No abstract

No abstract

<p>Combining data from in situ measurements, remote sensing and models can provide new insights on global vegetation dynamics, specifically on the role of vegetation in the carbon and water cycles. Here we will demonstrate the benefits of combining Metop Advanced SCATterometer (ASCAT) C-band radar backscatter observations with in-situ and model data for monitoring vegetation dynamics and constraining parameters in terrestrial carbon stock and flux simulations. </p> <p>The slope of the relation between backscatter and incidence angle of Metop ASCAT data is sensitive to vegetation dynamics over the Amazon region and North-American grasslands, as demonstrated in previous studies by Petchiappan et al. (2022) and Steele-Dunne et al. (2018).  Here we use the slope in combination with in-situ observations to analyze vegetation dynamics over the ICOS site in Sodankyla. Results from this boreal forest region in Northern Finland show that slope dynamics are influenced by freezing temperatures and snow, hindering monitoring of vegetation dynamics during these times. During periods without freezing temperatures and snow, the slope reveals phenological changes both in terms of seasonal changes and anomalies. During the 2018 drought, positive anomalies in slope were found, consistent with results found by Bastos et al., (2020), who demonstrated that increased temperature, drier than average conditions and increased radiation led to increased vegetation growth as modelled with several vegetation models and observed with SMOS Vegetation Optical Depth.</p> <p>To benefit terrestrial carbon cycle modelling and science, ASCAT slope can be assimilated directly into land surface models to constrain states and parameters related to the fast and slow water and carbon fluxes. Results from the ESA Land Carbon Constellation project will be presented to demonstrate that the measurement operator required for assimilation can be determined using several approaches. </p> <p>Bastos, A., Ciais, P., Friedlingstein, P., Sitch, S., Pongratz, J., Fan, L., Wigneron, J.P., Weber, U., Reichstein, M., Fu, Z., Anthoni, P., Arneth, A., Haverd, V., Jain, A.K., Joetzjer, E., Knauer, J., Lienert, S., Loughran, T., McGuire, P.C., Tian, H., Viovy, N., Zaehle, S., 2020. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Science Advances 6, eaba2724. https://doi.org/10.1126/sciadv.aba2724</p> <p>Petchiappan, A., Steele-Dunne, S.C., Vreugdenhil, M., Hahn, S., Wagner, W., Oliveira, R., 2022. The influence of vegetation water dynamics on the ASCAT backscatter-incidence angle relationship in the Amazon. Hydrology and Earth System Sciences 26, 2997–3019. https://doi.org/10.5194/hess-26-2997-2022</p> <p>Steele-Dunne, S.C., Hahn, S., Wagner, W., Vreugdenhil, M., 2019. Investigating vegetation water dynamics and drought using Metop ASCAT over the North American Grasslands. Remote Sensing of Environment 224, 219–235.</p>

<div> <div> <p><span data-contrast="auto">Seewinkel salt pans are a unique wetland ecosystem in eastern Austria that serves as habitat for a diverse range of e.g. birds and halophilic species. Due to groundwater drainage by channels and wells, the salt pans are in an increasingly vulnerable state as they are decisively conditioned by duration and timing of water abundance. However, water gauge data are merely given for three salt pans. The dynamics of</span><span data-contrast="auto"> salt pans in Seewinkel, locally referred to as </span><em><span data-contrast="auto">Salzlacken</span></em><span data-contrast="auto">, remain insufficiently understood in the context of continuously changing seasonal and long-term hydrological, meteorological, and climatological patterns. Based on previous results on salt pan mapping and monitoring, this work advances inundation state prediction for 34 salt pans by using high-resolution remote sensing data and machine learning methods. The random forest classification models build on hydrological and meteorological predictors in 12-monthly temporal resolution, as, e.g., reduced precipitation sums during the preceding winter season affect the recharge rates of salt pans and groundwater and, as a result, drying state in summer. Four models predict summer drying state at respective four points in time, namely in March, April, May, and June of each year between 1984 and 2022. We first show that remotely sensed water extent products, retrieved from Landsat data can serve as a target variable for data-driven modelling of small-scale salt pan water-dynamics. Secondly, we show that the applied models can successfully predict summer drying state and inundation periods of individual salt pans achieving a maximum F1-score of 0.81. Finally, it is demonstrated that very similar model results can be attained without in-situ groundwater measurements. Research based on water gauge measurements with similar model-designs has been done in the context of lakes, whereas the combination of satellite-derived water extent and salt pans, especially for ecosystems of small size, remains underrepresented. As the data retrieval in this work is based on global and freely available remote sensing data, this method is transferable to comparable salt pan ecosystems in other parts of the world.</span><span data-ccp-props="{"> </span></p> </div> </div>

<div> <div> <p><span data-contrast="auto">The increasing frequency and intensity of severe droughts over recent decades have significantly impacted crop production in the Pannonian Basin in southeastern Europe. Related crop yield losses can be substantial and require logistic compensation on an international level. To plan such compensations, seasonal crop yield forecasts have proven to be a valuable tool to support decision-makers in taking timely action. However, the impact of severe droughts on crop yields is often underestimated by such forecasts. To address this issue, we developed a maize and wheat yield forecasting system based on extreme-gradient-boosting machine learning for 42 regions in the Pannonian Basin. The used predictors describe vegetation state, weather, and soil moisture conditions derived from Earth observation, reanalysis, in-situ data, and seasonal weather forecasts. The wide range of predictors was selected to represent the state of the crops and the conditions they are facing and are expected to face. We expected it to be crucial, especially during severe drought years, to provide the model with sufficient information about the drought and its impacts. Afterwards, the model was validated, with a focus on drought years.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559740":240}"> </span></p> </div> <div> <p><span data-contrast="auto">Our results show that crop yield anomaly estimates in the two months preceding harvest have better performance than earlier in the year (relative root mean square errors below 17%) in all years. The models have their clear strength in forecasting interannual variabilities but struggle to forecast differences between regions within individual years. This is related to spatial autocorrelations and a lower spatial than temporal variability of crop yields. In years of severe droughts, there is a clear improvement in the forecasts with a 2-month lead time over longer forecasts too. The crop yield losses remain underestimated, but the wheat model performs in drought years better than for average years with errors below 12%. The errors of the maize forecasts in drought years are larger than for non-drought years: 30% two months ahead and 20% one month ahead. The feature importance analysis shows that in general wheat yield anomalies are controlled by temperature and maize by water availability during the last two months before harvest. In severe drought years, soil moisture is the most important predictor for the maize model and the seasonal temperature forecast becomes key for wheat forecasts two months before harvest. Going forward, a finer spatial resolution of the predictors will be tested </span><span data-contrast="auto">to better distinguish the yields between the different regions. In addition, longer time-series of crop yield data, including more data during severe drought years, will help to test the findings of this study.</span><span data-ccp-props="{"201341983":1,"335551550":1,"335551620":1,"335559740":255}"> </span></p> </div> </div>