VENµS-Derived NDVI and REIP at Different View Azimuth Angles

The bidirectional reflectance distribution function (BRDF) is crucial in determining the quantity of reflected light on the earth’s surface as a function of solar and view angles (i.e., azimuth and zenith angles). The Vegetation and ENvironment monitoring Micro-Satellite (VENµS) provides a unique opportunity to acquire data from the same site, with the same sensor, with almost constant solar and view zenith angles from two (or more) view azimuth angles. The present study was aimed at exploring the view angles’ effect on the stability of the values of albedo and of two vegetation indices (VIs): the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). These products were calculated over three polygons representing urban and cultivated areas in April, June, and September 2018, under a minimal time difference of less than two minutes. Arithmetic differences of VIs and a change vector analysis (CVA) were performed. The results show that in urban areas, there was no difference between the VIs, whereas in the well-developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. Results suggest that REIP is a more appropriate index than NDVI for field crop studies and monitoring. This conclusion can be applied in a constellation of satellites that monitor ground features simultaneously but from different view azimuth angles

VENµS-Derived NDVI and REIP at Different View Azimuth Angles

The bidirectional reflectance distribution function (BRDF) is crucial in determining the quantity of reflected light on the earth’s surface as a function of solar and view angles (i.e., azimuth and zenith angles). The Vegetation and ENvironment monitoring Micro-Satellite (VENµS) provides a unique opportunity to acquire data from the same site, with the same sensor, with almost constant solar and view zenith angles from two (or more) view azimuth angles. The present study was aimed at exploring the view angles’ effect on the stability of the values of albedo and of two vegetation indices (VIs): the normalized difference vegetation index (NDVI) and the red-edge inflection point (REIP). These products were calculated over three polygons representing urban and cultivated areas in April, June, and September 2018, under a minimal time difference of less than two minutes. Arithmetic differences of VIs and a change vector analysis (CVA) were performed. The results show that in urban areas, there was no difference between the VIs, whereas in the well-developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. Results suggest that REIP is a more appropriate index than NDVI for field crop studies and monitoring. This conclusion can be applied in a constellation of satellites that monitor ground features simultaneously but from different view azimuth angles

Burned Area Mapping Using Multi-Temporal Sentinel-2 Data by Applying the Relative Differenced Aerosol-Free Vegetation Index (RdAFRI)

Assessing the development of wildfire scars during a period of consecutive active fires and smoke overcast is a challenge. The study was conducted during nine months when Israel experienced massive pyro-terrorism attacks of more than 1100 fires from the Gaza Strip. The current project strives at developing and using an advanced Earth observation approach for accurate post-fire spatial and temporal assessment shortly after the event ends while eliminating the influence of biomass burning smoke on the ground signal. For fulfilling this goal, the Aerosol-Free Vegetation Index (AFRI), which has a meaningful advantage in penetrating an opaque atmosphere influenced by biomass burning smoke, was used. On top of it, under clear sky conditions, the AFRI closely resembles the widely used Normalized Difference Vegetation Index (NDVI), and it retains the same level of index values under smoke. The relative differenced AFRI (RdAFRI) set of algorithms was implemented at the same procedure commonly used with the Relative differenced Normalized Burn Ratio (RdBRN). The algorithm was applied to 24 Sentinel-2 Level-2A images throughout the study period. While validating with ground observations, the RdAFRI-based algorithms produced an overall accuracy of 90%. Furthermore, the RdAFRI maps were smoother than the equivalent RdNBR, with noise levels two orders of magnitude lower than the latter. Consequently, applying the RdAFRI, it is possible to distinguish among four severity categories. However, due to different cloud cover on the two consecutive dates, an automatic determination of a threshold level was not possible. Therefore, two threshold levels were considered through visual inspection and manually assigned to each imaging date. The novel procedure enables calculating the spatio-temporal dynamics of the fire scars along with the statistics of the burned vegetation species within the study area

Modelling Multi-Year Carbon Fluxes in the Arctic Critical Zone (Spitzbergen, NO)

<p>Presentation given at the Svalbard Science Conference 2023 (SSC23) that took place in Oslo, Norway on October 31st-November 01st, 2023. </p><p>Vegetation and soil regulate the terrestrial carbon cycle and contribute to the atmospheric CO2 concentration and Earth climate. The Arctic soil plays a major role in this cycle as the extension of permafrost areas is around 25% of the land in the Northern hemisphere and it is estimated that permafrost stores 2-3 times the atmospheric carbon. In the Holocene, the tundra has acted as a carbon sink, but it is not clear if the fast Arctic climate change will turn it into a carbon source. Yet, data regarding Arctic carbon fluxes are scarce and modelling of their fate is affected by large uncertainties. </p><p>With the aim of investigating the tundra carbon fluxes dynamics on the high Arctic, CNR established the Bayelva Critical Zone Observatory at the Ny Ålesund research station in Svalbard since 2019, equipped with an Eddy Covariance tower and portable flux chambers for the measurement of Gross Primary Productivity (GPP) and of Ecosystem Respiration (ER) variability at the point scale, making it possible to build empirical models that correlate such variables to climate and environmental parameters such as temperature, irradiance, moisture and phenology. A first model, published in 2022, identified temperature, solar irradiance, soil moisture and green fractional cover as drivers. Further measurements done in 2021 and 2022 adding further sites in the Bayelva basin, allowed us to enlarge the scale of application of the model. A further step will be the use of the high-resolution satellite data of the VENmS mission (4 meters, 1 day revisit time) to extend the modelling of GPP over the entire Broegger peninsula, facilitating the spatial upscaling of measured fluxes,identifying the main variables to be used in general vegetation models and allowing future projections of carbon fluxes under different climate change scenarios in the high Arctic tundra.</p&gt