Spatial predictions of near-surface air temperature ( $T_{air}$ ) in Antarctica are required as baseline information for a variety of research disciplines. Since the network of weather stations in Antarctica is sparse, remote sensing methods have large potential due to their capabilities and accessibility. Based on the MODIS land surface temperature (LST) data, $T_{air}$ at the exact time of satellite overpass was modelled at a spatial resolution of 1 km using data from 32 weather stations. The performance of a simple linear regression model to predict $T_{air}$ from LST was compared to the performance of three machine learning algorithms: Random Forest (RF), generalized boosted regression models (GBM) and Cubist. In addition to LST, auxiliary predictor variables were tested in these models. Their relevance was evaluated by a Cubist-based forward feature selection in conjunction with leave-one-station-out cross-validation to reduce the impact of spatial overfitting. GBM performed best to predict $T_{air}$ using LST and the month of the year as predictor variables. Using the trained model, $T_{air}$ could be estimated with a leave-one-station-out cross-validated $R^2$ of 0.71 and a RMSE of 10.51 ${}^{\circ }$ C. However, the machine learning approaches only slightly outperformed the simple linear estimation of $T_{air}$ from LST ( $R^2$ of 0.64, RMSE of 11.02 ${}^{\circ }$ C). Using the trained model allowed creating time series of $T_{air}$ over Antarctica for 2013. Extending the training data by including more years will allow developing time series of $T_{air}$ from 2000 on.

Sustainable land management requires reliable information about soil hydraulic properties. Among these properties, available water-holding capacity (AWC) is a key attribute, as it quantifies the amount of water available for plants that the soil can hold. Since direct measurements of AWC are costly, pedotransfer functions (PTF) are often used to estimate AWC, leveraging statistical relationships with properties that are easier to measure, such as texture, bulk density, and organic carbon content. This study evaluates visible near-infrared spectroscopy (vis-NIR) as an alternative approach to predict volumetric water content at field capacity (FC) and permanent wilting point (PWP) — AWC being the difference between PWP and FC. A suite of 970 vis-NIR soil spectra, recorded from air-dried, 2-mm, sieved soil samples, were associated with FC and PWP analytical data obtained from New Zealand’s National Soils Database. Partial least squares (PLS) regression and support vector machines on PLS latent variables (PLS-SVM) were used for spectroscopic modelling. With root mean squared errors below 7% and 5% for FC and PWP, respectively, our results indicate that vis-NIR spectroscopy can be used to quantitatively predict volumetric water content at FC and PWP.