The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

Cálculo del área y volumen de agua de dos reservorios de Cuba Central usando métodos de sensores remotos. Una nueva perspectiva

[EN] The availability, quality and management of water constitute essential activities of national, regional and local governments and authorities. Historic annual rain (between 1961 and 2020) in Chambas River Basin (Central Cuba) was evaluated. Two remote sensing methods (Normalized Difference Water Index and RADAR images) were used to calculate the variation of water area and volumes of two reservoirs (Chambas II and Cañada Blanca) of Ciego de Ávila Province at end of wet and dry seasons from 2014-2021. The results showed that mean annual rain was 1330.9 ± 287.4 mm and it did not showed any significant tendency at evaluated period. For both reservoirs, mean water areas measured with two methods were 19 % and 8 % smaller than the mean water area reported by authorities for the same period. The static water storage capacity (water volume) of both reservoirs varied (as area) between seasons with the greatest volume in both reservoirs recorded in October of 2017 (30.5 million of m3 in Chambas II and 45.1 million of m3 in Cañada Blanca reservoir). Large deviations of water area and volumes occurred during the dry season (lower values) and the wet season of 2017 (influenced by rain associated to of Hurricane Irma) and wet season of 2020 (influenced by rain associated to tropical storm Laura). Calculated area volume models with significant statistical correlation are another useful tool that could be used to improve water management in terms of accuracy and to increase reliable results in cases where gauge measurements are scarce or not available.[ES] La disponibilidad, calidad y manejo del agua constituye actividades esenciales de los gobiernos y autoridades regionales y locales.  Fue evaluada La lluvia anual histórica (entre 1961 y 2020) de la Cuenca del Río Chambas. Para el cálculo de la variación de las áreas y volúmenes del agua en dos reservorios de la Provincia de Ciego de Ávila al término de las temporadas lluviosa y poco lluviosa entre 2014 y 2021 fueron usados dos métodos de sensores remotos (Índice Normalizado de Diferencia de Agua e imágenes del RADAR). Los resultados mostraron que la lluvia media anual fue 1330.9±287.4 mm y no mostró tendencia significativa en el período evaluado. Para ambos reservorios, las áreas promedio de agua medidas con los dos métodos fueron 19 % y 8 % menores que el área de agua reportadas por las autoridades para el mismo período. La capacidad estática de almacenamiento de agua (volumen de agua) de los dos reservorios varió (como el área) entre temporadas, con el mayor volumen determinado en ambos reservorios en octubre de 2017 (30.5 millones de m3 en Chambas II y 45.1 millones de m3 en Cañada Blanca). Grandes desviaciones de las áreas y volúmenes del agua ocurrieron durante la temporada poco lluviosa (menores valores) y la temporada lluviosa de 2017 (influenciada por las lluvias asociadas el huracán Irma) y la temporada lluviosa de 2020 (influenciada por la lluvia asociada a la tormenta Laura). Los modelos calculados para la relación área volumen con una significación estadística son otra herramienta útil que podría ser usada para mejorar el manejo del agua en términos de precisión y el incremento de resultados confiables en casos donde la medición de los niveles de agua son escasos o no están disponibles.Valero-Jorge, A.; González-De Zayas, R.; Alcántara-Martín, A.; Álvarez-Taboada, F.; Matos-Pupo, F.; Brown-Manrique, O. (2022). Water area and volume calculation of two reservoirs in Central Cuba using Remote Sensing Methods. A new perspective. Revista de Teledetección. (60):71-87. https://doi.org/10.4995/raet.2022.17770OJS71876

Surface water monitoring in small water bodies: potential and limits of multi-sensor Landsat time series

Hydrometric monitoring of small water bodies (1–10&thinsp;ha) remains rare, due to their limited size and large numbers, preventing accurate assessments of their agricultural potential or their cumulative influence in watershed hydrology. Landsat imagery has shown its potential to support mapping of small water bodies, but the influence of their limited surface areas, vegetation growth, and rapid flood dynamics on long-term surface water monitoring remains unquantified. A semi-automated method is developed here to assess and optimize the potential of multi-sensor Landsat time series to monitor surface water extent and mean water availability in these small water bodies. Extensive hydrometric field data (1999–2014) for seven small reservoirs within the Merguellil catchment in central Tunisia and SPOT imagery are used to calibrate the method and explore its limits. The Modified Normalised Difference Water Index (MNDWI) is shown out of six commonly used water detection indices to provide high overall accuracy and threshold stability during high and low floods, leading to a mean surface area error below 15&thinsp;%. Applied to 546 Landsat 5, 7, and 8 images over 1999–2014, the method reproduces surface water extent variations across small lakes with high skill (R2 = 0.9) and a mean root mean square error (RMSE) of 9300&thinsp;m2. Comparison with published global water datasets reveals a mean RMSE of 21&thinsp;800&thinsp;m2 (+134&thinsp;%) on the same lakes and highlights the value of a tailored MNDWI approach to improve hydrological monitoring in small lakes and reduce omission errors of flooded vegetation. The rise in relative errors due to the larger proportion and influence of mixed pixels restricts surface water monitoring below 3&thinsp;ha with Landsat (Normalised RMSE&thinsp; = &thinsp;27&thinsp;%). Interferences from clouds and scan line corrector failure on ETM+ after 2003 also decrease the number of operational images by 51&thinsp;%, reducing performance on lakes with rapid flood declines. Combining Landsat observations with 10&thinsp;m pansharpened Sentinel-2 imagery further reduces RMSE to 5200&thinsp;m2, displaying the increased opportunities for surface water monitoring in small water bodies after 2015.</p

Monthly drought monitoring of the surface water area of Sawa Lake, Iraq during 2016-2022 using remote sensing data

Drought is a common phenomenon in Iraq's environment, and the country has experienced severe drought events exacerbated by the threat of climate change (low rainfall and high temperatures) over the past two decades. Iraq is located in a semi-arid region whose water resources have been restricted and mostly shared with its neighbours. To investigate the effect of drought on the surface water area of Sawa lake, we analysed 52 Sentinel-2 images from May 2016 to July 2022 using an open-source SNAP toolbox to map the boundary of the surface-water body of the lake. The results indicate that the surface water area of Sawa lake has decreased significantly over the last six years with the most extreme decline beginning in May 2021, when the area of the lake lost about 51% of its initial size (May 2016). By March 2022, the lake had disappeared and about 96% of the water's surface area had been lost. To better understand the potential causes of droughts, further analysis has been conducted on the effects of precipitation and human activities (vegetation cover and Al-Samawah saltpan for salt production) on the lake. Investigations revealed that the rapid expansion of agricultural areas around the lake by 254% and the increase in salt production from the Al-Samawah saltpan by about 121% are among the direct causes of the drought. In addition, the results of the statistical test analysis between the estimated surface water area of Sawa lake and human activities were significant at a 95% level of confidence. The findings of this study can assist decision-makers to understand the interaction between human activities and the lake's environment to design a strategic plan for lake recovery and a sustainable water resource management system in southern Iraq

Remote Sensing Of The Cryosphere In High Mountain Asia

High Mountain Asia (HMA), often referred to as the "third pole" of the world because its high elevation glaciers, contains the largest amount of fresh water outside the polar ice sheets. The region's hydrology is strongly controlled by variations in the timing and distribution of runoff from snow and glacier melt. Recent improvements in remote sensing technologies and atmospheric / land surface models provides new approaches for assessing the HMA cryosphere. A recently-funded NASA program aims to apply these tools to advance understanding of HMA cryospheric processes. Here we present an overview of planned team activities during the three-year project

The use of remote sensing data to monitor pools along non-perennial rivers in the Western Cape, South Africa.

>Magister Scientiae - MScThe lack of monitoring of non-perennial rivers is a major problem for water resources management, despite their significance in satisfying agricultural, economic and recreational needs. Pools in non-perennial rivers are not monitored, due to their remoteness. Remote sensing offers a promising alternative for the monitoring of changes in water storage in these pools. This study aims to assess the extent to which remotely-sensed datasets can be used to monitor the spatio-temporal changes of water storage of pools along non-perennial rivers in the Western Cape. The objectives of this study are: (1) to determine a suitable image preprocessing and classification technique for detecting and monitoring surface water along nonperennial rivers, and (2) to describe the spatial and temporal changes of water availability of pools along non-perennial rivers, using remotely sensed datasets. The Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalised Difference Vegetation Index (NDVI), Automated Water Extraction Index for shadowed (AWEIsh) and non-shadowed regions (AWEInsh) and the Multi-Band Water Index (MBWI) classification techniques were investigated in this study, using the Sentinel-2 and Landsat 8 datasets. In-situ measurements were used to validate the satellite-derived datasets, while the use of high resolution aerial photography and Digital-Globe WorldView imagery were further compared to the results. The results suggested that the NDWI is the most suitable classification technique for identifying water in pools along non-perennial rivers throughout the Western Cape. The NDWI applied to the Sentinel-2 Top-of-Atmosphere (TOA) reflectance dataset had the highest overall accuracy of 85%, when compared to the Sentinel-2 Dark Object Subtraction 1 (DOS1) atmospheric correction, Sentinel-2 Sen2Cor atmospheric correction, Landsat 8 TOA reflectance and Landsat 8 DOS1 atmospheric correction datasets. The incorporation of atmospheric correction was shown to eliminate surface water pixels in many of the smaller pools

The Color of Rivers

Rivers are among the most imperiled ecosystems globally, yet we do not have broad-scale understanding of their changing ecology because most are rarely sampled. Water color, as perceived by the human eye, is an integrative measure of water quality directly observed by satellites. We examined patterns in river color between 1984 and 2018 by building a remote sensing database of surface reflectance, RiverSR, extracted from 234,727 Landsat images covering 108,000 kilometers of rivers > 60 m wide in the contiguous USA. We found 1) broad regional patterns in river color, with 56% of observations dominantly yellow and 38% dominantly green; 2) river color has three distinct seasonal patterns that were synchronous with flow regimes; 3) one third of rivers had significant color shifts over the last 35 years. RiverSR provides the first map of river color and new insights into macrosystems ecology of rivers