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• W4244544104 abstract "&lt;p&gt;Grasslands are a predominant land cover form, responsible for ecosystem services such as slope stabilization, water and carbon storage or fodder provision for livestock. At the same time, altering climatic effects and human activities have influenced the natural growth pattern and condition of alpine grasslands over the past decades. Mountainous areas are projected to be particularly impacted by climatic changes and management practices. Nowadays, a wide variety and different installations of Earth observation systems are available to monitor and predict grassland growth and status, to evidence ecosystem services such as biodiversity, the fodder availability or to highlight the effectiveness of management practices.&lt;/p&gt;&lt;p&gt;In this study Support Vector Regression (SVR) and Random Forest (RF) machine learning techniques were used to estimate the aboveground biomass, plant water content and the leaf area index (LAI). As input, we combined hyperspectral imagery from field spectrometers, optical Sentinel-2 data as well as SAR data from Sentinel-1. The models were tested targeting approximately 250 biomass and LAI samples taken from 2017 to 2020 on grasslands in the Mazia/Matsch valley, located in South Tyrol (Italy). The dataset was divided based on grassland type (meadow and pasture) the growth period (up to three growth periods a year for meadows), as well as the year, to analyze the modelled predictions based on the growing stage of the vegetation.&lt;/p&gt;&lt;p&gt;The results obtained using the integration of the datasets are very promising in the meadow, with R&lt;sup&gt;2&lt;/sup&gt; reaching ranging from 0.5 to 0.8 for the biomass and from 0.6 to 0.8 for the LAI retrieval. At the same time, the division in growth phases shows a slightly higher correlation than during the first and second growing periods, indicating that the irregular growth after the last harvest of the year affects the capability of prediction of LAI and above-ground biomass. However, the predictability worsens on high biomass and LAI values before the harvest takes place, thus indicating an impact of the saturation in the optical data and revealing the need for additional data sources or an alternated weighting of the predictors in the models. The results on the pasture show that the prediction of LAI and biomass with optical and SAR data is difficult to achieve (mean R&lt;sup&gt;2&lt;/sup&gt; ranging from 0.3 to 0.4) given the natural heterogeneity in growth within the test area. Additional datasets such as cattle movement or the slope information could represent a valuable source of information for further LAI and biomass growth analyses in mountainous areas.&lt;/p&gt;&lt;p&gt;This research is part of the 2019-2021 project &amp;#8216;Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone&amp;#8217;, funded by ASI under grant agreement n.2018-37-HH.0.&lt;/p&gt;" @default.
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• W4244544104 date "2021-03-04" @default.
• W4244544104 modified "2023-09-29" @default.
• W4244544104 title "Multisensor SAR and optical estimation of grassland above-ground biomass and LAI: a case study for the Mazia valley in South Tyrol" @default.
• W4244544104 doi "https://doi.org/10.5194/egusphere-egu21-11932" @default.
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