In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes and data with limited annotations. This paper reviews how AI is being used to analyze extreme climate events (like floods, droughts, wildfires, and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating real-time information, and deploying understandable models, all crucial steps for gaining stakeholder trust and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy to enhance disaster readiness and risk reduction.

In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in remote sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the used explainable AI methods and their objectives, findings, and challenges in remote sensing applications is still missing. In this paper, we address these issues by performing a systematic review to identify the key trends of how explainable AI is used in remote sensing and shed light on novel explainable AI approaches and emerging directions that tackle specific remote sensing challenges. We also reveal the common patterns of explanation interpretation, discuss the extracted scientific insights in remote sensing, and reflect on the approaches used for explainable AI methods evaluation. As such, our review provides a complete summary of the state-of-the-art of explainable AI in remote sensing. Further, we give a detailed outlook on the challenges and promising research directions, representing a basis for novel methodological development and a useful starting point for new researchers in the field.

The escalating challenges of climate change, extreme weather events, and increasing food demand impose a significant strain on global food production. To develop and apply sustainable agriculture practices, farmers and organizations require detailed, timely information about weather, crops, and yields. While efficient agricultural monitoring relies heavily on remote sensing, the existing literature suffers from a notable lack of comprehensive, large-scale crop monitoring datasets. This paper introduces CropClimateX, a novel database built by optimizing location sampling to substantially cover cultivated areas throughout the contiguous United States. The database comprises 15,500 small 12 × 12 km data cubes spanning 1,527 counties. Crucially, each data cube integrates a rich array of multi-source information, including multi-sensor imagery (Sentinel-1/2, Landsat-8, MODIS), weather and extreme events (Daymet, heat/cold waves, and drought monitor maps), and environmental features (soil and terrain characteristics). This comprehensive, integrated dataset is designed to support a wide range of agricultural monitoring tasks, providing a vital resource for advancing research in sustainable farming and crop modeling.

Terragon (Earth(Poly)gon) is a Python package facilitating access to remote sensing and Earth observation data from multiple sources. Its goal is to unify the process of downloading data in a simple and efficient manner. While existing tools focus on specific satellites or data providers, Terragon offers a more flexible solution. The package offers a consistent way to search, filter, and download data from various data sources. It utilizes a polygon format to define the region of interest and creates a spatio-temporal data cube (Mahecha et al., 2020) of rasterized data in the Xarray Dataset format, as illustrated in Figure 1. Additionally, it ensures the alignment of projections and resolutions, organizing the data according to the selected resolution and coordinate reference system.