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Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season's backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former's lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring. [ABSTRACT FROM AUTHOR]

The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better. [ABSTRACT FROM AUTHOR]

P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%–36% (27–33 t/ha) for airborne data and 38%–39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity. [ABSTRACT FROM AUTHOR]

Between 2006 and 2011, the Advanced Land Observing Satellite (ALOS) Phased Array L-type Synthetic Aperture Radar (PALSAR) instrument acquired multi-temporal datasets under several environmental conditions and multiple configurations of look angle and polarization. The extensive archive of SAR backscatter observations over the forest test sites of Krycklan (boreal) and Remningstorp (hemi-boreal), Sweden, was used to assess the retrieval of stem volume at stand level. The retrieval was based on the inversion of a simple Water Cloud Model with gaps; single estimates of stem volume are then combined to obtain the final multi-temporal estimate. The model matched the relationship between the SAR backscatter and the stem volume under all configurations. The retrieval relative Root Mean Square Error (RMSE) differed depending upon environmental conditions, polarization and look angle. Stem volume was best retrieved in Krycklan using only HV-polarized data acquired under unfrozen conditions with a look angle of 34.3° (relative RMSE: 44.0%). In Remningstorp, the smallest error was obtained using only HH-polarized data acquired under predominantly frozen conditions with a look angle of 34.3° (relative RMSE: 35.1%). The relative RMSE was below 30% for stands >20 ha, suggesting high accuracy of ALOS PALSAR estimates of stem volumes aggregated at moderate resolution. [ABSTRACT FROM AUTHOR]

Longer wavelength synthetic aperture radars (SARs) are precious in the remote sensing of forested areas, being sensitive to contributions from the whole vegetation layer and from the ground below. The electromagnetic properties of such contributions are retrieved from multipolarimetric acquisitions, whereas their vertical structure is retrieved from multibaseline acquisitions through tomographic imaging. Combining baseline and polarization diversity provides most information, allowing the decomposition of the SAR signal into ground- and volume-only contributions. A formal treatment of this problem is provided with the algebraic synthesis technique, which extends the concepts of PolInSAR. The decomposition, however, is shown to be ambiguous in that different solutions are equally consistent with the data. The main goal of this paper is to discuss this topic in light of the experimental results from a tomographic and polarimetric analysis of the boreal forest within the Krycklan River catchment, Northern Sweden, investigated at P- and L-bands during the ESA campaign BioSAR 2008. Different solutions to the decomposition problem will be discussed by examining the corresponding vertical structures accessible through tomographic techniques. Elements are shown supporting the idea that ground-volume interactions play a nonnegligible role at P-band, and a solution is proposed to isolate contributions from direct volume backscattering. The retrieval of forest top height is discussed as well, leading to the conclusion that such parameter is robust against erroneous choices in the identification of volume-only contributions, thus corroborating the PolInSAR approach for the analysis of single-baseline data. [ABSTRACT FROM AUTHOR]

The multitemporal acquisition of images from the Sentinel-1 satellites allows continuous monitoring of a forest. This study focuses on the use of multitemporal C-band synthetic aperture radar (SAR) data to assess the results for forest type (FTY), between coniferous and deciduous forest, and tree species (SPP) classification. We also investigated the temporal stability through the use of backscatter from multiple seasons and years of acquisition. SAR acquisitions were pre-processed, histogram-matched, smoothed, and temperature-corrected. The normalized average backscatter was extracted for interpreted plots and used to train Random Forest models. The classification results were then validated with field plots. A principal component analysis was tested to reduce the dimensionality of the explanatory variables, which generally improved the results. Overall, the FTY classifications were promising, with higher accuracies (OA of 0.94 and K = 0.86) than the SPP classification (OA of 0.66 and K = 0.54). The use of merely winter images (OA = 0.89) reached, on average, results that were almost as good as those using of images from the entire year. The use of images from a single winter season reached a similar result (OA = 0.87). We conclude that multiple Sentinel-1 images acquired in winter conditions are feasible to classify forest types in a hemi-boreal Swedish forest. [ABSTRACT FROM AUTHOR]

Much attention is paid to the estimation of forest biomass-related variables (stem volume and above-ground biomass) with synthetic aperture radar (SAR) backscatter images because of the increasing number of sensors in space providing global and repeated coverage and the sensitivity of the backscattered intensity to forest properties. One of the most popular models used to estimate a biomass-related variable from SAR backscatter observations is the Water Cloud Model (WCM) because of its simplicity allowing for a straightforward retrieval. Nonetheless, a common feature of these estimates is the tendency to over- or underestimate specific ranges due to simplifying assumptions in the model. In this study, the WCM has been revisited by exploring pathways for a physically-based, Light Detection and Ranging (LiDAR)-aided, model parameterization at larger scale with the overall aim to reduce systematic retrieval errors associated with empirical assumptions in the model. The study was undertaken in Sweden where repeated observations of backscatter by the Advanced Land Observing Satellite (ALOS) Phased Array-type L-band Synthetic Aperture Radar (PALSAR) were available. The integration was prototyped in Sweden thanks to detailed allometries relating forest variables in the WCM. These were derived from spatially dense estimates of canopy density and vegetation height from observations by the Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) and measurements of height and stem volume from the Swedish National Forest Inventory (NFI). The SAR backscatter predicted by the revisited WCM was in strong agreement with the observations. When evaluated against stem volumes estimated from the NFI data, the SAR-based stem volumes presented strong dispersion at the pixel level. Average stem volume at the level of five or more pixels, i.e., for an area larger than 0.3 ha, were instead unbiased and similar to the average values obtained from the NFI data (relative root mean square error: 21.4%, estimation bias: 0.9 m3/ha and coefficient of determination: 0.67). This study demonstrates that the integration of allometries in the WCM effectively reduces estimation errors. The method here prototyped in Sweden qualifies to provide large-scale estimates of biomass-related variables using multiple observations of L-band backscatter with potential application worldwide. • Revision of the Water Cloud Model by complementing with forest allometries. • Integration of LiDAR metrics and forest inventory data to derive allometries. • Improved estimation of forest stem volume with revisited model. • Unbiased estimates of stem volume in Sweden with multiple ALOS PALSAR observations. • Retrieval error: 21%. [ABSTRACT FROM AUTHOR]