In contrast to the last century, where more people used to live in rural areas, at present, more than half of the world's population lives in urban settlements. Hence, the 21st century is the century of the cities and of urbanization. The rapid urbanization process experienced by the majority of developing countries during the last few decades has resulted in fundamental changes to the environment and to the social structure. In most of the megacities that have grown to unprecedented size, the pace of urbanization has far exceeded the growth of necessary infrastructure and services. In order to carry out the urban planning and development tasks necessary to improve the living conditions for the poorest world-wide, a detailed spatial data basis is required. Due to the high dynamics of megacities, traditional methods such as statistical analyses or fieldwork are limited to capture the urban process. Remote sensing provides the opportunity to monitor spatial patterns of urban structures with high spatial and temporal resolution. The present study investigates the potential to use very high-resolution (VHR) remote sensing data to identify urban structures and dynamics within Delhi, India. The paper presents a semi-automated, object-oriented classification approach which allows for the identification of informal settlements within the urban area. In order to provide indicators to identify socio-economic structures and their dynamics, the image classification results are embedded in an integrative analysis concept. Information on population and water related parameters are derived. This is understood to be a first step to the development of indicators which will help to identify and understand the different shapes, actors, and processes in megacities.
Abstract. Physically-based water balance models require a realistic parameterisation of land surface characteristics of a catchment. Alpine areas are very complex with strong topographically-induced gradients of environmental conditions, which makes the hydrological parameterisation of Alpine catchments difficult. Within a few kilometres the water balance of a region (mountain peak or valley) can differ completely. Hence, remote sensing is invaluable for retrieving hydrologically relevant land surface parameters. The assimilation of the retrieved information into the water balance model PROMET is demonstrated for the Toce basin in Piemonte/Northern Italy. In addition to land use, albedos and leaf area indices were derived from LANDSAT-TM imagery. Runoff, modelled by a water balance approach, agreed well with observations without calibration of the hydrological model. Keywords: PROMET, fuzzy logic based land use classification, albedo, leaf area index
Decades after release of the first PROSPECT + SAIL (commonly called PROSAIL) versions, the model is still the most famous representative in the field of canopy reflectance modelling and has been widely used to obtain plant biochemical and structural variables, particularly in the agricultural context. The performance of the retrieval is usually assessed by quantifying the distance between the estimated and the in situ measured variables. While this has worked for hundreds of studies that obtained canopy density as a one-sided Leaf Area Index (LAI) or pigment content, little is known about the role of the canopy geometrical properties specified as the Average Leaf Inclination Angle (ALIA). In this study, we exploit an extensive field dataset, including narrow-band field spectra, leaf variables and canopy properties recorded in seven individual campaigns for winter wheat (4x) and silage maize (3x). PROSAIL outputs generally did not represent field spectra well, when in situ variables served as input for the model. A manual fitting of ALIA and leaf water (EWT) revealed significant deviations for both variables (RMSE = 14.5°, 0.020 cm) and an additional fitting of the brown leaf pigments (Cbrown) was necessary to obtain matching spectra at the near infrared (NIR) shoulder. Wheat spectra tend to be underestimated by the model until the emergence of inflorescence when PROSAIL begins to overestimate crop reflectance. This seasonal pattern could be attributed to an attenuated development of ALIAopt compared to in situ measured ALIA. Segmentation of nadir images of wheat was further used to separate spectral contributors into dark background, ears and leaves + stalks. It could be shown that the share of visible fruit ears from nadir view correlates positively with the deviations between field spectral measurement and PROSAIL spectral outputs (R² = 0.78 for aggregation by phenological stages), indicating that retrieval errors increase for ripening stages. An appropriate model parameterization is recommended to assure accurate retrievals of biophysical and biochemical products of interest. The interpretation of inverted ALIA as physical leaf inclinations is considered unfeasible and we argue in favour of treating it as a free calibration parameter.
Abstract Global biomass demand is expected to roughly double between 2005 and 2050. Current studies suggest that agricultural intensification through optimally managed crops on today’s cropland alone is insufficient to satisfy future demand. In practice though, improving crop growth management through better technology and knowledge almost inevitably goes along with (1) improving farm management with increased cropping intensity and more annual harvests where feasible and (2) an economically more efficient spatial allocation of crops which maximizes farmers’ profit. By explicitly considering these two factors we show that, without expansion of cropland, today’s global biomass potentials substantially exceed previous estimates and even 2050s’ demands. We attribute 39% increase in estimated global production potentials to increasing cropping intensities and 30% to the spatial reallocation of crops to their profit-maximizing locations. The additional potentials would make cropland expansion redundant. Their geographic distribution points at possible hotspots for future intensification.
Model-based Selection of hyperspectral EnMAP Channels for optimal Inversion of Radiation Transfer Models in Agriculture. Satellite-based hyperspectral Earth observation data combined with physically based radiative transfer models have the strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables such as leaf chlorophyll content. To meet this goal, possible error sources in the modelling should be minimized. Thus, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the PROSAIL model was employed to emulate the setup of the future EnMAP hyperspectral sensor in the visible and near-infrared (VNIR) spectral region with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with highest mean absolute error (MAE) between model simulation and spectral measurement. For this purpose data from two campaigns were exploited (1) from Nebraska–Lincoln (maize and soybean) and (2) from Munich–North-Isar (maize and winter wheat). A significant increase of accuracy for leaf chlorophyll content (LCC, µg cm−2) estimations could be obtained, with relative RMSE decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE ~ 0.01) to stabilize the retrieval of crop biochemical variables.
The validation of coarse-scale remote sensing products like SMOS (ESA's Soil Moisture and Ocean Salinity mission) L2 soil moisture or L1c brightness temperature data requires the maintenance of long-term soil moisture monitoring sites like the Upper Danube Catchment SMOS validation site situated in Southern Germany. An automatic framework has been built up to compare SMOS data against in situ measurements, land surface model simulations, and ancillary satellite data. The uncertainties of the different data sets used for SMOS validation are being assessed in this paper by comparing different microwave radiative transfer and land surface model results to measured soil moisture and brightness temperature data from local scale to SMOS scale. The mean observed uncertainties of the modeled soil moisture decrease from 0.094 m 3 m -3 on the local scale to 0.040 m 3 m -3 root mean squared error (RMSE) on the large scale. The RMSE of anomalies is 0.023 m 3 m -3 on the large scale. The mean R2 increases from 0.6 on the local scale to 0.75 on the medium scale. The land surface model tends to underestimate soil moisture under wet conditions and has a smaller dynamical range than the measurements. The brightness temperature comparison leads to a RMSE around 12-16 K between microwave radiative transfer model and airborne measurements under varying soil moisture and vegetation conditions. The assessed data sets are considered reliable and robust enough to be able to provide a valuable contribution to SMOS validation activities.
The availability of in situ snow water equivalent (SWE), snowmelt and run-off measurements is still very limited especially in remote areas as the density of operational stations and field observations is often scarce and usually costly, labour-intense and/or risky. With remote sensing products, spatially distributed information on snow is potentially available, but often lacks the required spatial or temporal requirements for hydrological applications. For the assurance of a high spatial and temporal resolution, however, it is often necessary to combine several methods like Earth Observation (EO), modelling and in situ approaches. Such a combination was targeted within the business applications demonstration project SnowSense (2015–2018), co-funded by the European Space Agency (ESA), where we designed, developed and demonstrated an operational snow hydrological service. During the run-time of the project, the entire service was demonstrated for the island of Newfoundland, Canada. The SnowSense service, developed during the demonstration project, is based on three pillars, including (i) newly developed in situ snow monitoring stations based on signals of the Global Navigation Satellite System (GNSS); (ii) EO snow cover products on the snow cover extent and on information whether the snow is dry or wet; and (iii) an integrated physically based hydrological model. The key element of the service is the novel GNSS based in situ sensor, using two static low-cost antennas with one being mounted on the ground and the other one above the snow cover. This sensor setup enables retrieving the snow parameters SWE and liquid water content (LWC) in the snowpack in parallel, using GNSS carrier phase measurements and signal strength information. With the combined approach of the SnowSense service, it is possible to provide spatially distributed SWE to assess run-off and to provide relevant information for hydropower plant management in a high spatial and temporal resolution. This is particularly needed for so far non, or only sparsely equipped catchments in remote areas. We present the results and validation of (i) the GNSS in situ sensor setup for SWE and LWC measurements at the well-equipped study site Forêt Montmorency near Quebec, Canada and (ii) the entire combined in situ, EO and modelling SnowSense service resulting in assimilated SWE maps and run-off information for two different large catchments in Newfoundland, Canada.
Agriculture faces the challenge of providing food, fibre and energy from limited land resources to satisfy the changing needs of a growing world population. Global megatrends, e.g., climate change, influence environmental production factors; production and consumption thus must be continuously adjusted to maintain the producer–consumer-equilibrium in the global food system. While, in some parts of the world, smallholder farming still is the dominant form of agricultural production, the use of digital information for the highly efficient cultivation of large areas has become part of agricultural practice in developed countries. Thereby, the use of satellite data to support site-specific management is a major trend. Although the most prominent use of satellite technology in farming still is navigation, Earth Observation is increasingly applied. Some operational services have been established, which provide farmers with decision-supporting spatial information. These services have mostly been boosted by the increased availability of multispectral imagery from NASA and ESA, such as the Landsat or Copernicus programs, respectively. Using multispectral data has arrived in the agricultural commodity chain. Compared to multispectral data, spectrally continuous narrow-band sampling, often referred to as hyperspectral sensing, can potentially provide additional information and/or increased sampling accuracy. However, due to the lack of hyperspectral satellite systems with high spatial resolution, these advantages mostly are not yet used in practical farming. This paper summarizes where hyperspectral data provide additional value and information in an agricultural context. It lists the variables of interest and highlights the contribution of hyperspectral sensing for information-driven agriculture, preparing the application of future operational spaceborne hyperspectral missions.
