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The carbon dioxide (CO2) fertilization effect has captured worldwide attention, owing to its tremendous potential to challenge existing predictions of future climate. However, quantifying the CO2 fertilization effect (CFE) has proven to be challenging, given that it is closely entangled with other ecological and environmental processes. Recent years have witnessed significant advances with breakthroughs using theoretical methods to infer the CFE from eddy covariance tower measurements. Building on earlier findings, this study presents an innovative approach that utilizes explainable machine-learning techniques—describing the partial dependence of the response variable to each explanatory variable—to quantify the global CFE from remote-sensing platforms with an averaged $R ^{2}$ of 0.85. This study provides the first data-driven evidence of the global CFE and confirms the potential for extrapolation to the globe. The findings suggest that: 1) the employment of satellite vegetation proxies contributed to more than 50% of the fitting of gross primary productivity (GPP) and 2) the manifestation of the CO2 fertilization impact demonstrated heterogeneity among various types of ecosystems, and in some cases, an adverse effect was detected in broadleaf forests. Our results have significant implications for the preservation and protection of terrestrial ecosystems, particularly for a carbon-neutral future. This study, therefore, provides a valuable contribution to the growing body of knowledge in this area and highlights the potential of innovative analytical techniques to address complex ecological challenges. [ABSTRACT FROM AUTHOR]

Synthetic-aperture radar images of a forest site near Manley Hot Springs (64°N, 151°W), Alaska, were collected between August 1991 and December 1991, day and night, every 3 days, at C-band frequency (λ = 5.7 cm), vertical receive and transmit polarization, by the European Space Agency first Remote Sensing Satellite, ERS-1. During the same period, air and soil temperatures and dielectric and gravimetric moisture properties of the forest canopy and forest floor were monitored in three forest stands dominated, respectively, by black spruce (Picea mariana), white spruce (Picea glauca), and balsam poplar (Populus balsamifera). The calibrated ERS-1 radar backscatter values, σ°, of the forest stands are shown to exhibit a pronounced temporal pattern, with little separability between tree species. The largest change in σ°, α 3-dB decrease almost independent of tree species, is observed in early winter when the soil and vegetation freeze. In the summer, temporal fluctuations in σ° are about 1-2 dB in magnitude, depending on tree species. Diurnal variations in σ° are as large as 2 dB during fall freeze-up, and less than 1 dB in summer and winter. These temporal variations in radar backscatter from the forest are interpreted using the MIMICS radar backscatter model and the in situ surface observations as due to changes in the dielectric properties of the forest floor and forest canopy induced by precipitation (summer), drought (fall), and freezing (fall-winter) events. In winter, σ° increases across the entire landscape, probably because of volume scattering from large depth hoar ice crystals forming in the snow pack. © 1994.

Soil moisture (SM) and land surface temperature (LST) are entangled, and the retrieval of one of them requires a priori specification of the other one. Due to insufficient observational information, retrieval of LST and SM from passive microwave remote sensing data is often ill-posed, and the retrieval accuracy needs to be improved. In this study, a novel fully-coupled paradigm is developed to robustly retrieve SM and LST from passive microwave data, which integrates deep learning, physical methods, and statistical methods. The key condition of the general paradigm proposed by us is that the output parameters of deep learning can be uniquely determined by the input parameters theoretically through a certain mathematical equation. Firstly, the physical method is deduced based on the energy radiation balance equation. The nine unknowns require the brightness temperatures of nine channels to construct nine equations, and the solutions of the physical method equations are obtained by model simulation. Based on the derivation of the physical method, the solution of the statistical method is constructed using multi-source data. Secondly, the solutions of physical and statistical methods constitute the training and test data of deep learning, which is used to obtain the solution curve of physical and statistical methods. The retrieval accuracy of LST and SM is greatly improved by smartly utilizing the mutual prior knowledge of SM and LST and cross iterative optimization calculations. Finally, validation indicates that the mean absolute error of the retrieved SM and LST data are 0.027 m3/m3 and 1.38 K, respectively, at an incidence angle of 0–65°. A model-data-knowledge-driven and deep learning method can overcome the shortcomings of traditional methods and provide a paradigm for retrieval of other geophysical variables. The proposed paradigm not only has physical meaning, but also makes deep learning physically interpretable, which is a milestone in the retrieval of geophysical remote sensing parameters based on artificial intelligence technology. [ABSTRACT FROM AUTHOR]

Accurate predictions of sea surface temperature (SST) are crucial due to the significant impact of SST on the global ocean-atmospheric system and its potential to trigger extreme weather events. Many existing machine-learning-based SST predictions adapt the traditional iterative point-wise prediction mechanism, whose predicting horizons and accuracy are limited owing to the high sensitivity to cumulative errors during iterative predictions. Therefore, this paper proposes a novel granulation-based long short-term memory (LSTM)-random forest (RF) combination model that can fully capture the feature dependencies involved in the fluctuation of SST sequences, reduce the cumulative error in the iteration process, and extend the prediction horizons, which includes two sub-models (adaptive granulation model and hybrid prediction model). They can restack the one-dimensional SST time-series into multidimensional feature variables, and achieve a strong forecasting ability. The analysis shows that the proposed model can achieve more accurate prediction-hours in nearly all prediction ranges from 1 to 125 h. The average prediction error of the proposed model in 25–125 h is 0.07 K, similar to that (0.067 K) in the first 24 h, which exhibits a high generalization performance and robustness and isthus a promising platform for the medium- and long-term forecasting of hourly SSTs. [ABSTRACT FROM AUTHOR]