Small reservoirs play an important role in mining, industries, and agriculture, but storage levels or stage changes are very dynamic. Accurate and up-to-date maps of surface water storage and distribution are invaluable for informing decisions relating to water security, flood monitoring, and water resources management. Satellite remote sensing is an effective way of monitoring the dynamics of surface waterbodies over large areas. The European Space Agency (ESA) has recently launched constellations of Sentinel-1 (S1) and Sentinel-2 (S2) satellites carrying C-band synthetic aperture radar (SAR) and a multispectral imaging radiometer, respectively. The constellations improve global coverage of remotely sensed imagery and enable the development of near real-time operational products. This unprecedented data availability leads to an urgent need for the application of fully automatic, feasible, and accurate retrieval methods for mapping and monitoring waterbodies. The mapping of waterbodies can take advantage of the synthesis of SAR and multispectral remote sensing data in order to increase classification accuracy. This study compares automatic thresholding to machine learning, when applied to delineate waterbodies with diverse spectral and spatial characteristics. Automatic thresholding was applied to near-concurrent normalized difference water index (NDWI) (generated from S2 optical imagery) and VH backscatter features (generated from S1 SAR data). Machine learning was applied to a comprehensive set of features derived from S1 and S2 data. During our field surveys, we observed that the waterbodies visited had different sizes and varying levels of turbidity, sedimentation, and eutrophication. Five machine learning algorithms (MLAs), namely decision tree (DT), k-nearest neighbour (k-NN), random forest (RF), and two implementations of the support vector machine (SVM) were considered. Several experiments were carried out to better understand the complexities involved in mapping spectrally and spatially complex waterbodies. It was found that the combination of multispectral indices with SAR data is highly beneficial for classifying complex waterbodies and that the proposed thresholding approach classified waterbodies with an overall classification accuracy of 89.3%. However, the varying concentrations of suspended sediments (turbidity), dissolved particles, and aquatic plants negatively affected the classification accuracies of the proposed method, whereas the MLAs (SVM in particular) were less sensitive to such variations. The main disadvantage of using MLAs for operational waterbody mapping is the requirement for suitable training samples, representing both water and non-water land covers. The dynamic nature of reservoirs (many reservoirs are depleted at least once a year) makes the re-use of training data unfeasible. The study found that aggregating (combining) the thresholding results of two SAR and multispectral features, namely the S1 VH polarisation and the S2 NDWI, respectively, provided better overall accuracies than when thresholding was applied to any of the individual features considered. The accuracies of this dual thresholding technique were comparable to those of machine learning and may thus offer a viable solution for automatic mapping of waterbodies.

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

Precise and accurate delineation of flooding areas with synthetic aperture radar (SAR) and multi-spectral (MS) data is challenging because flooded areas are inherently heterogeneous as emergent vegetation (EV) and turbid water (TW) are common. We addressed these challenges by developing and applying a new stepwise sequence of unsupervised and supervised classification methods using both SAR and MS data. The MS and SAR signatures of land and water targets in the study area were evaluated prior to the classification to identify the land and water classes that could be delineated. The delineation based on a simple thresholding method provided a satisfactory estimate of the total flooded area but did not perform well on heterogeneous surface water. To deal with the heterogeneity and fragmentation of water patches, a new unsupervised classification approach based on a combination of thresholding and segmentation (CThS) was developed. Since sandy areas and emergent vegetation could not be classified by the SAR-based unsupervised methods, supervised random forest (RF) classification was applied to a time series of SAR and co-event MS data, both combined and separated. The new stepwise approach was tested for determining the flood extent of two events in Italy. The results showed that all the classification methods applied to MS data outperformed the ones applied to SAR data. Although the supervised RF classification may lead to better accuracies, the CThS (unsupervised) method achieved precision and accuracy comparable to the RF, making it more appropriate for rapid flood mapping due to its ease of implementation.

The Caprivi basin in Namibia has been affected by severe flooding in recent years resulting in deaths, displacements and destruction of infrastructure. The negative consequences of these floods have emphasized the need for timely, accurate and objective information about the extent and location of affected areas. Due to the high temporal variability of flood events, Earth Observation (EO) data at high revisit frequency is preferred for accurate flood monitoring. Currently, EO data has either high temporal or coarse spatial resolution. Accurate methodologies for the estimation and monitoring of flooding extent using coarse spatial resolution optical image data are needed in order to capture spatial details in heterogeneous areas such as Caprivi. The objective of this work was the retrieval of the fractional abundance of water ( ${\gamma }_w$ ) by applying a new spectral indices-based unmixing algorithm to Medium Resolution Imaging Spectrometer Full Resolution (MERIS FR) data using a minimum number of spectral bands. These images are technically similar to the OLCI image data acquired by the Sentinel-3 satellite, which are to be systematically provided in the near future. The normalized difference wetness index (NDWI) was applied to delineate the water surface and combined with normalized difference vegetation index (NDVI) to account for emergent vegetation within the water bodies. The challenge to map flooded areas by applying spectral unmixing is the estimation of spectral endmembers, i.e., pure spectra of land cover features. In our study, we developed and applied a new unmixing method based on the use of an ensemble of spectral endmembers to capture and take into account spectral variability within each endmember. In our case study, forty realizations of the spectral endmembers gave a stable frequency distribution of ${\gamma }_w$ . Quality of the flood map derived from the Envisat MERIS (MERIS) data was assessed against high (30 m) spatial resolution Landsat Thematic Mapper (TM) images on two different dates (17 April 2008 and 22 May 2009) during which floods occurred. The findings show that both the spatial and the frequency distribution of the ${\gamma }_w$ extracted from the MERIS data were in good agreement with the high-resolution TM retrievals. The use of conventional linear unmixing, instead, applied using the entire available spectra for each image, resulted in relatively large differences between TM and MERIS retrievals.

This study on a large irrigation scheme in Morocco addressed a two-fold question: (a) does the local water management authority adapt water releases to atmospheric water demand ET₀-P? and (b) does crop actual evapotranspiration respond to inter- and intra-annual variability in water releases? We have evaluated the inter-annual variability of ET₀-P during the period 1992–2017 and compared its anomalies (i.e., deviations from average) with anomalies in annual water release. Overall, it appeared that anomalies in water release were consistent with anomalies in ET₀-P. The actual evapotranspiration (ET_a) was estimated using a time series of multi-spectral satellite image data by applying the Surface Energy Balance (SEBAL) algorithm in a dry, wet, and reference year. We have determined the quartiles of the distribution of the ET₀-P time series to identify these three years. The dry year was 2015–2016, the wet year was 2014–2015, and the reference (median of ET₀-P) was 2013–2014. Finally, we compared seasonal and annual anomalies in ET₀-P, ET_a and release, W_d of irrigation water. In the period 1992–2017, the relative anomalies in ET₀-P and W_d were similar only in 2000–2001 and 2015–2016. The analysis of anomalies in water inflow and stocks confirmed the response in increased W_d following wet years with higher inflow and replenishment of the reservoir. The response of crop water use to the irrigation water supply was evaluated by comparing anomalies in the ratio of actual to maximum ET, i.e., ET_a/ET_c with anomalies in W_d. As regards the Sidi Bennour, Faregh, and Gharbia districts, the relative anomalies in ET_a/ET_c and W_d were consistent, i.e., they had the same sign and comparable magnitude. Overall, the study shows that water delivery W_d responds to inter-annual variability in the pre-irrigation season water inflows into the reservoirs, rather than in ET₀-P. On the other hand, actual crop water use, i.e., ET_a/ET_c, does respond to inter- and intra-annual variability in W_d. This evidence suggests that there is scope for adaptive water management based on a flexible adaptation of water release to inter- and intra-annual variability in water demand.

A protected natural area in the Emilia Romagna region, Northern Italy is threatened by hydro-meteorological hazards, particularly sea storms. In the last 50 years the northern part of the Bellocchio Park (Sacca Bellocchio II Nature Reserve, Site code EUAPP0072 - Ferrara, Italy)  was interested by an intensive urbanization (Lido di Spina) with the realization of infrastructures, e.g. roads and residential settlements. This land use change led to the construction of embankments and to the conversion of wetlands. These modifications, in combination to even more frequent storm surge events increased coastal erosion. In addition, inland flooding caused by storm surges acts with the reduction of the lagoon and the increase of soil salinity. As an example, the last event occurred in December 2020  eroded a large portion of the Bellocchio beach.   Co-design, co-development and deployment of NBS solutions to reduce storm surge risk in the Bellocchio Park is one of the objectives of the H2020 project OPEn-air laboRAtories for Nature baseD solUtions to Manage environmental risks (OPERANDUM). BellocchioBellochio park is in fact one of the 10 Open Air Laboratories (OAL) where the evidence of mitigation of hydro-meteorological risk by NBS will be demonstrated by the combination of different models, approaches and data.During the co-design process in the Bellocchio park, potential deployment locations of sand dunes have been identified in collaboration with local authorities devoted to the management of the natural area and to the coast defense (CB and ARSTePC-RER) and an environmental engineering consultant assisting Arpae (IRIS sas). Field visits were devoted to the analysis of the environmental features, strengths and weaknesses of candidate sites.This work aims to explore the usefulness of the combined use of multisource remote sensing and modeling in decision making during the co-design process of a NBS. The impacts of the most intense extreme storm surge events in the last 30 years have been documented by delineating flooded areas along the coast using Synthetic Aperture Radar and Multispectral image data. Coastal erosion has been also described by means of change detection analysis and very high resolution multispectral EO data. This screening has given a picture of areas at the risk, i.e. the area most likely to be affected by storm-surge events. Auxiliary data like Digital Terrain Models has been assimilated in a dedicated model to produce flood maps under different scenarios, i.e. different locations and size of NBS and different intensities of storm surge.  The integrated analysis was helpful in defining the priority sites, among the ones defined by the stakeholders and engineers, in term of effectiveness for storm surge risk reduction.