Developments in space-based hyperspectral sensors, advanced remote sensing, and machine learning can help crop yield measurement, modelling, prediction, and crop monitoring for loss prevention and global food security. However, precise and continuous spectral signatures, important for large-area crop growth monitoring and early prediction of yield production with cutting-edge algorithms, can be only provided via hyperspectral imaging. Therefore, this study used new-generation Deutsches Zentrum für Luft- und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) images to classify the main crop types (hybrid corn, soybean, sunflower, and winter wheat) in Mezőhegyes (southeastern Hungary). A Wavelet-Attention convolutional neural network (WA-CNN), Random forest (RF) and support vector machine (SVM) algorithms were utilized to automatically map the crops over the agricultural lands. The best accuracy was achieved with the WA-CNN, a feature-based deep learning algorithm and a combination of two images with overall accuracy (OA) value of 97.89% and the user's accuracy (UA) producer's accuracy (PA) was from 97% to 99%. To obtain this, firstly Factor Analysis was introduced to decrease the size of the hyperspectral image (HSI) data cube. A wavelet transform was applied to extract important features and combined with the spectral attention mechanism CNN to gain higher accuracy in mapping crop types. Followed by SVM algorithm reported overall accuracy (OA) of 87.79%, with the producer's and user's accuracies of its classes ranging from 79.62% to 96.48% and from 79.63% to 95.73%, respectively. These results demonstrate the potentiality of DESIS data to observe the growth of different crop types and predict the harvest volume, which is crucial for farmers, smallholders, and decision-makers.

The species richness and biodiversity of vegetation in Hungary are increasingly threatened by invasive plant species brought in from other continents and foreign ecosystems. These invasive plant species have spread aggressively in the natural and semi-natural habitats of Europe. Common milkweed (Asclepias syriaca) is one of the species that pose the greatest ecological menace. Therefore, the primary purpose of the present study is to map and monitor the spread of common milkweed, the most common invasive plant species in Europe. Furthermore, the possibilities to detect and validate this special invasive plant by analyzing hyperspectral remote sensing data were investigated. In combination with field reference data, high-resolution hyperspectral aerial images acquired by an unmanned aerial vehicle (UAV) platform in 138 spectral bands in areas infected by common milkweed were examined. Then, support vector machine (SVM) and artificial neural network (ANN) classification algorithms were applied to the highly accurate field reference data. As a result, common milkweed individuals were distinguished in hyperspectral images, achieving an overall accuracy of 92.95% in the case of supervised SVM classification. Using the ANN model, an overall accuracy of 99.61% was achieved. To evaluate the proposed approach, two experimental tests were conducted, and in both cases, we managed to distinguish the individual specimens within the large variety of spreading invasive species in a study area of 2 ha, based on centimeter spatial resolution hyperspectral UAV imagery.

Satellite images are widely used for crop yield estimation, but their coarse spatial resolution means that they often fail to provide detailed information at the field scale. Recently, a new generation of high-resolution satellites and CubeSat platforms has been launched. In this study, satellite data sources including PlanetScope and Sentinel-2 were combined with topographic and climatic variables, and the improvement in wheat yield estimation was evaluated. Wheat yield data from a combine harvester were used to train and validate a yield estimation model based on random forest regression. Nine vegetation indices (NDVI, GNDVI, MSAVI2, MTVI2, MTCI, reNDVI, SAVI, EVI and WDVI) and spectral bands were tested. During the model training, the Sentinel-2 data realized a slightly higher estimation accuracy than the PlanetScope data. However, combining environmental data with the PlanetScope data realized the highest estimation accuracy. For the validated models, adding the topographic and climatic datasets to the satellite data sources improved the estimation accuracy, and the results were slightly better with the Sentinel-2 data than with the PlanetScope data. Observation data of the milk and dough stages provided the highest estimation accuracy of the wheat yield at 210–225 days after sowing.

Today, invasive alien species cause serious trouble for biodiversity and ecosystem services, which are essential for human survival. In order to effectively manage invasive species, it is important to know their current distribution and the dynamics of their spread. Unmanned aerial vehicle (UAV) monitoring is one of the best tools for gathering this information from large areas. Vegetation indices for multispectral camera images are often used for this, but RGB colour-based vegetation indices can provide a simpler and less expensive solution. The goal was to examine whether six RGB indices are suitable for identifying invasive plant species in the QGIS environment on UAV images. To examine this, we determined the shoot area and number of common milkweed (Asclepias syriaca) and the inflorescence area and number of blanket flowers (Gaillardia pulchella) as two typical invasive species in open sandy grasslands. According to the results, the cover area of common milkweed was best identified with the TGI and SSI indices. The producers’ accuracy was 76.38% (TGI) and 67.02% (SSI), while the user’s accuracy was 75.42% (TGI) and 75.12% (SSI), respectively. For the cover area of blanket flower, the IF index proved to be the most suitable index. In spite of this, it gave a low producer’s accuracy of 43.74% and user’s accuracy of 51.4%. The used methods were not suitable for the determination of milkweed shoot and the blanket flower inflorescence number, due to significant overestimation. With the methods presented here, the data of large populations of invasive species can be processed in a simple, fast, and cost-effective manner, which can ensure the precise planning of treatments for nature conservation practitioners.

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

This study aimed to develop a new method for combining Sentinel-2 and PlanetScope imagery. The normalized difference vegetation indices (NDVI) data were retrieved from the Earth observation satellites S2 Level-2A and PS Level-3 surface reflectance (PS) during the soybean growing phase. The proposed method utilizes the pyDMS algorithm, which is a decision tree-based technique for enhancing low and high-resolution images with information from large-scale images. The robustness and flexibility of a multidimensional data fusion, deep neural network, and machine learning-based yield estimation model were analyzed based on the within-field variability in soybean yield. A comparative analysis revealed that the fusion data with 1.5 to 2.5 tons significantly outperformed individual predictions, demonstrating higher accuracy and fewer errors. The fusion data used to predict yields showed relatively small error ranges of 0.5 to 0.2 t/ha. In contrast, the PS and S2 datasets showed higher prediction errors. The study employed vegetation indices, and during validation, crop forecasts were compared using an NDVI map. The effectiveness of artificial neural networks in predicting crop yields was highlighted, demonstrating superior performance across diverse datasets compared with other algorithms. This novel fusion technique is essential for monitoring crop health and growth, improving agricultural practices, such as fertilization and water management, and improving yield forecast accuracy. This study provides valuable insights into phenology monitoring, image fusion accuracy, and the effectiveness of machine learning algorithms in predicting crop yields, emphasizing the benefits of fused imagery.