In this study, the potential of the newly developed Hybrid Retrieval Workflow, integrated into the EnMAP-Box, is demonstrated by quantifying NPV using hyperspectral data. Due to its diverse functions in agricultural and natural ecosystems, NPV mapping is becoming increasingly important. The emergence of new-generation spaceborne spectrometers like PRISMA and EnMAP, providing largescale and potentially multi-temporal hyperspectral data, opens up new possibilities for NPV mapping on a global scale. The EnHyR facilitates NPV mapping using hybrid machine learning approaches that use simulated spectral training data optimized via Active Learning supported by in-situ data. Preliminary results for Slovakia and Southern Germany highlight the potential of the EnHyR using PRISMA and EnMAP data for global NPV mapping.

Non-photosynthetic vegetation (NPV) has been identified as priority variable in the context of new spaceborne imaging spectroscopy missions. In this study we provide a first attempt to quantify NPV biomass from these unprecedented data streams to be provided by multiple recently launched or planned instruments. A hybrid workflow is proposed including Gaussian process regression (GPR) trained over radiative transfer model (RTM) simulations and applying active learning strategies. A soybean field data set including two dates with NPV measurements on yellow and senescent (brown) plant organs was used for model validation, resulting in relative errors of 13.4%. This prototype retrieval model was then applied over a resampled Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) scene, resulting in trustful estimates of NPV biomass for some areas with crop residue cover and senescent vegetation. In view of these results, the proposed workflow may show a promising path towards operational delivery of next-generation global NPV products.

Contiguous spectral measurements enable the derivation of a large variety of agriculturally relevant biophysical and biochemical variables. Recent and future hyperspectral Earth observation missions are combining the advantages of high spectral resolution with the stability and reliability of satellite platforms. This potentially enables the integration of hyperspectral information products into agricultural practice. Dedicated tools are required that make use of the added value of contiguous spectral information operationally provided from spaceborne instruments. The free and open source software “EnMAP-Box” features a collection of hyperspectral algorithms (“Agricultural Applications: Agri-Apps”) that are specifically designed for the retrieval of agriculturally relevant information products. By applying these tools to data from DESIS and PRISMA sensors collected during the growing season of 2020, the potential of the EnMAP-Box Agri-Apps for supporting agricultural land use monitoring is demonstrated. First results indicate that the collection of agricultural algorithms is well-suited to process hyperspectral data from different sources not limited to EnMAP.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is in preparation to carry a unique visible to shortwave infrared spectrometer. CHIME will globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. The mission shall provide Level 1B, 1C and 2A products, as well a set of downstream products related to the different environmental applications, such as the quantification of vegetation traits. In this context, this work presents the first hybrid retrieval models for the operational delivery of vegetation properties products. Within ESA's CHIME end-to-end (E2E) simulator study, 13 leaf and canopy trait models were developed as part of the L2B vegetation (L2BV) module. The E2E framework functions as a simulated reality that enabled to test and improve the algorithms. The models were further tuned and validated against campaign data using active learning methods. As a proof of concept, the prototype retrieval models were applied to both hyperspectral airborne (HyPlant) and spaceborne (PRISMA) imagery that were first resampled to CHIME band settings. Among the provided vegetation products, it led to a first space-based canopy nitrogen content map over a heterogeneous landscape. The obtained CHIME-like L2BV traits maps demonstrate the feasibility to routinely deliver a collection of next-generation vegetation products across the globe.

The proper management of nitrogen (N) is a pre-requisite for sustainable fertilization in modern agriculture. Methods for N - retrieval from Earth Observation (E.O.) data have been mainly based on empirical algorithms. In the present study, two methods (physically based / hybrid) for the assessment of crop nitrogen content$(\mathrm{N}_{\mathrm{area}})$and concentration$(\mathrm{N}_{\mathrm{mass}})$were tested. Data from a hyperspectral field campaign in the framework of the future satellite mission Environmental Mapping and Analysis Program (EnMAP) were exploited using a recalibrated PROSPECT model coupled with the canopy reflectance model 4SAIL. The physically based algorithm achieved relative errors (rRMSE) of 72% for$\mathrm{N}_{\mathrm{area}}$with$\mathrm{R}^{2}=0.92$. The hybrid approach obtained higher accuracies with rRMSE lower than 16% for the retrieval of$\mathrm{N}_{\mathrm{mass}}$. Uncertainties of the predictor variables have to be taken into account. Both algorithms represent interesting techniques for global agricultural monitoring from hyperspectral satellite data but further analysis is required.

We introduce the Interactive Visualization of Vegetation Reflectance Models (IVVRM) tool as a sandbox environment for the PROSAIL family of radiative transfer models. Every interaction with the Graphical User Interface (GUI) invokes a new model run of the updated parameter set and the results are instantly plotted on the screen. The quasi-simultaneous response allows easy hands-on practice with PROSAIL for education and training as well as straightforward inversions of biophysical variables from spectra by manual curve fitting. It is shown that expert knowledge can improve the quality of parameter retrieval and reveal sources of uncertainties in the field data and the models. IVVRM is free of charge and available as an application through the EnMAP-Box 3.0.

The water- food-energy nexus intimately binds food and energy production to water use. The largest fraction of today's water use is through food and energy production in agriculture. Water use in agriculture, however, is predominantly wasteful and not sustainable, due to over-exploitation of scarce water resources on one hand and through inefficient use of water on the other. So far, no global monitoring system for efficiency and sustainability of agricultural water use exists. Both quantities thus are largely unknown especially on a global level. This leads to the limitation that efficiency and sustainability of water use cannot yet be incorporated in control mechanisms for virtual water flows in global food trade. New observational capacities (e.g. the COPERNICUS sensors) and model approaches can now be applied to bridge the gap between local and global scales by quantifying and investigating water flows in detail. This paper presents the outline, basic concept and first results of the ViWA-Initiative, where operational Earth Observation, physically-based growth modelling and economic trade modelling are combined to provide a continuous monitoring system for global virtual water flows in the agricultural commodity chain.

The retrieval of quantitative equivalent water thickness on canopy level (EWTc) is an agriculturally important task for hyperspectral remote sensing. In this study the Beer-Lambert law is applied to inversely determine water content from measured winter wheat spectra collected in 2015 and 2017. The spectral model is calibrated using a look-up-table (LUT) of 50.000 PROSPECT spectra. Validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS) and in-situ data acquired in Southern Germany. After considering destructive in-situ water content measurements separately for leaves, stems, and fruits, results indicate optically active plant water by plant component in the 930 to 1060 nm range of canopy reflectance. Results for spectrally derived EWTc were most promising for leaves and ears reaching coefficients of determination up to 0.75 and a normalized RMSE (nRMSE) of 24% between measured and estimated canopy water content.

The land surface process model PROMET is used to simulate the nitrogen cycle of two large winter wheat fields (115 and 87 ha) in Northern Germany in dependence of the prevailing meteorology and the given fertilizations (site specific and homogeneous). Through assimilation of remote sensing information, small scale patterns of nitrogen concentration could be modelled, which correlate strongly (R2up to 0.82) with Yara N-Tester field measurements during the month of May 2013. With increasing homogeneity of the canopy, the correlation gradually decreases towards June 2013 (R2= 0.22). The achieved results are a strong indicator that the precise simulation of temporal and spatial nitrogen dynamics of winter wheat fields is possible with the proposed model system.

For a sustainable and efficient land management, spatially distributed and up-to-date information on the land surface is of central importance. A continuous flow of land management information is the basis to improve decisions on use, cultivation intensity and allocation of resources (e.g. water for irrigation). Remote sensing is in a unique position to contribute to this task as it is globally available and provides specific information about current crop status. The M4Land concept is designed to derive information products for a sustainable management of the land surface. In this paper, the methodology and the results for the autonomous development of three products, i.e. land use, crop cycle and intensity of agricultural use, is presented for two test sites and for several years. In the M4Land system, a crop growth model (PROMET) and a reflectance model (SLC) are coupled in order to provide these information products by analyzing multi-temporal and multi-sensoral satellite images.

The upcoming satellite mission EnMAP offers the highly relevant opportunity of retrieving information on the seasonal development of vegetation parameters on a regional scale based on hyperspectral data. This study aims to investigate the potential of retrieving leaf area index (LAI) information from hyperspectral images. The widely used PROSAIL model is applied to generate look-up-table (LUT) libraries, by which the model is inverted to derive LAI information. Different techniques for the LUT based inversion are tested, such as several cost functions, type and amount of artificial noise, number of considered solutions and type of averaging method. The optimal inversion procedure (laplace, median, 4% inverse multiplicative noise, 350 averages) is identified by validating the results against corresponding in-situ measurements (N = 330) of LAI, leading to robust results (R2= 0.65, RMSE = 0.64).

A method to derive products for a sustainable management of the land surface is developed in the frame of the M4Land project (“Model based, Multi-temporal, Multi-scale and Multi-sensoral retrieval of continuous land management information”). The system relies on a model-supervised dynamic classification of land cover from multi-temporal satellite data that works automatically without the need for training data or manual data processing. This approach is tested for the first time in a mesoscale setting (300m resolution). The performed model-supervised land cover classification of ENVISAT MERIS data at a test site in Southern Germany in 2010 is promising with an overall accuracy of 84.7%. After the consolidation of the method on this scale further land management products can be developed that are based on the underlying land surface model data.

The synergistic use of optical and SAR data for applications in agriculture and precision farming is analyzed. Plant parameters derived from optical sensors have proven to be very valuable inputs for accurate crop growth modeling and biomass monitoring. Information on the temporal development of Leaf Area Index (LAI) and structural canopy changes, such as harvest, strongly support the simulation of plant development and yield formation in a realistic and spatially distributed manner. The accuracy of LAI retrieval, based on RapidEye data using radiative transfer simulations with SLC, has been successfully validated with in-situ measurements for wheat, maize and rapeseed. Thus, LAI derived from optical data covering many fields and observing the whole crop cycle served for analyzing the sensitivity of TerraSAR-X to LAI for wheat. A high correlation between LAI values and radar backscatter especially in VV polarization was observed on field basis. Derivation of LAI from SAR-data can successfully complement LAI derived from optical data and thus stabilize the necessary data sources for plant modeling, making the data less dependent on weather conditions. Additionally, a structural indicator for the determination of the harvest date was found in the VH/VV backscatter ratio.

Complex process-based land surface models require detailed input parameters. While the process-describing parameters mostly are well known from laboratory research, the spatial parameters, such as terrain, land use or soil maps, often are available at coarse spatial resolutions only. The mass and energy balance of the land surface is strongly determined by plant growth. Depending on the application of the model, especially the spatial distribution of growth influencing site characteristics, such as soil properties for example, therefore is of major importance. This study demonstrates, how Earth Observation data can be used to overcome the lack of spatial detail, applying the land surface model PROMET to a precision farming task, i.e. site specific cereal yield modelling on a farm in northeast Germany. Maps of photosynthetically active leaf area, generated from RapidEye and Landsat TM data, were assimilated and the model output was successfully validated against measured yield maps for the summer of 2010.

The challenges of future global food production are characterized by a growing population, changing eating habits and climate change. Ecological intensification of global agriculture can avoid food shortage sustainably and preserve a minimum of nature. The paper describes logic and possible architecture of a global agricultural land management information system (ALMIS) as a means to implement ecological intensification. It shows that specifically hyperspectral remote sensing combined with complementing R/S sources is essential to provide the necessary data to derive, independent of location, the necessary farming information for sustainable, spatially explicit management of agriculture on the whole Globe.

In the frame of the German hyperspectral satellite mission “Environmental Mapping and Analysis Program” (EnMAP) a software interface is under development to facilitate the processing of the future hyperspectral data used by a range of environmental applications. For agricultural studies, the software will include the Agricultural Vegetation Analyzer (AVA) module, which is based on a look-up table (LUT) inversion of a radiative transfer model (RTM). Moreover, a statistical evaluator box (MapStat) will be implemented to validate, amongst others, the estimation of vegetation biophysical variables. Results from a hyperspectral field campaign with the Airborne Prism Experiment (APEX) instrument in Bavaria (Germany) showed that AVA can be regarded as robust and sound technique obtaining moderate to good results without the need of crop- or site specific calibration. MapStat will guarantee comparability between numerous modeling studies expected in the upcoming years within the context of the future EnMAP sensor.