Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land

Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries.

Geomorphological patterns of remotely sensed methane hot spots in the Mackenzie Delta, Canada

We studied geomorphological controls on methane (CH4) hotspots in the Mackenzie Delta region in northern Canada using airborne imaging spectroscopy collected as part of the Arctic Boreal Vulnerability Experiment (ABoVE). Methane emissions hotspots were retrieved at ~25 m2 spatial resolution from a ~10,000 km2 AVIRIS-NG survey of the Mackenzie Delta acquired 31 July – 3 August 2017. Separating the region into the permafrost plateau and the lowland delta, we refined the domain wide power law of CH4 enhancements detected as a function of distance to standing water in different ecoregions. We further studied the spatial decay of the distance to water relationship as a function of land cover across the Delta. We show that geomorphology exerts a strong control on the spatial patterns of emissions at regional to sub-regional scales: compared to methane hotspots detected in the upland, we find that methane hotspots detected in the lowland have a more gradual power law curve indicating a weaker spatial decay with respect to distance from water. Spatial decay of CH4 hotspots in uplands is more than 2.5 times stronger than in lowlands, which is due to differences in topography and geomorphological influence on hydrology. We demonstrate that while the observed spatial distributions of CH4 follow expected trends in lowlands and uplands, these quantitatively complement knowledge from conventional wetland and freshwater CH4 mapping and modelling.

Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer

The retrieval of sun-induced fluorescence (SIF) from hyperspectral radiance data grew to maturity with research activities around the FLuorescence EXplorer satellite mission FLEX, yet full-spectrum estimation methods such as the spectral fitting method (SFM) are computationally expensive. To bypass this computational load, this work aims to approximate the SFM-based SIF retrieval by means of statistical learning, i.e., emulation. While emulators emerged as fast surrogate models of simulators, the accuracy-speedup trade-offs are still to be analyzed when the emulation concept is applied to experimental data. We evaluated the possibility of approximating the SFM-like SIF output directly based on radiance data while minimizing the loss in precision as opposed to SFM-based SIF. To do so, we implemented a double principal component analysis (PCA) dimensionality reduction, i.e., in both input and output, to achieve emulation of multispectral SIF output based on hyperspectral radiance data. We then evaluated systematically: (1) multiple machine learning regression algorithms, (2) number of principal components, (3) number of training samples, and (4) quality of training samples. The best performing SIF emulator was then applied to a HyPlant flight line containing at sensor radiance information, and the results were compared to the SFM SIF map of the same flight line. The emulated SIF map was quasi-instantaneously generated, and a good agreement against the reference SFM map was obtained with a R2 of 0.88 and NRMSE of 3.77%. The SIF emulator was subsequently applied to 7 HyPlant flight lines to evaluate its robustness and portability, leading to a R2 between 0.68 and 0.95, and a NRMSE between 6.42% and 4.13%. Emulated SIF maps proved to be consistent while processing time was in the order of 3 min. In comparison, the original SFM needed approximately 78 min to complete the SIF processing. Our results suggest that emulation can be used to efficiently reduce computational loads of SIF retrieval methods.

Mapping the spatial distribution of NO₂ with in situ and remote sensing instruments during the Munich NO₂ imaging campaign

We present results from the Munich NO2 imaging campaign (MuNIC) where nitrogen dioxide (NO2) near-surface concentrations (NSC) and vertical column densities (VCD) were measured with stationary, mobile and airborne in situ and remote sensing instruments. The most intensive day of the campaign was 7 July 2016, when the NO2 VCD field was mapped with the Airborne Prism Experiment (APEX) imaging spectrometer. The spatial distribution of APEX VCDs was rather smooth with a horizontal gradient between lower values upwind and higher values downwind of the city center. The NO2 map had no pronounced source signatures except for the plumes of two combined heat and power plants (CHP). The APEX VCDs agree well with mobile MAX-DOAS observations from two vehicles conducted in the same afternoon (r = 0.55). In contrast to the VCDs, mobile NSC measurements revealed high spatial and temporal variability along the roads with highest values in congested areas and tunnels. The NOx emissions of the two CHP plants were estimated from the APEX observations using a mass-balance approach. The estimates are higher than reported emissions, but uncertainties are high because the campaign day was unstable and convective, resulting in low and highly variable wind speeds. The NOx emission estimates are consistent with CO2 emissions determined from two ground-based FTIR instruments operated near one CHP plant. We conclude that airborne imaging spectrometers are well suited to map the spatial distribution of NO2 VCDs over large areas. The emission plumes of point sources can be detected in the APEX observations, but accurate flow fields are essential to estimate emissions with sufficient accuracy. The application of airborne imaging spectrometers for studying NSCs, for example as input for epidemiological studies, is less straight forward and requires to account for the non-trivial relationship between VCDs and NSCs.

Assessment of recommended and acceptable image quality indicators’ values, based on materials, obtained with various aerial surveying systems for mapping purposes

The results of fine quality indicators assessment for images, obtained with various aerial surveying systems for mapping purposes are presented. The values of parameters that characterize technical conditions, technical parameters of airborne imaging and technologic settings for imagery postprocessing, natural conditions of aerial surveying are determined. The author presents a comparative analysis of fine quality indicators assessment both for photos, the quality of which was found to be satisfactory as production technical control results and those rejected due to some relevant indicators (haze, blurring, etc) and accepted unsuitable for creating cartographic products based on them. It is shown, that some quality indicators, such as spatial resolution, sharpness and the degree (standard deviation) of random noise, are advisable to be determined at performing factory calibration of aerial cameras and included in certificate as well as other calibration parameters as metrological characteristics of the obtained imagery. Acceptable values of fine quality indicators, obtained during joint analysis of previous theoretical researches results and experimental verification, based on images, obtained by various aerial surveying systems for mapping purposes were generalized and recommended during the research.

Impact of 3D radiative transfer on airborne NO₂ imaging remote sensing over cities with buildings

Airborne imaging remote sensing is increasingly used to map the spatial distribution of nitrogen dioxide (NO2) in cities. Despite the small ground-pixel size of the sensors, the measured NO2 distributions are much smoother than one would expect from high-resolution model simulations of NO2 over cities. This could partly be caused by 3D radiative transfer effects due to observation geometry, adjacency effects and effects of buildings. Here, we present a case study of imaging a synthetic NO2 distribution for a district of Zurich using the 3D MYSTIC (Monte carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres) solver of the libRadtran radiative transfer library. We computed NO2 slant column densities (SCDs) using the recently implemented 3D-box air mass factors (3D-box AMFs) and a new urban canopy module to account for the effects of buildings. We found that for a single ground pixel (50 m × 50 m) more than 50 % of the sensitivity is located outside of the pixel, primarily in the direction of the main optical path between sun, ground pixel, and instrument. Consequently, NO2 SCDs are spatially smoothed, which results in an increase over roads when they are parallel to the optical path and a decrease otherwise. When buildings are included, NO2 SCDs are reduced on average by 5 % due to the reduced sensitivity to NO2 in the shadows of the buildings. The effects of buildings also introduce a complex pattern of variability in SCDs that would show up in airborne observations as an additional noise component (about 12 µmol m−2) similar to the magnitude of typical measurement uncertainties. The smearing of the SCDs cannot be corrected using 1D-layer AMFs that assume horizontal homogeneity and thus remains in the final NO2 map. The 3D radiative transfer effects by including buildings need to be considered to compute more accurate AMFs and to reduce biases in NO2 vertical columns obtained from high-resolution city-scale NO2 remote sensing.