processed to retrieve the LST. LST time series of each pixel was decomposed into the seasonal, trend, and remainder components. Coal fire areas were demarcated by using the range of the trend components. To trace the trend and change point of the LST time series, the Mann–Kendall test was applied to the trend components, and the Pettitt test was employed to the original time series vectors of those pixels located in the coal fire areas, respectively. The random sample consensus algorithm was utilized to identify the background temperature (inliers) and high temperature (outliers) and, thus, judge the coal fire burning period, and the symbolic aggregate approximation algorithm was used to evaluate the robustness of the judgment. The calibration was conducted according to the filed surveys, obtaining spatiotemporal 3-D coal fire dynamics. The proposed methodology was testified by comparisons with fieldwork and regional anomaly extractor algorithm, demonstrating good performance in comprehensive monitoring of persistent coal fires. [ABSTRACT FROM AUTHOR]

• Coupled maize model is a 4D maize growth model based on growing degree days. • Coupled maize model simulates and visualizes maize canopy on multiple levels. • Improved thermal-driven crop growth model and plant functional-structural algorithm. • Coupled maize model has high accuracy through multi-seasonal dataset validation. Crop canopy parameters are critical for environmental remote sensing, describing crop phenotypes, and ensuring food security. Evaluating the effect of temperature on crop growth is crucial for estimating crop canopy parameters. However, existing crop growth and plant functional-structural models cannot simultaneously model temperature responses, perform accurate dynamic simulations, and provide multi-scale computer visualizations. This limitation has hindered the application of structural models of maize plants for use in 3D radiative transfer models, crop structure evaluations, and crop phenotype descriptions. We improve the leaf/organ-level thermal-driven crop growth model (MAIZSIM) and the plant functional-structural algorithm. To address these limitations, we propose the coupled maize model, a four-dimensional (4D) growth model based on growing degree days. This model can simulate and visualize the structural parameters of the maize canopy at the organ, plant, seasonal, and population levels. The model outputs three-dimensional (3D) predictions of the maize structure (file format.obj), enabling editing and 3D visualizations. We use maize datasets from multiple phenological periods to test the proposed model's accuracy and stability in simulating the canopy parameters at multiple levels. The results show that the normalized root mean square errors (NRMSEs) between the simulated and measured maize leaf size, area, leaf node height, and vein curve derived from the coupled maize model are below 0.1, demonstrating the model's high accuracy. [ABSTRACT FROM AUTHOR]

• Pixels with greater subpixel percent tree cover generally had earlier greenup dates (GUDs). • A large area of significantly earlier GUD was observed across the Hulunbuir forest-grassland ecotone. • Increases in spring temperature and percent tree cover contributed to the earlier trends in GUD. Changes in climate and land cover are potential drivers of land surface phenology changes. Here, we investigate whether subpixel percent tree cover (PTC) change is an important driver of trends in satellite derived vegetation spring greenup date (GUD) across the Hulunbuir temperate forest-grassland ecotone in northeastern China. GUD was estimated using the MODIS-derived enhanced vegetation index time series during 2001–2020 with a spatial resolution of 500 m. To understand the influential mechanisms of PTC on GUD, we examined relationships between the spatial variations in GUD and PTC at multiple spatio-temporal extents. Forested pixels with greater PTC were found to have generally earlier GUDs for all forest types. The GUD of forests was also generally earlier than that of grassland. On the other hand, we observed approximately 23.7% and 1.2% significantly earlier and later trends in GUD across the region, respectively. Meanwhile, widespread increases in preseason land surface temperature (LST) and PTC were detected. Both increases in LST and PTC contributed to the earlier GUD in the forested region. Specifically, we found negative correlations (Spearman correlation coefficient -0.17 to -0.55) between the change slopes of GUD and PTC in every forest and grassland type. The results highlight the important impacts of subpixel PTC on GUD variations, and improve the understanding of ecosystem changes under the effects of climate and human activities (e.g., afforestation) over the Hulunbuir temperate forest-grassland ecotone. [ABSTRACT FROM AUTHOR]

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management. [ABSTRACT FROM AUTHOR]

• Local space–time Moran's I from NDVI can monitor forest disturbance and recovery. • Elasticity, malleability and trend were selected as forest resilience indicators. • Dense satellite images are successful at deriving forest resilience. Resilience is the capacity of an ecosystem to respond and recover from damage or stress. It can inherently exhibit the cycle and feedback of the disturbed ecosystem recovery process for a specified period. The current availability of dense and consistent time series of satellite images holds the promise of monitoring forest resilience. The aim of this study is to establish forest resilience indicators using dense Landsat time series and assess forest resilience in response to the maximum disturbance magnitude in the hilly red soil region, Hengyang Basin, Southern China. To achieve this, Landsat images of the study area from 1987 to 2017 were collected. Normalized Difference Vegetation Index (NDVI), i.e., proxy of forest green characteristics, number of patch (NP), average patch size (PS), patch perimeter-area ratio (PPAR) and aggregation index (AI), i.e., proxies of forest landscape metrics were calculated from Landsat images. And then elasticity (i.e., the time and rate of recovery), malleability (i.e., degree of deviation from an initial state) and trend (i.e., the pattern of change) as the indicators of forest resilience were constructed. The local space–time Moran's I (STI) based on NDVI residual space–time Moran's I (STI) was employed to characterize the forest disturbance and recovery process. The results revealed the following. Firstly, the STI calculated from dense time series NDVI residuals are successful at monitoring the forest disturbance recovery process, regardless of whether changes were dramatic or subtle. Secondly, the most forest disturbances (i.e., > 75%) occurred in the late 1980s and early 1990s. NDVI and landscape metrics also differed in their response to disturbances; NDVI, PPAR and AI are more malleable to disturbance than PS and NP are. Finally, approximately 40% of the disturbed forest had the strong elasticity with a short recovery time (i.e., a year). We conclude that measuring forest resilience via vegetation greenness and landscape metrics using dense time series satellite images is practical as an operational tool for policy makers, landowners, and national park managers. Moreover, insights into ecological dynamics are emerging from capturing the process showing the difference both before and after the change. [ABSTRACT FROM AUTHOR]

Land cover changes, especially excessive economic forest plantations, have significantly threatened the ecological security of West Dongting Lake wetland in China. This work aimed to investigate the spatiotemporal dynamics of forests in the West Dongting Lake region from 2000 to 2018 using a reconstructed monthly Landsat NDVI time series. The multi-type forest changes, including conversion from forest to another land cover category, conversion from another land cover category to forest, and conversion from forest to forest (such as flooding and replantation post-deforestation), and land cover categories before and after change were effectively detected by integrating Breaks For Additive Seasonal and Trend (BFAST) and random forest algorithms with the monthly NDVI time series, with an overall accuracy of 87.8%. On the basis of focusing on all the forest regions extracted through creating a forest mask for each image in time series and merging these to produce an 'anytime' forest mask, the spatiotemporal dynamics of forest were analyzed on the basis of the acquired information of multi-type forest changes and classification. The forests are principally distributed in the core zone of West Donting Lake surrounding the water body and the southwestern mountains. The forest changes in the core zone and low elevation region are prevalent and frequent. The variation of forest areas in West Dongting Lake experienced three steps: rapid expansion of forest plantation from 2000 to 2005, relatively steady from 2006 to 2011, and continuous decline since 2011, mainly caused by anthropogenic factors, such as government policies and economic profits. This study demonstrated the applicability of the integrated BFAST method to detect multi-type forest changes by using dense Landsat time series in the subtropical wetland ecosystem with low data availability. [ABSTRACT FROM AUTHOR]

• Establishing indicators to describe landscape structure patterns and processes. • Explaining the ecological significance of spatio-temporal variation indicators. • Reasoning landscape structure variation in Changting County. Terrestrial ecosystems are constantly changing at various spatial and temporal scales due to natural and/or anthropogenic factors. It is therefore of great interest to develop effective indicators based on spatio-temporal variations to detect, map, and monitor ecosystem changes on the continental and global scales. In this study, spatio-temporal variation indicators were established to characterize landscape structure changes based on temporal analysis combined with spatial information obtained using dense Landsat time series in Changting County, Fujian, Province, China. All available Landsat images from 1987 to 2017 were collected. The fractal dimension (FD) of the spatial neighbor normalized difference vegetation index (NDVI) derived from the Landsat images was calculated. Four change patterns (stable, increasing, decreasing, and wave) were explored using the ordinary least-squares method between abrupt change points performed by a Mann–Kendall test on the FD time series to characterize the spatio-temporal dynamics. The results showed that 49.98% of the landscape structure in the study area decreased over the 31-year study period. The forest and water were dominated by decreasing patterns (67.00% and 56.27%); the urban land and cropland were dominated by wave patterns (50.07% and 50.95%); bare land regions were dominated by wave patterns and increasing patterns (50.52% and 31.38%). Wide and discontinuous landscape structures with high heterogeneity were replaced by landscape patches. The regional landscape structure heterogeneity increased in the bare land regions and other homogeneous landscape structures, while the decreases may be coupled with internal landscape structure changes. It was concluded that the spatial and temporal variations of the change patterns and change processes determined based on the FD of the NDVI in the spatial neighbor region are systematic indicators and can be used to detect landscape structure changes successfully and provide insights into dynamic trends. [ABSTRACT FROM AUTHOR]