ETH-GQS: An estimation of geoid-to-quasigeoid separation over Ethiopia

The determination of accurate orthometric or normal heights remains one of the main challenges for the geodetic community in Ethiopia.These heights are required for geodetic and geodynamic scientific research as well as for extensive engineering applications.The main objective of this study is to estimate the geoid-to-quasi geoid separation(GQS)in Ethiopia(ETH-GQS).Such separation would be required for the conversion between geoid and quasigeoid models,which is mandatory for the determination of accurate geodetic heights in mountain regions.The airborne free-air gravity anomalies and the topo-graphic information retrieved from the SRTM3(Shuttle Radar Topography Mission of a spatial resolution 3 arc-second)digital elevation model were used to compute the ETH-GQS model according to the Sjoberg's strict formula for the geoid-to-quasigeoid separation.The ETH-GQS was then validated using GNSS-levelling data as well as geoid heights determined from different Global Geopotential Models(GGMs),namely the EGM2008,EIGEN-6C4 and GECO.The results reveal that the standard deviation of differences between the geoid heights obtained from the EIGEN-6C4 model and the geometric geoid heights obtained from GNSS-levelling data were improved by~75%(i.e.from~24 to~6 cm)when considering GQS values obtained from the ETH-GQS.

Combining machine learning,space-time cloud restoration and phenology for farm-level wheat yield prediction

Though studies showed the potential of high-resolution optical sensors for crop yield prediction,several factors have limited their wider application.The main factors are obstruction of cloud,identification of phenology,demand for high computing infrastructure and the complexity of statistical methods.In this research,we created a novel approach by combining four methods.First,we implemented the cloud restoration algorithm called gapfill to restore missed Normalized Difference Vegetation Index(NDVI)values derived from Sentinel-2 sensor(S2)due to cloud obstruction.Second,we created square tiles as a solution for high computing infrastructure demand due to the use of high-resolution sensor.Third,we implemented gapfill following critical crop phenology stage.Fourth,observations from restored images combined with original(from cloud-free images)values and applied for winter wheat prediction.We applied seven base machine learning as well as two groups of super learning ensembles.The study successfully applied gapfill on high-resolution image to get good quality estimates for cloudy pixels.Consequently,yield prediction accuracy increased due to the incorporation of restored values in the regression process.Base models such as Generalized Linear Regression(GLM)and Random Forest(RF)showed improved capacity compared to other base and ensemble models.The two models revealed RMSE of 0.001 t/ha and 0.136 t/ha on the holdout group.The twomodels also revealed consistent and better performance using scatter plot analysis across three datasets.The approach developed is useful to predict wheat yield at field scale,which is a rarely available but vital in many developmental projects,using optical sensors.