Accurate observation of global in-cloud wind fields is essential for advancing the understanding of the atmospheric water and energy cycles and for improving the initialization of cloud-resolving numerical weather prediction and general circulation models. In recent years, growing attention from the remote sensing community has driven substantial progress in observational technologies. Among them, spaceborne Doppler microwave radar has emerged as the only viable remote sensing technique capable of penetrating deeply into cloud systems, enabling comprehensive global-scale in-cloud wind measurements. This article presents a comprehensive synthesis of recent developments in spaceborne Doppler radar systems for global in-cloud wind field retrieval. It systematically reviews Doppler velocity estimation techniques, onboard antenna scanning strategies, and wind field retrieval algorithms under the constraints of spaceborne operation. Building upon current technological capabilities and emerging trends, this article highlights several promising research directions, including the implementation of mission-level integrated phased-array multibeam radar simulators to support next-generation global in-cloud wind measurement missions, the execution of airborne prototype system performance evaluation campaigns, the development of high-resolution wind field retrieval algorithms increasingly enhanced by artificial intelligence (AI) techniques, and observing system simulation experiment (OSSE)-based forecast-impact assessment to quantify the potential value of spaceborne in-cloud Doppler wind observations for numerical weather prediction. Together, these directions provide technical guidance for the design of future spaceborne Doppler radar missions.
Benchmark datasets have been the backbone of hyperspectral image classification research, with classics such as Indian Pines (IP), Salinas (SA), Pavia (PA), Pavia University (PU), Pavia Center (PC), Kennedy Space Center (KSC), and Botswana (BO) providing the foundation for decades of progress. These datasets have supported numerous algorithmic advances, yet the rapid evolution of deep learning now exposes their shortcomings: they are limited in scale, lack diversity, and have been reused to the point of saturation. As a result, new methods are often judged on benchmarks that no longer capture the complexity of real-world scenarios, leading to inconsistent evaluations and the illusion of steady progress. This article offers a critical review of hyperspectral benchmarking, tracing the field’s reliance on classical datasets and revealing how their constraints shape, and sometimes distort, methodological outcomes. Through experimental analyses with state-of-the-art deep learning models, we illustrate how existing practices can bias comparisons and obscure meaningful conclusions. Our aim is not to rank methods but to demonstrate the urgent need for larger, more representative, and rigorously designed datasets. We close with a forward-looking perspective: to sustain meaningful progress, the hyperspectral remote sensing community must embrace modern benchmarks that reflect real-world diversity and align with emerging learning paradigms. By linking the historical trajectory of datasets to current methodological challenges, including issues of generalization, information leakage, and benchmark reliability, this review article provides timely guidance for building the next generation of benchmarks and ensuring that future research delivers reliable advances.
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