This paper presents plans and efforts on European Union (EU) Member States (MSs) (including Norway and Iceland)-specific support for monitoring emissions and removals from land use, land use change and forestry (LULUCF) which are built on existing and forthcoming Copernicus data and services. Land use and change between land uses are mapped using at top level and appropriate sub-categories in accordance to the current the Intergovernmental Panel on Climate Change (IPCC) guidelines to allow MSs to calculate the greenhouse gas (GHG) emissions and removals. We discuss findings on (1) technical needs in Member State (MS) for meeting technical requirements of reporting the GHG emissions and removals, (2) definition and implementation of improved national and pan-European methodologies and technical solutions such as tools or software or integrated data sets meeting the needs of the LULUCF regulation including the calculation of carbon stock change and the GHG emissions and removals and (3) training and workshops for the relevant services in MSs.

The study covered the analysis of crop development during the seasons for the years of various conditions. Temperature Condition Index characterized the growth of vegetation during the season. For better assessment of crop development conditions, the TCI was taken as decaying 3-point moving average. The examination has been done to find out the delay in reaction of vegetation on lack of precipitation and increase of air temperature. 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 longterm extreme values. The spatio-temporal variations of agricultural drought have been expressed by remote sensing based drought index - DISS. 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. The research has been carried out within the Project SAPOL4CROP NCBiR

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 main goal of this study was the determination of carbon flux model for the area of the Biebrza Wetlands, to evaluate their potential as carbon sinks, based on meteorological, soil and vegetation parameters. Based on ground data and satellite data (images from the Sentinel -1, -2, -3 and MODIS) acquired in 2017–2019, a statistical model was used that combines the carbon dioxide stream, latent heat and soil moisture. The flux of carbon dioxide and latent heat were recorded using the eddy covariance tower, which allows for very high sensitivity measurements at a frequency of 20 Hz. Soil moisture was measured at various points in the Biebrza National Park at a depth of 5 cm with a time resolution of 15 minutes. Later, the field parameters were replaced with their satellite equivalents (e.g. soil moisture - [1]), which made it possible to obtain carbon dioxide flux values for any point within the park. The assessment of carbon fluxes is performed at 1-km spatial scale. Carbon measurements done by the chamber method were used to verify the model. All data were extracted using a R programming language script. Further analyses were carried out in R and Statistica program, the satellite image processing was performed using a ERDAS and program R.

In preparation for Canada's launch of the RADARSAT-Constellation, this study examines the use of VV-VH intensities to estimate the Leaf Area Index (LAI) of corn. LAI is indicative of crop productivity. Two implementations of the Water Cloud Model performed equally well in estimating corn LAI over sites in Poland and Canada with correlation coefficients over 0.8 and Root Mean Square Errors and Mean Average Errors of 0.72-0.73 m2m-2and 0.47-0.54 m2m-2, respectively. This research will continue to pull in data from other international sites. If results remain robust, a strong case can be made to use an integration of Sentinel-1 and RCM for operational crop condition monitoring.

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.

Grasslands deliver wide range of ecosystem services such as carbon sequestration, water quality, flood and erosion control as well as biomass. But since they are impacted by climate change, there is a need for their constant monitoring. Due to climate changes the following grasslands growth conditions are modified: soil moisture, biomass and as a consequence carbon balance. Required multi-temporal and spatial observations are possible with satellite data. The objective of this paper is to present the long term observations of temperature and vegetation conditions (NDVI) of grasslands on the basis of MODIS satellite data as well as already performed and further scheduled frequent analysis of carbon balance, soil moisture and biomass performed with the application of Sentinel1 A; B and Sentinel2 A&B.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

Wetland ecosystems keep large amounts of organic carbon and have large influence on global climate change. Net ecosystem exchange (NEE) have been modeled by means of microwave satellite images. Assessment of biomass and soil moisture has been essential for the study to elaborate the methodology for evaluating carbon sink at the ecosystem under Ramsar Convention. Backscattering coefficient (σ˚) calculated from microwave images acquired by ENVISAT, ALOS and Sentinel-1 radar sensors was analyzed along with ground truth measurements of biomass, LAI, soil moisture (SM) and NEE carried out for vegetation classes distinguished from MERIS image. The models for NEE were developed using IS4 VV which represented vegetation biomass and IS4 HH representing SM. Application of the independent set of microwave data which were possible to gather gives a valuable opportunity to verify the accuracy in assessment of biomass and humidity based on various available sensors.

The results of measurements of CO2exchange in various wetland communities were elaborated for the area of Biebrza National Park in north-eastern Poland. The research has been done within ongoing ESA-PECS and National Research Project (No N N526 160040) realized in the Institute of Geodesy and Cartography, Remote Sensing Department in Warsaw. CO2flux measurements were performed with a static chamber method from April till September from 2010 – 2012. For each of the classified wetlands vegetation habitats the relationship between soil moisture and backscattering coefficient has been examined and the best combination of microwave variables ENVISAT ASAR (wave length, incidence angle, polarization) has been used for mapping and monitoring of soil moisture. Carbon input to an ecosystem occurs through the process of photosynthesis. The rate of photosynthesis (CO2uptake) is referred to as gross primary production (GPP). CO2is in turn released to the atmosphere through respiration. The difference between GPP and Recois referred to as net ecosystem exchange (NEE). By estimating the direction of NEE, it is possible to determine whether a surface is a likely source or a sink of carbon. The NEE has been related to biomass and soil moisture which was also related to the ratio of NDVI and Ts from NOAA/AVHRR.

This study was conducted during 2003-2009 in Biebrza Wetlands, a NATURA 2000 and Ramsar Convention test site situated in Northeast Poland. It is one of the largest in Europe natural rich biotope with the large amount of unique spices of flora and important zone for nesting and wintering for fauna. Protection of wetlands that are very sensitive ecosystems are of great importance in nature conservation for carbon and water cycles. Changes of soil water content affect plant cover and lead to elimination or preference of certain species. Controlling soil moisture is essential for protection of peat-forming plant communities and slow down drying processes against mineralization and carbon exhaust. Data from optical and microwave satellite images and soil-vegetation ground measurements were analyzed to develop methods for monitoring and mapping soil-vegetation parameters over wetlands. This study was conducted in the framework of national grant N N526021733 and ESA projects AOID.122 and AOALO.3742.

The SPOT VEGETATION and NOAA AVHRR images have been used as the source of information on crop growing conditions and yield forecast for cereals and winter wheat in Poland. Three indices - Vegetation Condition Index (VCI), Accumulated Vegetation Condition Index (AVCI) based on NDVI and Temperature Condition Index (TCI) based on surface temperature have been computed for agriculture area in Poland. These indices were correlated with cereal yield and critical periods of crop development were found as significant for this relationship. The information about indices from particle decades was included into the cereals and winter wheat yield modelling. The database of the indices and obtained statistical models were used for developing the Predictive Indices (PTVCI, PTAVCI, VCIAVG) applied for the forecast of yield for each decade of the year. The method may be used operationally due to its simplicity and easy obtainable data.

The radar data have been used for establishing the proper crop information system in Poland. The objective of the study is to find an efficient method of crop classification based on satellite microwave data and to find the relationship of different soil - vegetation parameters on backscatter. There is a large demand of microwave images as due to often cloud effect these satellite data are available during a certain growth season. Wielkopolska region located in western Poland was selected for the research works. This region, characterized by intensive agricultural practices and diversified agricultural pattern, was equipped with ground truth information, which enabled to make properly the whole classification process.

The study was carried out for agricultural area in Poland for the years 2003-2007. The aim of the project was to examine the impact of soil-vegetation parameters on backscatter calculated from ENVISAT.ASAR under various polarizations and incidence angles. The extensive field measurements have been carried out simultaneously to satellite overpasses. Additionally to microwave acquisitions the optical data from TERRA.ASTER satellite have been used to distinguish different crop types. There has been applied two approaches: (1) for each of the class the equations for calculation of LAI and soil moisture from microwave data have been derived using statistical analyses, and the maps of LAI and soil moisture have been presented; (2) the semi empirical water-cloud model has been applied to describe the contribution of soil moisture and winter wheat descriptor as LAI on backscattering coefficient. The results show that presented methods can be implemented into monitoring of crop growth.

The investigation carried out at wetlands in Biebrza Basin, the biggest area of the marshes and swamps in Central Europe, aimed at finding the best biophysical properties of wetlands vegetation to characterise marshland habitats. The various soil-vegetation indices on the basis of all considered spectral bands of satellites as Landsat +ETM, SPOT, ERS-2, NOAA, ENVISAT have been calculated. The GEMI and EVI index calculated from SPOT VEGETATION was the best for distinguishing vegetation classes. Significant correlation between LAI measured at the ground and the indices was with GEMI and EVI index. Soil moisture values calculated from ERS-2 and ENVISAT microwave data well characterise marshland humidity classes. For the retrieval of the biophysical parameters as LAI (Leaf Area Index), vegetation moisture (VM) and soil moisture (SM), ERS-2.SAR and ENVISAT ASAR data acquired at VV, HV, VH and HH polarisations at two different viewing angles (IS2, IS4) have been applied. Evapotranspiration was assessed using NOAA AVHRR and meteorological data. ERS-2 and ENVISAT images have been obtained from ESA for AO-ID122 project.

Agricultural production in Poland is the main source of income for nearly 12% of population providing food for 40 million people of the on tire country and the main contribution to the gross national product through the export of live animals, animals and vegetable products and prepared foodstuffs. The production of basic consumer cereals is to considerable extent weather dependent and in some years is not sufficient for needs within the country. The investigation has been performed for cereals using NOAA/AVHRR data. NDVI, TCI and VCI indices have been applied for calculating cereals growth conditions and to assess the sensitivity of these indices in monitoring drought effect on yield. The effect of soil moisture conditions have been statistically compared to modeled values of evapotranspiration as well as to detailed ground observations of soil moisture and vegetation parameters, which are measured at special agricultural stations. It was proved that the applied methods give good results.

The numeric inversion of water-cloud model of synchronized microwave bands of ERS-2 and JERS satellites gave the possibility of obtaining crop characteristics. Model performance was validated by comparison between backscattering coefficients simulated and measured by satellites. The contribution of various crop characteristics was presented and compared to measured soil-vegetation parameters at the ground level during satellite overpasses.