The use of effective methods for large-area drought monitoring is an important issue; hence, there have been many attempts to solve this problem. In this study, the Drought Information Satellite System (DISS) index is presented, based on the synergistic use of meteorological data and information derived from satellite images. The index allows us to monitor drought phenomena in various climatic and environmental conditions. The approach utilizes two indices for constructing a drought index: (1) the hydrothermal coefficient (HTC), which characterizes meteorological conditions across the study area over a long-term period; and (2) the temperature condition index (TCI) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) data, which refers instantaneous land surface temperature (LST) to long-term extreme values. The model for drought assessment based on the DISS index was applied for generating drought index maps for Poland for the 2001–2019 vegetation seasons. The performance of the index was verified through comparison of the extent of agricultural drought to the reduction in cereal and maize yield. Analysis of variance revealed a significant relationship between the area of drought determined by the drought index and the decrease in cereal yield due to unfavorable growth conditions. The presented study proves that the proposed drought index can be an effective tool for large-area drought monitoring under variable environmental conditions.

Remote sensing technologies are increasingly integrated with autonomous robotic platforms to enhance data-driven decision-making in precision agriculture. Rather than replacing conventional platforms such as satellites or UAVs, autonomous ground robots complement them by enabling high-resolution, site-specific observations in real time, especially at the plant level. This review analyzes how remote sensing sensors—including multispectral, hyperspectral, LiDAR, and thermal—are deployed via robotic systems for specific agricultural tasks such as canopy mapping, weed identification, soil moisture monitoring, and precision spraying. Key benefits include higher spatial and temporal resolution, improved monitoring of under-canopy conditions, and enhanced task automation. However, the practical deployment of such systems is constrained by terrain complexity, power demands, and sensor calibration. The integration of artificial intelligence and IoT connectivity emerges as a critical enabler for responsive, scalable solutions. By focusing on how autonomous robots function as mobile sensor platforms, this article contributes to the understanding of their role within modern precision agriculture workflows. The findings support future development pathways aimed at increasing operational efficiency and sustainability across diverse crop systems.

Climate change, particularly the increasing frequency of droughts, poses a critical challenge for agriculture. Rising temperatures and water scarcity threaten both agricultural productivity and ecosystem stability, making the identification of effective drought mitigation strategies essential. This study introduces an innovative approach to agricultural drought monitoring in Poland, utilizing remote sensing (RS) satellite data, collected from 2001 to 2020, and the Drought Identification Satellite System (DISS) index at a 1 km × 1 km spatial resolution, in combination with Copernicus High-Resolution Layers (HRL). To assess areas’ capacities to mitigate drought risks, a multi-criteria decision (MCD) analysis of regional environmental conditions was conducted. Focusing on the Mazowieckie Voivodeship, an algorithm was developed to evaluate regional susceptibility to drought. Spatial datasets were used to analyze environmental indicators, producing a map of communal temperature mitigation capacities. Statistical analysis identified drought vulnerability, highlighting areas in need of urgent intervention, such as increased mid-field tree planting. The study revealed that the frequency of droughts in this region during the growing season from 2001 to 2020 exceeded 40%. As a result, 40 LAU 2 administrative units have been affected by multiple negative environmental factors that contribute to drought formation and its long-term persistence. The proposed methodology, integrating diverse satellite data sources and spatial analyses, offers an effective tool for drought monitoring, mitigation planning, and ecosystem protection in a changing climate. This approach provides valuable insights for policymakers and land managers in addressing agricultural drought challenges and enhancing regional resilience to the impacts of climate change.

Remote satellite observations have played a crucial role in monitoring vegetation since the 1970s, starting with the development of the Normalized Difference Vegetation Index (NDVI) by Rouse (1974) and Tucker (1979). Despite advances in satellite technology, validation with in situ measurements, which are often locally sparse, remains essential. Areas such as grasslands and wetlands, which are vital for CO ₂ balance and water quality, require special attention. Within the GrasSAT project, in situ measurements were conducted in Poland and Norway, and included LAI, soil moisture, and biomass. This article focuses exclusively on in situ studies carried out in Poland, and presents results related to models operating under Polish environmental conditions, highlighting the importance of local factors in the context of comparing satellite and ground data. Different sampling methods, such as linear transect and quadrat methods, were considered. The research aimed to understand and improve the consistency between satellite and ground data, which is crucial for accurate vegetation models.

Time series of weekly and daily solutions for coordinates of permanent GNSS stations may indicate local deformations in Earth’s crust or local seasonal changes in the atmosphere and hydrosphere. The errors of the determined changes are relatively large, frequently at the level of the signal. Satellite radar interferometry and especially Persistent Scatterer Interferometry (PSI) is a method of a very high accuracy. Its weakness is a relative nature of measurements as well as accumulation of errors which may occur in the case of PSI processing of large areas. It is thus beneficial to confront the results of PSI measurements with those from other techniques, such as GNSS and precise levelling. PSI and GNSS results were jointly processed recreating the history of surface deformation of the area of Warsaw metropolitan with the use of radar images from Envisat and Cosmo-SkyMed satellites. GNSS data from Borowa Gora and Jozefoslaw observatories as well as from WAT1 and CBKA permanent GNSS stations were used to validate the obtained results. Observations from 2000–2015 were processed with the Bernese v.5.0 software. Relative height changes between the GNSS stations were determined from GNSS data and relative height changes between the persistent scatterers located on the objects with GNSS stations were determined from the interferometric results. The consistency of results of the two methods was 3 to 4 times better than the theoretical accuracy of each. The joint use of both methods allows to extract a very small height change below the level of measurement error.