Cheikh Anta Diop University

Accurate nearshore bathymetry estimation remains a critical challenge, impacting coastal forecasting evolution assessments through the inaccuracies in both in-situ and remote sensing surveys. This article introduces the Satellite Derived Bathymetry (SDB) temporal correlation method, showcasing its ability in deriving accurate nearshore bathymetry from one minute spaceborne videos. The approach utilises correlation of pixel intensity time series, shifted in time and space, extracted from a frame stack within a defined correlation window. The resulting correlation is then projected using the Radon Transform to infer wave characteristics (celerity and wavelength) for the estimation of depth through wave linear dispersion. Moreover, the adaptation of the correlation window based on a first wavelength estimation provided a more focused assessment of the wavefield that reveals morphological features such as sandbars in the bathymetric estimation. The method’s capabilities using adapted correlation window is illustrated through its application to a metric resolution Jilin satellite video (57 s at 5 Hz) along the Saint-Louis coast in Senegal. Through this demonstration, the temporal correlation method is among the first SDB methods to successfully capture the submerged sandbar along a beach. Comparison against in-situ measurements conducted three years prior to the video acquisition shows a good agreement with a bias of 0.97 m within the initial 2 km of the cross-shore profile. Furthermore, the application of previously developed sky-glint surface elevation analysis on video pixel intensity, prior to the bathymetry estimation, significantly reduces the bias to 0.44 m in the Saint-Louis estimation. This article highlights the potential applications of future Earth observation satellite missions that will capture image sequences (or videos) such as CO3D (CNES/Airbus).

Adrien N. Klotz; Rafael Almar; Yohan Quenet; Erwin W.J. Bergsma; David Youssefi; Stephanie Artigues; Nicolas Rascle; Boubou Aldiouma Sy; Abdoulaye Ndour. Nearshore satellite-derived bathymetry from a single-pass satellite video: Improvements from adaptive correlation window size and modulation transfer function. Remote Sensing of Environment 2024, 315 .

Adrien N. Klotz, Rafael Almar, Yohan Quenet, Erwin W.J. Bergsma, David Youssefi, Stephanie Artigues, Nicolas Rascle, Boubou Aldiouma Sy, Abdoulaye Ndour. Nearshore satellite-derived bathymetry from a single-pass satellite video: Improvements from adaptive correlation window size and modulation transfer function. Remote Sensing of Environment. 2024; 315 ():.

Adrien N. Klotz; Rafael Almar; Yohan Quenet; Erwin W.J. Bergsma; David Youssefi; Stephanie Artigues; Nicolas Rascle; Boubou Aldiouma Sy; Abdoulaye Ndour. 2024. "Nearshore satellite-derived bathymetry from a single-pass satellite video: Improvements from adaptive correlation window size and modulation transfer function." Remote Sensing of Environment 315, no. : .

Diène Diégane Thiaré; Diégane Sarr; François Delattre; Philippe Giamarchi; Atanasse Coly. Direct and reverse micellar-enhanced photo-induced fluorescence determination of fenvalerate in senegalese surface and groundwater. Journal of Photochemistry and Photobiology A: Chemistry 2024, 456 .

Diène Diégane Thiaré, Diégane Sarr, François Delattre, Philippe Giamarchi, Atanasse Coly. Direct and reverse micellar-enhanced photo-induced fluorescence determination of fenvalerate in senegalese surface and groundwater. Journal of Photochemistry and Photobiology A: Chemistry. 2024; 456 ():.

Diène Diégane Thiaré; Diégane Sarr; François Delattre; Philippe Giamarchi; Atanasse Coly. 2024. "Direct and reverse micellar-enhanced photo-induced fluorescence determination of fenvalerate in senegalese surface and groundwater." Journal of Photochemistry and Photobiology A: Chemistry 456, no. : .

Enhancing food security in the Sahel through nature-based solutions is urgent given population growth, resource scarcity and climate change. Traditional agroforestry parklands are a farmer- and nature-based widespread form of ecological intensification which randomly integrates trees into crop fields. While most studies estimating crop yields in agroforestry have been conducted in controlled experimental settings, few have addressed the inherent variability in such highly heterogeneous systems. Thus, the purpose of this study is to benefit from a UAV-based proxy-sensing and machine learning approach to address the variability of pearl millet grain yield, according to the distance to randomly distributed trees in a traditional agroforestry system dominated by Faidherbia albida (i.e. groundnut basin of Senegal). 21 vegetation indices (VIs), 32 normalized difference texture indices (NDTIs) derived from multispectral drone images, and normalization variables for radiative conditions were used with yield data collected in 15 plots (around 1 ha each) and subplots (15 m ² each) displayed at 3 distances from the tree over five cropping seasons (2018 - 2022). In this context, the optimal phenological stage was determined for predicting pearl millet grain yield, which proved to be the pre-heading period. This period was used as the basis for our machine learning model training dataset in the subplots. Two models, Random Forest (RF) and Gradient Boosting Machine (GBM) were compared by combining VIs, NDTIs and normalization variables. GBM was the best-performing model, explaining 78% of observed pearl millet yield variability over five years in the subplots, with a RMSE of 16 g. m ⁻² . This study revealed that NDTIs calculated from red and green bands were more influential for yield estimation than those based on near-infrared. These results were subsequently used to predict yield in all plots, resulting in a mean relative error of 17.5% between yields estimated by the farmers and GBM-estimated yields. This approach represents a pathway to assessing the withinfield yield variability in highly heterogeneous agroforestry plots and to demonstrate, quantify and optimize tree benefits for ecological intensification.

Serigne Mansour Diene; Ibrahima Diack; Alain Audebert; Olivier Roupsard; Louise Leroux; Abdoul Aziz Diouf; Modou Mbaye; Romain Fernandez; Moussa Diallo; Idrissa Sarr. Improving pearl millet yield estimation from UAV imagery in the semiarid agroforestry system of Senegal through textural indices and reflectance normalization. IEEE Access 2024, PP, 1 -1.

Serigne Mansour Diene, Ibrahima Diack, Alain Audebert, Olivier Roupsard, Louise Leroux, Abdoul Aziz Diouf, Modou Mbaye, Romain Fernandez, Moussa Diallo, Idrissa Sarr. Improving pearl millet yield estimation from UAV imagery in the semiarid agroforestry system of Senegal through textural indices and reflectance normalization. IEEE Access. 2024; PP ():1-1.

Serigne Mansour Diene; Ibrahima Diack; Alain Audebert; Olivier Roupsard; Louise Leroux; Abdoul Aziz Diouf; Modou Mbaye; Romain Fernandez; Moussa Diallo; Idrissa Sarr. 2024. "Improving pearl millet yield estimation from UAV imagery in the semiarid agroforestry system of Senegal through textural indices and reflectance normalization." IEEE Access PP, no. : 1-1.