In this study, an unsupervised classification approach is used to investigate and characterize the spatial and temporal variability of MetOp-A ASCAT backscatter (σ◦) and the TUW SMR vegetation parameters across mainland France between 2007 and 2017. Currently, soil moisture data is retrieved from ASCAT backscatter measurements using the TU Wien Soil Moisture Retrieval (TUW SMR) approach. To correct for the influence of vegetation on soil moisture, two so-called ’vegetation parameters’ are also estimated from the backscatter measurements. These vegetation parameters are the slope (σ′) and curvature (σ′′) of a second-order Taylor polynomial which describes the incidence angle dependence of backscatter. Recently, Steele-Dunne et al. (2019) showed that the slope and curvature contain significant information about vegetation phenology and vegetation water dynamics across the North-American grasslands, suggesting that the vegetation parameters are a potentially valuable source of information on vegetation dynamics. This study further investigates the value of the vegetation parameters as a source of information about vegetation dynamics for land cover types present in France. 3492 ASCAT grid points were clustered using agglomerative hierarchical clustering based on σ′ and subsequently analysed. The results show that clusters based on σ′ are contiguous and can resemble distinct land cover features; areas like Paris and the Alps are clearly visible in cluster maps. While the clusters differ in terms of σ′ – which follows from a clustering based on σ′ – the results show that the clusters generally also have distinct σ◦ and σ′′ characteristics. This suggests that the clusters represent ’scattering surfaces’ that differ in terms of their seasonal scattering characteristics. It was found that grid points with a heterogeneous land cover footprint tend to have noisy seasonal backscatter signatures, while homogeneous land cover footprints have more recognizable seasonal behavior. Additionally, certain backscatter signatures tend to correspond to certain land cover footprints; in particular the agricultural area around Paris produced clear σ◦ , σ′, and σ′′ signatures corresponding to specific growth stages of wheat and the rapid land cover change during the agricultural season. In general, the results are consistent with the existing assumptions that σ′ is a measure for vegetation density and σ′′ is a measure for the relative dominance of ground-bounce and direct scattering from vertical vegetation constituents. Finally, clustering was performed on ten years of dynamically estimated σ′ and a measure for robustness was introduced to quantify the clustering certainty for each grid point. Robust grid points are found in areas that have relatively stable land cover like the Alps or Paris, suggesting that these areas exhibit predictable seasonal backscatter behavior with low interannual variability. Poor robustness scores are mainly found in north-west France, where land cover is heterogeneous and seasonal backscatter behavior is highly variable. This study confirms that the TUW SMR vegetation parameters contain valuable information about vegetation phenology across different land cover footprints. Furthermore, it was shown that unsupervised classification methods based on the vegetation parameters are able to identify areas with similar scattering characteristics, and are able to show how these areas change over time.

The Phetchaburi River in Phetchaburi province, Thailand, has a watershed with many different water resource projects. The surrounding farms rely on the Phetchaburi River for irrigation water and the drinking water companies rely on it as a source of water. However, the Phetchaburi basin has problems with yearly floods, salt intrusion and pollution. Water monitoring stations in the region are scarce. A new telemetering system has been put in place, but due to the cost of these stations they are few in number. This project presents a showcase for a cheap and robust water monitoring system in terms of both quantity through water level data and quality through various water quality parameters using apps on an android or iPhone device to gather and analyse the data. The app from Mobile Water Management (MWM) is used to measure the water level through reading a photo of a staff gauge. The Akvo app uses various methods like electronic devices and reading strips via a photo to measure several water quality parameters. It was proven that construction of the staff gauges needed for the MWM app is cheap and does not require highly skilled workers. The resulting data is reliable, if the app is handled by someone trained in handling the app, and/or the data that is created is checked by a trained person. The fact that the pictures taken by the app are uploaded to the database makes for easy verification of the data. This makes verification of telemetric data possible, which as it turns out is not always reliable when compared to the MWM data. The Akvo app has a similar advantage in the sense that verification of the data at a later moment is not only possible, but also easy. This eliminates several human errors in the data collection process and effectively increases the data quality. Right now, several RID officers are needed to collect this data. Using the Akvo app, the required manpower can be lowered. Data analysis shows that the Phetchaburi River has significant levels fecal contamination (E. coli) and issues with low oxygen concentrations at certain moments. For this reason, it is not recommended to use as recreational, fishing or irrigation water. The boundary between salt and freshwater is constantly changing depending on weather conditions and can cause serious problems for local farmers. When constructing the staff gauge there are multiple possible human errors that need to be avoided in order for the MWM app to work correctly. This mainly has to do with the placement of the staff gauge sticker, keeping it straight and unobstructed and also directed towards the user. It turned out that several of the Akvo strips are not working correctly. Other than that, taking data from many parameters can also be time consuming. We recommend that the RID looks into this method of data collection further, both as a cheap and easy way to expand their water monitoring network, and in the case of the MWM app to verify the effectiveness of the telemetering systems.