Radoslaw Gurdak

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.

This study, employing the AquaCrop model, demonstrated notable efficacy in assessing and predicting crop yields for winter wheat, maize, winter rapeseed, and sugar beets in the Joint Experiment for Crop Assessment and Monitoring (JECAM) test area of Poland from 2018 to 2023. In-situ measurements, conducted through field campaigns, included parameters such as electromagnetic radiation reflectance, Leaf Area Index (LAI), soil moisture, accumulated photosynthetically active radiation, chlorophyll content, and plant development phase. The model was calibrated with input data covering daily climatic parameters from the ERA5-land Daily Aggregated repository, crop details, and soil characteristics. Specifically, for winter wheat, the Root Mean Square Error (RMSE) values ranged from 1.92% to 14.26% of the mean yield per hectare. Maize cultivation showed RMSE values ranging from 0.21% to 1.41% of the mean yield per hectare. Winter rapeseed exhibited RMSE values ranging from 0.58% to 17.15% of the mean yield per hectare. In the case of sugar beets, the RMSE values ranged from 0.40% to 1.65% of the mean yield per hectare. Normalized Difference Vegetation Index (NDVI)-based predictions showed higher accuracy for winter wheat, similar accuracy for maize and sugar beets, but lower accuracy for winter rapeseed compared to Leaf Area Index (LAI). The study contributes valuable insights into agricultural management practices and facilitates decision-making processes for farmers in the region.

The CO ₂ flux on Biebrza Wetlands in Poland is analysed by ground measurements and satellite data. The main goal of this study was to determine the Net Ecosystem Exchange (NEE) for sedges, reeds and grasses using meteorological, soil and vegetation parameters and satellite indicators. The calculations are based on chamber measurements scattered over the main habitats and meteorological registrations, and compared to the 2015–2022 eddy covariance (EC) measurements. The NEE model, to get its distribution throughout the day, was elaborated using modelled gross primary production GPP and modelled respiration RESP. The model of ecosystem respiration was developed applying air temperature, soil temperature, soil moisture and NDVI. The satellite vegetation index NDVI, surface and air difference temperature (T _S -T _A ), and NDII proved to be good predictors for the daily mean GPP. The data from the EC tower was used to compare the results from modelled NEE and CO ₂ flux.

The increase in demand for food and the need to predict the impact of a warming climate on vegetation makes it critical that the best tools for assessing crop production are found. Chlorophyll fluorescence (ChlF) has been proposed as a direct indicator of photosynthesis and plant condition. The aim of this paper is to study the feasibility of estimating ChlF from spectral vegetation indices derived from Sentinel-2, in order to monitor crop stress and investigate ChlF changes in response to surface temperatures and meteorological observations. The regressions between thirty three Sentinel-2-derived VIs, and ChlF measured on the ground were evaluated in order to estimate the best predictors of ChlF. The r-Pearson correlation and polynomial linear regression were used. For maize, the highest correlation between ChlF and VIs were found for NDII (r=0.65) and for SIPI (r=−0.68). The weakest relationship between VIs and ChlF were found for sugar beets. Despite this, it should be noted that the highest correlation for sugar beets appeared for EVI (r=0.45) and S2REP (r=0.43). The results of this study indicate the need for a synergy of low and high resolution satellite data that will enable a more detailed analysis for estimating fluorescence and its relation to climatic conditions, environmental aspects, and VIs derived from satellite images.

The study is carried out for agricultural areas in Poland and South Africa for the years 2019–2021. The aim of the project is to develop crop growth monitoring system using Copernicus satellite data (Sentinel-1,-2,-3) and low-resolution data from Terra MODIS satellites. At the first stage of the analysis Sentinel-2 indices, based on various combinations of spectral reflectance, were derived for particular fields within JECAM sites. The correlation coefficients between S-2 indices and LAI most often exceed 0.7 and precision of LAI determination varies from 92 to 81%, depending on year and crop type. NDII and DSWI proved to be most appropriate for LAI assessment, although usefulness of narrowband indices based on red-edge bands, like CLRE, MTCI, NDREI has been also found. Finally the prognosis model based on meteorological data with the periodical input of NDVI and LST for winter wheat yield prognosis has been applied. RMSE error of the model is 5.4 dt/ha.

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.

Soil moisture measurement using satellite radar images for various kinds of crops is a subject widely described in the literature. Typical solutions presented concern the crops grown in areas with flat surfaces (e.g. wheat). However, some crop cultivation with uneven ground surfaces poses a totally different problem if satellite radar imaging is to be applied. The shape of the farmland surface can significantly affect the satellite radar cross-section e.g. rows usually formed in parallel ridges separated from each other by some distance. To investigate how the shape of the cultivation surface affects the RF-signal reflections, simulations were carried out. For verification purposes, real radar images (SAR) were obtained from the SENTINEL-1 satellite along with ground truth information about soil moisture from sensors on site.In this article, the authors present some preliminary results of research on the estimation of soil moisture by using neural networks based on satellite SAR radar data.

The objective of the study was to estimate soil moisture (SM) from Sentinel-1 (S-1) satellite images acquired over wetlands. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in north-eastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to SM retrieval for a broad range of vegetation and soil moisture conditions. The methodology is based on research into the effect of vegetation on backscatter (σ°) changes under different soil moisture and Normalized Difference Vegetation Index (NDVI) values. The NDVI was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. It was found that the state of the vegetation expressed by NDVI can be described by the indices such as the difference between σ° VH and VV, or the ratio of σ° VV/VH, as calculated from the Sentinel-1 images in the logarithmic domain. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the newly developed models using Water Cloud Model (WCM) that includes the derived indices based on S-1, allowed the estimation of SM for wetlands with reasonable accuracy (10 vol. %). The developed soil moisture retrieval algorithms based on S-1 data are suited for wetland ecosystems, where soil moisture values are several times higher than in agricultural areas.

The study was carried out for agricultural area in Poland for the years 2016-2017. The aim of the project was to examine the applicability of vegetation parameters calculated from Sentinel-2 and PROBA-V satellite data for crop yield prognosis. The extensive field measurements have been carried out parallel to Sentinel-2 and Proba-V satellite overpasses in order to elaborate the best relationship between satellite data and in-situ measured LAI. Finally the prognosis model based on meteorological data with the periodical input of LAI for wheat yield prognosis has been applied. Additionally classification of crops over JECAM site in Wielkopolska district was performed for 2016 and 2017 to choose the fields with wheat and with other crops for further research.