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It is important to understand temporal and spatial variations in the structure and photosynthetic capacity of tropical rainforests in a world of changing climate, increased disturbances and human appropriation. The equatorial rainforests of Central Africa are the second largest and least disturbed of the biodiversly-rich and highly productive rainforests on Earth. Currently, there is a dearth of knowledge about the phenological behavior and long-term changes that these forests are experiencing. Thus, this study reports on leaf area seasonality and its time trend over the past two decades as assessed from multiple remotely sensed datasets. Seasonal variations of leaf area in Congolese forests derived from MODIS data co-vary with the bimodal precipitation pattern in this region, with higher values during the wet season. Independent observational evidence derived from MISR and EPIC sensors in the form of angular reflectance signatures further corroborate this seasonal behavior of leaf area. The bimodal patterns vary latitudinally within this large region. Two sub-seasonal cycles, each consisting of a dry and wet season, could be discerned clearly. These exhibit different sensitivities to changes in precipitation. Contrary to a previous published report, no widespread decline in leaf area was detected across the entire extent of the Congolese rainforests over the past two decades with the latest MODIS Collection 6 dataset. Long-term precipitation decline did occur in some localized areas, but these had minimal impacts on leaf area, as inferred from MODIS and MISR multi-angle observations.

SUMMARY: Long non‐coding RNAs (lncRNAs) play crucial roles in various biological processes in plants. However, the functional mechanism of lncRNAs in fruit ripening, particularly the transition from unripe to ripe stages, remains elusive. One such lncRNA1840, reported by our group, was found to have important role in tomato fruit ripening. In the present study, we gain insight into its functional role in fruit ripening. CRISPR‐Cas9 mediated lncRNA1840 mutants caused the delayed tomato fruit ripening. Notably, loss function of lncRNA1840 did not directly impact ethylene signaling but rather delay ethylene synthesis. Transcriptomic analysis revealed differences in the expression of ripening related genes in lncRNA1840 mutants, suggesting that it is involved in gene regulation of fruit ripening. We used Chromatin Isolation by RNA Purification (ChIRP)‐Seq to identify lncRNA1840 binding sites on chromatin. ChIRP‐seq suggested that lncRNA1840 had occupancy on 40 genes, but none of them is differentially expressed genes in transcriptomic analysis, which indicated lncRNA1840 might indirectly modulate the gene expression. ChIRP‐mass spectrometry analysis identified potential protein interactors of lncRNA1840, Pre‐mRNA processing splicing factor 8, highlighting its involvement in post‐transcriptional regulatory pathways. In summary, lncRNA1840 is key player in tomato plant growth and fruit ripening, with multifaceted roles in gene expression and regulatory networks. Significance Statement: Long non‐coding RNAs (lncRNAs) play important roles in various biological processes in plants, but the mechanism of action of lncRNAs during fruit ripening remains unexplored. lncRNA1840 was identified in tomato, which confirmed that it affected the ripening process of tomato fruit and achieved post‐transcriptional regulation by participating in alternative splicing, which improved the regulatory network of tomato fruit ripening and filled the gap of lncRNA in the field of fruit ripening. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing technology is an advanced and powerful tool that enables fine identification of the numerous soil reflectance spectrum characteristics. Heavy metal(loid)s (HMs) are the primary pollutants affecting the soil biodiversity and ecosystem services. Estimating HMs' concentrations in soils using hyperspectral data is an effective method but is challenging due to the effects of varied soil properties and measurement-related errors inflicted by atmospheric effects. This study focused on typical mining areas in the Fenwei Plain (FWP), China. Soil-related data were collected by leveraging airborne- and ground-based integrated remote sensing observations. The concentrations of eight HMs, namely copper (Cu), lead (Pb), zinc (Zn), nickel (Ni), chromium (Cr), cadmium (Cd), arsenic (As), and mercury (Hg), were measured by laboratory analysis from 100 in situ soil samples. Soil reflectance spectra were processed based on resampling and envelope methods. The combination datasets of the concentrations and optimal soil reflectance spectra were used to build the soil-related parameter retrieval models using three machine learning (ML) methods, and the feasibility of applying the high-performance retrieval model to estimate the HM concentrations in mining areas was evaluated and explored. Spectral analysis results show that four hundred and twenty-eight bands of five wavelength ranges are of high quality and obviously demonstrate the spectral characteristics selected to build the soil-related parameter models. The evaluation results of eight combination data subsets and three methods show that the preprocessing of spectral data (ground- and airborne-based reflectance) and soil samples with the random forest (RF) method can obtain higher accuracy than support vector machine (SVM) and partial least squares (PLS) methods, denoted as the AER-ACS-RF and GER-GCS-RF models (the average RMSE values of eight HMs were 2.61 and 2.53 mg/kg, respectively). The highest R2 values were observed in Cd and As, with an equal value of 0.98, followed by that of Pb (R2 = 0.97). The relative prediction deviation (RPD) values of Cu and AS were greater than 1.9. Moreover, the airborne-based AER-ACS-RF model presents a good mapping effect about the concentrations (mg/kg) of eight HMs in mining areas, ranging from 21.65 to 31.25 (Cu), 16.38 to 30.45 (Pb), 62.02 to 109.48 (Zn), 23.33 to 32.47 (Ni), 49.81 to 66.56 (Cr), 0.09 to 0.23 (Cd), 7.31 to 12.24 (As), and 0.03 to 0.17 (Hg), respectively. [ABSTRACT FROM AUTHOR]

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) onboard the International Space Station (ISS) provides standard land surface temperature (LST) products to meet community concerns on water stress and evapotranspiration. In 2019, an anomaly on the mass storage unit (MSU) changed the data acquisition mode of ECOSTRESS to a direct streaming one, eliminating three bands centered at 1.60, 8.29, and $9.20~\mu \text{m}$. This letter analyzes the feasibility of retrieving LST from ECOSTRESS data using split-window (SW) techniques which need only two thermal infrared (TIR) bands to serve as an alternative to produce LST products. Three different spilt-window algorithms have been developed and analyzed for all possible TIR band combinations using the simulated top-of-atmosphere (TOA) radiance dataset. Besides, the selected SW algorithms were validated using the intercomparison method with ECOSTRESS LST product data and the temperature-based method with surface radiation budget (SURFRAD) ground-based measurements. The result showed that SW algorithms provided higher accuracies than the temperature-emissivity separation (TES) algorithm, with the main advantage that the atmospheric profile data is not demanded prior. [ABSTRACT FROM AUTHOR]

The land surface temperature (LST) of urban is a key factor in the field of urban environmental monitoring, and thermal infrared (TIR) remote sensing is an efficient method to obtain it. An important assumption of the traditional thermal radiance transfer model is that the land surface is flat, which has now proven difficult to hold under the urban landscape. Most of the existing urban thermal radiance transfer models have been developed for remote sensing images with a spatial resolution of tens of meters. Currently, airborne TIR sensors have the observation capability to acquire remote sensing images with an ultrahigh spatial resolution (1 cm–1 m), and the model needs to be improved. This letter proposed a new ultrahigh spatial resolution urban thermal radiance transfer model (UHURT) after analyzing the transfer processes within the urban canopy at ultrahigh spatial resolution. Various radiance components, the emitted radiance, reflected atmospheric downward radiance, and adjacent radiance, were modeled separately. The results of the traditional model and the UHURT model were compared with those of a ray-tracing computer simulation model, which showed that the new model successfully quantifies the multiple scattering and adjacent effects and obtained images closer to the computer simulation images. Besides, the LST retrieval of the computer-simulated images was performed using the traditional model and the UHURT model, and the proposed model successfully reduced the errors of the retrieval results and weakened the spatial correlation between the residual distribution and the geometric characteristics of the urban landscape. [ABSTRACT FROM AUTHOR]

SUMMARY: Fruit size and shape are controlled by genes expressed during the early developmental stages of fruit. Although the function of ASYMMETRIC LEAVES 2 (AS2) in promoting leaf adaxial cell fates has been well characterized in Arabidopsis thaliana, the molecular mechanisms conferring freshy fruit development as a spatial–temporal expression gene in tomato pericarp remain unclear. In the present study, we verified the transcription of SlAS2 and SlAS2L, two homologs of AS2, in the pericarp during early fruit development. Disruption of SlAS2 or SlAS2L caused a significant decrease in pericarp thickness as a result of a reduction in the number of pericarp cell layers and cell area, leading to smaller tomato fruit size, which revealed their critical roles in tomato fruit development. In addition, leaves and stamens exhibited severe morphological defects in slas2 and slas2l single mutants, as well as in the double mutants. These results demonstrated the redundant and pleiotropic functions of SlAS2 and SlAS2L in tomato fruit development. Yeast two‐hybrid and split‐luciferase complementation assays showed that both SlAS2 and SlAS2L physically interact with SlAS1. Molecular analyses further indicated that SlAS2 and SlAS2L regulate various downstream genes in leaf and fruit development, and that some genes participating in the regulation of cell division and cell differentiation in the tomato pericarp are affected by these genes. Our findings demonstrate that SlAS2 and SlAS2L are vital transcription factors required for tomato fruit development. Significance Statement: Understanding early fruit development in tomato is critical since the size and shape of a fruit is largely decided in the early stages. Our previous work identified hundreds of transcription factors specifically expressed in the pericarp of 0–5 days post anthesis fruits in tomato that are likely involved in this complex process. ASYMMETRIC LEAVES 2 (AS2) belongs to the LATERAL ORGAN BOUNDARIES family, and its role in promoting leaf adaxial cell fates in Arabidopsis has been well characterized. SlAS2 and SlAS2L, two homologs of AS2 in tomato, are specifically expressed in the ovary wall/pericarp at early fruit developmental stage, but their functions in fleshy fruit development are still unclear. In the present study, we found that knockout mutations in SlAS2 or SlAS2L caused significantly reduced pericarp thickness, leading to smaller mature fruit size. Histocytological observation of the pericarp revealed that disruption of SlAS2 or SlAS2L inhibited both cell division and expansion, leading to the observed thinner pericarp. Additionally, leaf and stamen defects in single and double mutants suggested that SlAS2 and SlAS2L have redundant functions in plant development. Both SlAS2 and SlAS2L interact with SlAS1 in vitro, which is consistent with Arabidopsis AS2. Molecular analyses revealed that genes regulated by SlAS2 and SlAS2L in leaf and fruit development are different, and were related to cell division and differentiation processes during tomato pericarp development. Our results confirm the vital roles of SlAS2 and SlAS2L in regulating tomato fruit development. [ABSTRACT FROM AUTHOR]