Remote Sensing for Soil Organic Carbon Mapping and Monitoring

[no abstract available

Analyses of the Impact of Soil Conditions and Soil Degradation on Vegetation Vitality and Crop Productivity Based on Airborne Hyperspectral VNIR–SWIR–TIR Data in a Semi-Arid Rainfed Agricultural Area (Camarena, Central Spain)

Soils are an essential factor contributing to the agricultural production of rainfed crops such as barley and triticale cereals. Changing environmental conditions and inadequate land management are endangering soil quality and productivity and, in turn, crop quality and productivity are affected. Advances in hyperspectral remote sensing are of great use for the spatial characterization and monitoring of the soil degradation status, as well as its impact on crop growth and agricultural productivity. In this study, hyperspectral airborne data covering the visible, near-infrared, short-wave infrared, and thermal infrared (VNIR–SWIR–TIR, 0.4–12 µm) were acquired in a Mediterranean agricultural area of central Spain and used to analyze the spatial differences in vegetation vitality and grain yield in relation to the soil degradation status. Specifically, leaf area index (LAI), crop water stress index (CWSI), and the biomass of the crop yield are derived from the remote sensing data and discussed regarding their spatial differences and relationship to a classification of erosion and accumulation stages (SEAS) based on previous remote sensing analyses during bare soil conditions. LAI and harvested crop biomass yield could be well estimated by PLS regression based on the hyperspectral and in situ reference data (R2 of 0.83, r of 0.91, and an RMSE of 0.2 m2 m−2 for LAI and an R2 of 0.85, r of 0.92, and an RMSE of 0.48 t ha−1 for grain yield). In addition, the soil erosion and accumulation stages (SEAS) were successfully predicted based on the canopy spectral signal of vegetated crop fields using a random forest machine learning approach. Overall accuracy was achieved above 71% by combining the VNIR–SWIR–TIR canopy reflectance and emissivity of the growing season with topographic information after reducing the redundancy in the spectral dataset. The results show that the estimated crop traits are spatially related to the soil’s degradation status, with shallow and highly eroded soils, as well as sandy accumulation zones being associated with areas of low LAI, crop yield, and high crop water stress. Overall, the results of this study illustrate the enormous potential of imaging spectroscopy for a combined analysis of the plant-soil system in the frame of land and soil degradation monitoring

Evaluation of Airborne HySpex and Spaceborne PRISMA Hyperspectral Remote Sensing Data for Soil Organic Matter and Carbonates Estimation

Remote sensing and soil spectroscopy applications are valuable techniques for soil property estimation. Soil organic matter (SOM) and calcium carbonate are important factors in soil quality, and although organic matter is well studied, calcium carbonates require more investigation. In this study, we validated the performance of laboratory soil spectroscopy for estimating the aforementioned properties with referenced in situ data. We also examined the performance of imaging spectroscopy sensors, such as the airborne HySpex and the spaceborne PRISMA. For this purpose, we applied four commonly used machine learning algorithms and six preprocessing methods for the evaluation of the best fitting algorithm.. The study took place over crop areas of Amyntaio in Northern Greece, where extensive soil sampling was conducted. This is an area with a very variable mineralogical environment (from lignite mine to mountainous area). The SOM results were very good at the laboratory scale and for both remote sensing sensors with R2 = 0.79 for HySpex and R2 = 0.76 for PRISMA. Regarding the calcium carbonate estimations, the remote sensing accuracy was R2 = 0.82 for HySpex and R2 = 0.36 for PRISMA. PRISMA was still in the commissioning phase at the time of the study, and therefore, the acquired image did not cover the whole study area. Accuracies for calcium carbonates may be lower due to the smaller sample size used for the modeling procedure. The results show the potential for using quantitative predictions of SOM and the carbonate content based on soil and imaging spectroscopy at the air and spaceborne scales and for future applications using larger datasets

A Spectral Transfer Function to Harmonize Existing Soil Spectral Libraries Generated by Different Protocols

Soil spectral libraries (SSLs) are important big-data archives (spectra associated with soil properties) that are analyzed via machine-learning algorithms to estimate soil attributes. Since different spectral measurement protocols are applied when constructing SSLs, it is necessary to examine harmonization techniques to merge the data. In recent years, several techniques for harmonization have been proposed, among which the internal soil standard (ISS) protocol is the most largely applied and has demonstrated its capacity to rectify systematic effects during spectral measurements. Here, we postulate that a spectral transfer function (TF) can be extracted between existing (old) SSLs if a subset of samples from two (or more) different SSLs are remeasured using the ISS protocol. A machine-learning TF strategy was developed, assembling random forest (RF) spectral-based models to predict the ISS spectral condition using soil samples from two existing SSLs. These SSLs had already been measured using different protocols without any ISS treatment the Brazilian (BSSL, generated in 2019) and the European (LUCAS, generated in 2009-2012) SSLs. To verify the TF's ability to improve the spectral assessment of soil attributes after harmonizing the different SSLs' protocols, RF spectral-based models for estimating organic carbon (OC) in soil were developed. The results showed high spectral similarities between the ISS and the ISS-TF spectral observations, indicating that post-ISS rectification is possible. Furthermore, after merging the SSLs with the TFs, the spectral-based assessment of OC was considerably improved, from R2 = 0.61, RMSE (g/kg) = 12.46 to R2 = 0.69, RMSE (g/kg) = 11.13. Given our results, this paper enhances the importance of soil spectroscopy by contributing to analyses in remote sensing, soil surveys, and digital soil mapping

User Inquiries and Ground Segment Operation Activities

Since the beginning of the operational phase in November 2022, the Environmental Mapping and Analysis Programm (EnMAP) has gathered substantial interest within the Earth Observation community, counting a considerable number of more than 1800 registered users from over 80 different countries across the globe. The EnMAP data archive is also used very frequently by users with ordering and downloading approx. 2000 tiles per months. Any science user can submit its (his/her) own observations request via the EnMAP Instrument Planning Portal (IPP, https://planning.enmap.org/, also reachable through the official website www.enmap.org), by submitting a proposal. The IP portal also provides access links to the entire EnMAP data archive via the EOWEB Geoportal. The number of users requesting future observations varies significantly based on geographic location. Notably, Europe sees the highest demand for the EnMAP mission, leading to challenges such as conflicting orders for areas within the same orbit. This convergence has introduced complexities in data acquisition, particularly for time series and orchestration of field campaigns. To ensure (increase) regular data acquisition and boost mission efficiency, a "Foreground Mission" has been introduced. This entails prioritized acquisition of extended 990-kilometer flightlines (stripes) over Europe, with a specific focus on Germany. This strategic approach aims to improve data coverage in Germany and streamlines recurring acquisitions along key transects. First this informative presentation provides an assessment of ground segment operations, with special attention given to user feedback and inquiries. Along the way, it outlines the most prevalent user concerns and highlights the strategic factors involved in requesting observations. As second part the audience gains deeper insight into the newly implemented Foreground Mission initiative

Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century

Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral-organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one-tenth of the cost of a chemical determination. Spectroscopy is cost-effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over-fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over-fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered 'black boxes'. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Highlights Spectroscopy is reliable because it is a highly repeatable and reproducible analytical technique. Spectra are calibrated to estimate concentrations of soil properties with known error. Spectroscopy is cost-effective for estimating soil properties. Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist. Large libraries of soil spectra provide information that can be used locally to aid estimates from new samples

The EnMAP Observation Planning and Data Access for Scientific Users

EnMAP (Environmental Mapping and Analysis Program; www.enmap.org) is the first German imaging spectroscopy mission, to be launched in April 2022. After its Launch, Early Orbit and Commissioning Phase (6 months), the EnMAP mission will be available to the international user community for the data access and ordering process. EnMAP will be operated by the German Aerospace Center (DLR) covering all aspects relevant to assure successful mission operations. This comprises controlling and commanding the satellite using multi-mission infrastructures as well as observation planning, data reception, hyperspectral data processing including calibration, data archiving, data access and delivery, and providing web-interfaces to the international user community. This presentation will give an overview of EnMAP observation planning and data access concepts and outlines the data ordering workflow in particular for scientific users. The user can get access to EnMAP data using two different order options: On the one hand the user can submit future order requests through the EnMAP Data Access Portal (EDAP). The EDAP links to a set of functions for registered users that will support the international user community. This portal includes amongst others the proposal portal allowing submission of proposal for all scientific users responding to a Data Announcement of Opportunity (AO) and the Observation Request Portal providing planning support of observation requests and allowing submission of future orders. On the other hand, the already recorded data can be searched, processed and delivered based on catalogue from the archive through the EOWEB® GeoPortal. Although EnMAP is based on an open data policy and every type of user is in principle entitled to download data and request acquisitions, there will be different user categories to set acquisition priorities. The scientific (Cat-1) orders has higher priority and is requested to submit a proposal, which will undergo a scientific evaluation. The associated results will be presented by an interactive map supporting the establishment of a worldwide user network and guarantee the highest transparency of the proposal process. In the Observation Portal the user is able to submit future order requests by specifying following order parameters, such as the geographic area of interest (AOI) (between 74° North and 74° South), length of the AOI as a multiple of 30 km and up to 1000 km, the specification of the maximum allowable tilt angle of the satellite across the orbit (5° to 30°), the time span in which the acquisition should be performed and the option for time series and the number of data takes per months. To ensure acceptable illumination conditions, only images with sun zenith angle lower 60° will be considered. As for data acquisition EnMAP will be able to collect 5000 km along track and 30 km across track per day. The probability that the order will be included in the mission planning depends on the requirements for the observation as well as the specified priority and quota. Whether a specific data take is scheduled at the end depends on factors such as e.g. available data storage, cloud probabilities (e.g. historical and predicted cloud coverage) and, if requested by the user, sunglint probability (this is relevant for water products only). Users should make a request at least 25 hours before the scheduled recording to ensure the uplink. All data are available no later than 24 hours after collection for further processing into data products. The EnMAP ground segment will provide a range of standardized data products with different levels of processing of Level 1B, Level 1C and Level 2A based on archived Level 0 comprising extensive quicklooks and metadata. Due to required multiple processing options, each product is generated specifically for the order and delivered using FTPS (FTP with SSL) provided by multi-mission facilities

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Midinfrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique

First year EnMAP radiometric performance based on scenes over RadCalNet and PICS sites

The radiometric performance of EnMAP is presented based on scenes over RadCalNet and PICS sites acquired during the first year since launch. The results show the typical accuracy and stability attainable by EnMAP radiometric products