Martin Kanning

Coastal waters are one of the most vulnerable resources that require comprehensive investigation in space and time. One of the key factors for effective coastal monitoring is the use of remote sensing technologies. Since the Coastal Zone Color Scanner (CZCS) in 1978, a long list of space-borne missions had been successfully launched. However, those missions are limited to coastal waters applications. Despite a large number of missions, the existing systems are still facing similar challenges as four decades ago. Spatial and spectral data reconstruction and recovery a high resolution (HR) imagery data from a low resolution (LR) imaging is a challenging task in many applications. The most promising technique in the field of digital image processing is known as Super Resolution (SR). Many techniques focus on reconstructing information at the sub-pixel level and dividing the original LR space into pixels corresponding to the HR space. Other methods assume that a series of LR images (in time) of a scene scanned from different perspectives (affine) will provide SR. Alternative methods use different data sources and proper image algorithms. In most cases, SR methods will perform a learning process in which the system will try to identify the inherent redundancy in the natural data in order to retrieve HR information from LR based on a spatial correlation between the original images. The learning process can be significantly efficient by using the Convolutional Neural Network (CNN). CNN submit to training through a large dataset that preserves the scene’s characteristics. The flexibility afforded by CNN is learning nonlinear relationships when reconstructing a spatial characteristic from an LR image to HR image. The main aim of this study is to identify spectral features related to the coastal water and inland water variations at different spatial and temporal scale and integrate them with a multi-scale information system. The main objectives of the study are developing of spatial-temporal-spectral fusion approach for multi-source data collected from the same geographical site; creating a new method for single image reconstruction from non-complementary information scene. The proposed method measures HR given LR by a downscaling process by turning HR into an LR. The deterministic process calculated using a Gaussian filter and by a photographic-focused distribution function. The correlation coefficient (at the LR-pixel level) used as an inverse ratio to upscaling. The proposed architecture is based on a three-convolutional network. In the first stage, the convolution is directly applied to the LR data, and then another sub-pixel convolution layer is subtracted to generate SR data from LR data through an upscaling process. This study performed in two sites, (1) a training site in Israel, (2) a test site in Germany. The training site is shallow seawaters around Oren River, Israel and the test site is Alfsee inland water in Germany. The results in both sites are SR imagery with full Sentinel 2 spectral resolution and spatial resolution of 0.3 m.

The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400–1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R²_LAI = 0.79, RMSE_{LAI [m²m⁻²]} = 0.18, R²_CHL = 0.77, RMSE_{CHL [µg cm⁻²]} = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R²_yield = 0.88, RMSE_{yield [dt ha⁻¹]} = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.

The determination of soil texture and organic carbon across agricultural areas provides important information to derive soil condition. Precise digital soil maps can help to till agricultural fields with more accuracy, greater cost-efficiency and better environmental protection. In the present study, the laboratory analysis of sand, silt, clay and soil organic carbon (SOC) content was combined with hyperspectral image data to estimate the distribution of soil texture and SOC across an agricultural area. The aim was to identify regions with similar soil properties and derive uniform soil regions based on this information. Soil parameter data and corresponding laboratory spectra were used to calibrate cross-validated (leave-one-out) partial least squares regression (PLSR) models, resulting in robust models for sand (R² = 0.77, root-mean-square error (RMSE) = 5.37) and SOC (R² = 0.89, RMSE = 0.27), as well as moderate models for silt (R² = 0.62, RMSE = 5.46) and clay (R² = 0.53, RMSE = 2.39). The regression models were applied to Airborne Imaging Spectrometer for Applications DUAL (aisaDUAL) hyperspectral image data to spatially estimate the concentration of these parameters. Afterwards, a decision tree, based on the Food and Agriculture Organization (FAO) soil texture classification scheme, was developed to determine the soil texture for each pixel of the hyperspectral airborne data. These soil texture regions were further refined with the spatial SOC estimations. The developed method is useful to identify spatial regions with similar soil properties, which can provide a vital information source for an adapted treatment of agricultural fields in terms of the necessary amount of fertilizers or water. The approach can also be adapted to wider regions with a larger sample size to create detailed digital soil maps (DSMs). Further, the presented method should be applied to future hyperspectral satellite missions like Environmental Mapping and Analysis Program (EnMap) and Hyperspectral Infrared Imager (HyspIRI) to cover larger areas in shorter time intervals. Updated DSMs on a regular basis could particularly support precision farming aspects.