Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP

Leitão, Pedro J.; Schwieder, Marcel; Suess, Stefan; Okujeni, Akpona; Galvão, Lênio Soares; Linden, Sebastian van der; Hostert, Patrick

doi:10.3390/rs71013098

Cited by 28 publications

(24 citation statements)

References 86 publications

(104 reference statements)

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“…They recommended a Support Vector Classification (SVC) model integrating synthetic mixtures with the parametrization to obtain more accurate results with the EnMAP sensor. Leitão et al [48] performed a similar study and pointed out mapping precision enhancement with EnMAP data compared to Hyperion results. This difference in precision was associated with the better spatial coverage, revisit time, spectral and temporal resolution of EnMAP.…”

Section: Preliminary Analysis Of the Literature Databasementioning

confidence: 95%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi-and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors' publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors' applications with Sentinel-2's and (ii) analyzes the applications' requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses.

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Section: Preliminary Analysis Of the Literature Databasementioning

confidence: 95%

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

et al. 2018

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“…• Natural ecosystems and ecosystem transitions: EnMAP specifically creates opportunities for monitoring spatially-heterogeneous landscapes and environmental gradients [61]. Through monitoring and analysis of complex natural ecosystem processes, it is possible to deepen the understanding of anthropogenic impacts and to contribute to global mitigation programs, such as the United Nation's Reducing Emissions from Deforestation and Forest Degradation (REDD) or the Convention on Biological Diversity.…”

Section: Development Of the Enmap Science Planmentioning

confidence: 99%

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

et al. 2015

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Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

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“…These data, by systematically describing the Earth's surface in a very detailed manner, have great potential for ecosystem monitoring and aboveground carbon mapping (Leitão et al 2015). These data, by systematically describing the Earth's surface in a very detailed manner, have great potential for ecosystem monitoring and aboveground carbon mapping (Leitão et al 2015).…”

Section: Introductionmentioning

confidence: 99%

From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

et al. 2018

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In times of rapid global change, ecosystem monitoring is of utmost importance. Combined field and remote sensing data enable large-scale ecosystem assessments, while maintaining local relevance and accuracy. In heterogeneous landscapes, however, the integration of field-collected data with remote sensing image pixels is not a trivial matter. Indeed, much of the uncertainty in models that use remote sensing to map larger areas lies on the field data integration. In this study, we propose to use fine spatial resolution (5 9 5 m 2 ) remote sensing data as auxiliary data for upscaling field-sampled aboveground carbon data to target (meso-scale, i.e., 30 9 30 m 2 ) image pixels. In this process, we assess the effects of field data disaggregation and extrapolation, with and without the auxiliary data. We test this on three study sites in heterogeneous landscapes of the Brazilian savanna. We thus compare two methods that use auxiliary data-surface method, which uses a weighting layer, and regression method, which applies a regression model-with one method without auxiliary data-cartographic method. To evaluate our results, we compared observed vs. estimated aboveground carbon values (for known samples) at the pixel level. Additionally, we fitted a random forest regression model with the assigned carbon estimates and the target satellite imagery and assessed the influence of the fraction of extrapolated vs. sampled carbon values on model performance. We observed that, in heterogeneous landscapes, the use of fine spatial resolution remote sensing data improves the upscaling of field-based aboveground carbon data to coarser image pixels. We also show that a surface method is more suitable for spatial disaggregation, while a regression approach is preferable for extrapolating non-sampled pixel fractions. In our study, larger datasets, which included a higher proportion of estimated values, generally delivered better models of aboveground carbon than smaller datasets that are assumed to more reliably reflect reality. Our approach enables to link field and remote sensing data, which in turn enables the detailed mapping of aboveground carbon in heterogeneous landscapes over large areas through the optimized integration of field data and multi-scale remote sensing data.

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Monitoring Natural Ecosystem and Ecological Gradients: Perspectives with EnMAP

Cited by 28 publications

References 86 publications

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

From sample to pixel: multi‐scale remote sensing data for upscaling aboveground carbon data in heterogeneous landscapes

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