A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

The 43-year (1978-2020) global 9km remotely sensed soil moisture product

The 43-year global 9km remotely sensed soil moisture product is estimated by the fusion of two kinds of microwave soil moisture products using the spatial temporal fusion model (STFM). One product is the Climate Change Initiative (CCI) 0.25° passive soil moisture product in version 6.1. European Space Agency (ESA) integrates multi-source passive microwave observation data from 1978 to 2020 for CCI 0.25° passive soil moisture estimation. Another product is the Soil moisture Active and Passive (SMAP) 9km soil moisture product in version 3. The SMAP 9km data is less than three months (from April 13–July 7) as the failure of SMAP radar. The soil moisture STFM takes the known CCI 0.25°data and SMAP 9km data at the same date as the reference, and then to fuse other date CCI soil moisture for the unknown 9km soil moisture estimation at the date. The estimated 9km soil moisture covers from 1978 to 2020 in global scale, one image per day, 15402 in total and the data volume up to 13.6 G. The estimated long time series 9km soil moisture will play an important role in the researches and applications at regional scale

Multicomponent Reductive Coupling for Selective Access to Functional γ-Lactams by a Single-Atom Cobalt Catalyst

International audienceDespite their significant importance to numerous fields, the difficulties in direct and diverse synthesis of α-hydroxy-γ-lactams pose substantial obstacles to their practical applications. Here, we designed a nitrogen and TiO2 co-doped graphitic carbon-supported material with atomically dispersed cobalt sites (CoSA-N/NC-TiO2), which was successfully applied as a multifunctional catalyst to establish a general method for direct construction of α-hydroxy-γ-lactams from cheap and abundant nitro(hetero)arenes, aldehydes, and H2O with alkynoates. The striking features of operational simplicity, broad substrate and functionality compatibility (>100 examples), high step and atom efficiency, good selectivity, and exceptional catalyst reusability highlight the practicality of this new catalytic transformation. Mechanistic studies reveal that the active CoN4 species and the dopants exhibit a synergistic effect on the formation of key acid-masked nitrones; their subsequent nucleophilic addition to the alkynoates followed by successive reduction, alkenyl hydration, and intramolecular ester ammonolysis delivers the desired products. In this work, the concept of reduction interruption leading to new reaction route will open a door to further develop useful transformations by rational catalyst design

Reductive Coupling of Nitroarenes and HCHO for General Synthesis of Functional Ethane-1,2-diamines by a Cobalt Single-Atom Catalyst

International audienceDespite the extensive applications, selective and diverse access to N,N’-diarylethane-1,2-diamines remains, to date, a challenge. Here, by developing a bifunctional cobalt single-atom catalyst (Co(SA)-N/NC), we present a general method for direct synthesis of such compounds via selective reductive coupling of cheap and abundant nitroarenes and formaldehyde, featuring good substrate and functionality compatibility, an easily accessible base metal catalyst with excellent reusability, and high step and atom efficiency. Mechanistic studies reveal that the N-anchored cobalt single atoms (CoN(4)) serve as the catalytically active sites for the reduction processes, the N-doped carbon support enriches the HCHO to timely trap the in situ formed hydroxyamines and affords the requisite nitrones under weak alkaline conditions, and the subsequent inverse electron demand 1,3-dipolar cycloaddition of the nitrones and imines followed by hydrodeoxygenation of the cycloadducts furnishes the products. In this work, the concept of catalyst-controlled nitroarene reduction to in situ create specific building blocks is anticipated to develop more useful chemical transformations