Earth observation (EO) applications involving complex and heterogeneous data sources are commonly approached with machine learning models. However, there is a common assumption that data sources will be persistently available. Different situations could affect the availability of EO sources, like noise, clouds, or satellite mission failures. In this work, we assess the impact of missing temporal and static EO sources in trained models across four datasets with classification and regression tasks. We compare the predictive quality of different methods and find that some are naturally more robust to missing data. The Ensemble strategy, in particular, achieves a prediction robustness up to 100%. We evidence that missing scenarios are significantly more challenging in regression than classification tasks. Finally, we find that the optical view is the most critical view when it is missing individually.

Yield prediction at both field and subfield level poses a significant challenge, yet it holds paramount importance for decision-making and food security within the agricultural sector. Recent efforts, focused on integrating remote sensing data coupled with machine learning models, thereby creating globally scalable models for various crop types. This study underscores the effectiveness of Sentinel-2 and complementary data sources such as weather, soil, and terrain in enhancing machine learning-based yield prediction. We address the limitations of previous works and introduce a framework that incorporates local neighborhood information using convolutional neural networks and geographical coordinates. Additionally, we address the complexity of sensor fusion, showcasing both input fusion and feature fusion frameworks. We highlight that handling modalities with varying spatial and temporal resolutions requires adequate and advanced fusion mechanisms in crop yield prediction. Notably, this study reports an R2of 0.86 for soybean in Argentina using a feature fusion scheme with attention mechanism. The results are demonstrated on a large yield dataset for soybean, wheat, and rapeseed distributed across Argentina, Uruguay, and Germany.

Vegetation indices allow to efficiently monitor vegetation growth and agricultural activities. Previous generations of satellites were capturing a limited number of spectral bands, and a few expert-designed vegetation indices were sufficient to harness their potential. New generations of multi- and hyperspectral satellites can however capture additional bands, but are not yet efficiently exploited. In this work, we propose an explainable-AI-based method to select and design suitable vegetation indices. We first train a deep neural network using multispectral satellite data, then extract feature importance to identify the most influential bands. We subsequently select suitable existing vegetation indices or modify them to incorporate the identified bands and retrain our model. We validate our approach on a crop classification task. Our results indicate that models trained on individual indices achieve comparable results to the baseline model trained on all bands, while the combination of two indices surpasses the baseline in certain cases.

Since the launch of the Sentinel-2 (S2) satellites, many ML models have used the data for diverse applications. The scene classification layer (SCL) inside the S2 product provides rich information for training, such as filtering images with high cloud coverage. However, there is more potential in this. We propose a technique to assess the clean optical coverage of a region, expressed by a SITS and calculated with the S2-based SCL data. With a manual threshold and specific labels in the SCL, the proposed technique assigns a percentage of spatial and temporal coverage across the time series and a high/low assessment. By evaluating the AI4EO challenge for Enhanced Agriculture, we show that the assessment is correlated to the predictive results of ML models. The classification results in a region with low spatial and temporal coverage is worse than in a region with high coverage. Finally, we applied the technique across all continents of the global dataset LandCoverNet.

Accurate and reliable crop yield prediction is a complex task. The yield of a crop depends on a variety of factors whose accurate measurement and modeling is challenging. At the same time, reliable yield prediction is highly desirable for farmers to optimize crop production. In this paper, we introduce a modeling based on remote sensing data and Machine Learning models evaluated on a large-scale dataset to address the challenge of an operational crop yield estimation and forecasting on field and subfield level. With our approach, we aim towards a global yield modeling based on Machine Learning models which operates across crop types without the need for crop-specific modeling. We demonstrate that our approach learns to map in-field variability for all studied crop types. Overall, the predictions have an error (RRMSE) of around 15% and an R2value of 0.77 at field level.

With a rapidly increasing amount and diversity of remote sensing (RS) data sources, there is a strong need for multi-view learning modeling. This is a complex task when considering the differences in resolution, magnitude, and noise of RS data. The typical approach for merging multiple RS sources has been input-level fusion, but other - more advanced - fusion strategies may outperform this traditional approach. This work assesses different fusion strategies for crop classification in the CropHarvest dataset. The fusion methods proposed in this work outperform models based on individual views and previous fusion methods. We do not find one single fusion method that consistently outperforms all other approaches. Instead, we present a comparison of multi-view fusion methods for three different datasets and show that, depending on the test region, different methods obtain the best performance. Despite this, we suggest a preliminary criterion for the selection of fusion methods.

The utilization of multimodal analysis techniques, combining satellite imagery with terrain information, has gained prominence in flood detection. This study focuses on the inclusion of elevation data into the Sen1floods11 dataset, an open dataset for flood damage detection, to investigate the influence of terrain information on flood detection tasks. Among the considered terrain information, the inclusion of elevation data resulted in a bias of overestimating the presence of water in relatively low-lying areas, without contributing to accuracy improvement. However, the utilization of slope information, derived from differentiating the elevation data, mitigated such bias and yielded a slight improvement in accuracy. This finding aligns with the utilization of derivatives in physical equations describing flood flow, suggesting the explicit incorporation of physics-based principles, such as the flow of water based on slope, to enhance model accuracy in future research endeavors.

Numerous remote sensing applications rely on temporal satellite data, and Deep learning models are increasingly being used for such tasks. Nevertheless, these models operate as black boxes, lacking transparency and understandability. We address this gap by using explainable AI on an agricultural task. Specifically, we trained a recurrent neural network on individual pixels from multispectral time-series of Sentinel-2 satellite images to predict crop yield. We then applied nine feature attribution methods on a sample of the dataset and computed the spectral and temporal contributions to the final individual predictions. The aggregated results were evaluated qualitatively and quantitatively. Results suggest that LIME and Shapley sampling value methods performed best on the quantitative scores, followed by GradientShap. Most backpropagation-based techniques had highly inconsistent scores across the explained data points. Finally, to guide remote sensing practitioners in using Explainable AI on similar datasets, we further discuss some selection criteria to be considered.

We introduce a simple yet effective early fusion method for crop yield prediction that handles multiple input modalities with different temporal and spatial resolutions. We use high-resolution crop yield maps as ground truth data to train crop and machine learning model agnostic methods at the sub-field level. We use Sentinel-2 satellite imagery as the primary modality for input data with other complementary modalities, including weather, soil, and DEM data. The proposed method uses input modalities available with global coverage, making the framework globally scalable. We explicitly highlight the importance of input modalities for crop yield prediction and emphasize that the best-performing combination of input modalities depends on region, crop, and chosen model.

Modern combine harvesters can collect geo-located real-time yield measurement while harvesting. This data can be used to train Machine Learning models that predict the yield at sub-field level based on remote sensing input data. The performance of these models is, however, highly dependent on the quality of the yield data. It is therefore important to develop automatic cleaning techniques to correct for common errors in combine harvester yield maps. In this work, we compare different combinations of data cleaning techniques by evaluating their impact on the yield-prediction model performance at field and sub-field level. Our findings indicate that basic cleaning techniques such as absolute thresholds are sufficient at the field level, whereas the performance at the sub-field level is enhanced through the utilization of more intricate statistical cleaning methods.

Floods are one of the most severe natural catastrophes and therefore emergency response operations are crucial in order to save lifes. These operations require information about flooded areas so that rescue missions can precisely and efficiently use their available resources. This requires a quick automated procedure which is able to identify these regions from remote sensing images. To achieve this goal we utilize machine learning and apply our method on the Sen1Floods11 dataset. Our main contribution lies in the fusion of Digital Elevation Maps (DEMs) with Satellite data. We investigate the effect of several different combinations of processing methods of DEMs, such as depression filling, deriving slope and curvature or flow metrics. In total 44 different experiments have been performed where our best performing combination outperformed the benchmark in terms of mean IoU. Lastly, we also publish our code for downloading and processing DEMS as well as running our experiments.