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1 Tarpanelli, A.; Santi, E.; Tourian, M. J.; Filippucci, P.; Amarnath, Giriraj; Brocca, L. 2019. Daily river discharge estimates by merging satellite optical sensors and radar altimetry through artificial neural network. IEEE Transactions on Geoscience and Remote Sensing, 57(1):329-341. [doi: https://doi.org/10.1109/TGRS.2018.2854625]
Rivers ; Discharges ; Estimation ; Water levels ; Remote sensing ; Satellite imagery ; Landsat ; Moderate Resolution Imaging Spectroradiometer ; Neural networks ; Radar ; Performance indexes ; Time series analysis ; Case studies / Nigeria / Italy / Niger River / Benue River / Po River / Lokoja / Pontelagoscuro
(Location: IWMI HQ Call no: e-copy only Record No: H048997)
(2.81 MB)
Thanks to the large number of satellites, the multimission approach is becoming a viable method to integrate measurements and intensify the number of samples in space and time for monitoring the earth system. In this paper, we merged data from different satellite missions, optical sensors, and altimetry, for estimating daily river discharge through the application of the artificial neural network (ANN) technique. ANN was selected among other retrieval techniques because it offers an easy but effective way of combining input data from different sources into the same retrieval algorithm. The network is trained in a calibration period and validated in an independent period against in situ observations of river discharge for two gauging sites: Lokoja along the Niger River and Pontelagoscuro along the Po River. For optical sensors, we found that the temporal resolution is more important than the spatial resolution for obtaining accurate discharge estimates. Our results show that Landsat fails in the estimation of extreme events by missing most of the peak values due to its long revisit time (14–16 days). Better performances are obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer. Radar altimetry provides results in between MODIS-TERRA and MODIS-AQUA at Lokoja, whereas it outperforms all single optical sensors at Pontelagoscuro. The multimission approach, involving optical sensors and altimetry, is found the most reliable tool to estimate river discharge with a relative root-mean-square error of 0.12% and 0.27% and Nash-Sutcliffe coefficient of 0.98 and 0.83 for the Niger and Po rivers, respectively.
2 Ramat, G.; Santi, E.; Paloscia, S.; Fontanelli, G.; Pettinato, S.; Santurri, L.; Souissi, N.; Da Ponte, E.; Wahab, M. M. A.; Khalil, A. A.; Essa, Y. H.; Ouessar, M.; Dhaou, H.; Sghaier, A.; Hachani, A.; Kassouk, Z.; Chabaane, Z. L. 2023. Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia. European Journal of Remote Sensing, 56(1):2157335. [doi: https://doi.org/10.1080/22797254.2022.2157335]
Remote sensing ; Techniques ; Water management ; Climate change ; Monitoring ; Drought ; Vegetation ; Soil moisture ; Neural networks ; Semiarid zones ; Evapotranspiration ; Precipitation ; Moderate resolution imaging spectroradiometer ; Case studies / Egypt / Tunisia / Mediterranean region
(Location: IWMI HQ Call no: e-copy only Record No: H052192)
https://www.tandfonline.com/doi/epdf/10.1080/22797254.2022.2157335?needAccess=true
https://vlibrary.iwmi.org/pdf/H052192.pdf
https://vlibrary.iwmi.org/pdf/H052192.pdf
(25.90 MB) (25.9 MB)
This study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data.
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