Remote Sensing of Environment

Journal: Remote Sensing of Environment

Abbreviation

Remote Sens. Environ.

Publisher

Elsevier

ISSN

0034-4257

Show all metadata

Search Results

Publications 1 - 10 of 76

Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers – From theory to application
Aasen, Helge; Bolten, Andreas (2018)
Remote Sensing of Environment
Remote sensing of landslides
Metternicht, Graciela; Hurni, Lorenz; Gogu, Radu (2005)
Remote Sensing of Environment
WAFER: A new method to retrieve sun-induced fluorescence based on spectral wavelet decompositions
Oehl, Veronika; Damm, Alexander (2023)
Remote Sensing of Environment
Sun-induced fluorescence (SIF) as a close remote sensing based proxy for photosynthesis is accepted as a useful measure to remotely monitor vegetation health and gross primary productivity. It is therefore important to develop methods that allow for its precise and reliable retrieval from radiance measurements with spectral resolutions that have been increasing over the past few years. Retrieval methods are catching up to the increasing complexity of the available datasets making use of their whole information extent (spectral, spatial and temporal) but the comparability of different SIF retrievals and consistency across scales is still limited. In this work we present the new retrieval method WAFER (WAvelet decomposition FluorEscence Retrieval) based on wavelet decompositions of the measured spectra of reflected radiance as well as a reference radiance not containing fluorescence. By comparing absolute absorption line depths by means of the corresponding wavelet coefficients, a relative reflectance is retrieved independently of the fluorescence, i.e. without intro-ducing a coupling between reflectance and fluorescence. The fluorescence can then be derived as the remaining offset. This method can be applied to arbitrary chosen wavelength windows in the whole spectral range, such that all the spectral data available is exploited, including the separation into several frequency (i.e. width of absorption lines) levels and without the need of extensive training datasets. At the same time, the assumptions about the reflectance shape are minimal and no spectral shape assumptions are imposed on the fluorescence, which not only avoids biases arising from wrong or differing fluorescence models across different spatial scales and retrieval methods but also allows for the exploration of this spectral shape for different measurement setups. WAFER is tested on a synthetic dataset as well as several diurnal datasets acquired with a field spectrometer (FloX) over an agricultural site. We compare the WAFER method to two established retrieval methods, namely the improved Fraunhofer line discrimination (iFLD) method and spectral fitting method (SFM) and find a good agreement with the added possibility of exploring the true spectral shape of the offset signal and free choice of the retrieval window. On our synthetic dataset, WAFER seems to outperform the SFM and works best in a spectral window only containing solar Fraunhofer lines where we achieve a relative retrieval error of 10% on average. Applied to the real dataset, the method returns reasonable diurnal cycles for SIF and can, due to the decoupling of reflectance and fluorescence retrieval, reveal interesting trends at times when vegetation canopies may experience a midday depression that remain largely unobserved with current methods.
Imaging spectroscopy to assess the composition of ice surface materials and their impact on glacier mass balance
Naegeli, Kathrin; Damm, Alexander; Huss, Matthias; et al. (2015)
Remote Sensing of Environment
Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi-spectral satellite data
Roth, Sandra I.B.; Leiterer, Reik; Volpi, Michele; et al. (2019)
Remote Sensing of Environment
There is an increasing demand for clove products, mainly dried buds and essential oil on global markets. Consequently, the importance of clove trees as a provisioning service is increasing at the local level, particularly for smallholders cultivating clove trees as cash crops. Due to limited availability of data on local production, using remote sensing-based methods to quantify today's clove production is of key interest. We estimated the clove bud yield in a study site in northeastern Madagascar by detecting individual clove trees and determining relevant production systems, including pasture and clove, clove plantation and agroforestry systems. We implemented an individual tree detection method based on two machine learning approaches. Specifically, we proposed using a circular Hough transform (CHT) for the automated detection of individual clove trees. Subsequently, we implemented a tree species classification method using a random forests (RF) classifier based on a set of features extracted for relevant trees in the above production systems. Finally, we classified and mapped different production systems. Based on the number of detected clove trees growing in a clove production system, we estimated the production system-dependent clove bud yield. Our results show that 97.9% of all reference clove trees were detected using a CHT. Classifying clove and non-clove trees resulted in a producer accuracy of 70.7% and a user accuracy of 59.2% for clove trees. The classification of the clove production systems resulted in an overall accuracy of 77.9%. By averaging different clove tree yield estimates obtained from the literature, we estimated an average total yield of approximately 575 tons/year for our 25,600 ha study area. With this approach, we demonstrate a first step towards large-scale clove bud yield estimation using remote sensing data and methodologies.
Geodetic glacier mass balance in the Karakoram (2011–2019) from TanDEM-X: An InSAR DEM differencing framework
Li , Shiyi; Hajnsek, Irena (2025)
Remote Sensing of Environment
Glaciers serve as sensitive indicators of climate change, influencing both regional water supplies and global sea-level rise. Contrasting to the global tendency towards retreat, glaciers in the Karakoram exhibits an unusual pattern of stability and modest thickening. However, the spatial variability and underlying causes of the mass balance anomalies remain insufficiently understood, primarily due to the limitations in previous measurement methods. To address this gap, we conducted a comprehensive geodetic analysis of glacier elevation changes in the central and eastern Karakoram, covering 681 glaciers of over 10,000 km²between 2011 and 2019. The elevation was measured exclusively with TanDEM-X InSAR data to reduce penetration bias and temporal ambiguities. The geodetic analysis was conducted using a three-module DEM Differencing framework. In this framework, the first module generates high-quality InSAR DEM with an iterative approach to address the challenges of mountainous terrain for InSAR processing; the second module employed an innovative voids filling method using Gaussian Process Regression for robust elevation change mapping; and the third module incorporates a non-stationary uncertainty analysis for rigorous uncertainty quantification. The results reveal a regional mean elevation change rate of (Formula presented) and a specific mass balance of (Formula presented), indicating slight overall thickening during the study period. The spatial patterns of elevation change display pronounced heterogeneity and clear differences between surge-type and non-surge glaciers, reflecting the complex interplay of dynamic, climatic, and morphological factors in the region. This study demonstrates the capability of high-resolution TanDME-X InSAR DEM for accurate geodetic mass balance analysis in challenging mountain environments. The proposed framework provides a scalable methodology for future large-scale glacier studies.
Using remotely sensed soil moisture for land–atmosphere coupling diagnostics: The role of surface vs. root-zone soil moisture variability
Hirschi, Martin; Mueller, Brigitte; Dorigo, Wouter; et al. (2014)
Remote Sensing of Environment
Hot extremes have been shown to be induced by antecedent surface moisture deficits in several regions. While most previous studies on this topic relied on modeling results or precipitation-based surface moisture information (particularly the standardized precipitation index, SPI), we use here a new merged remote sensing soil moisture product that combines active and passive microwave sensors to investigate the relation between the number of hot days (NHD) and preceding soil moisture deficits. Along with analyses of temporal variabilities of surface vs. root-zone soil moisture, this sheds light on the role of different soil depths for soil moisture–temperature coupling. The global patterns of soil moisture–NHD correlations from remote sensing data and from SPI as used in previous studies are comparable. Nonetheless, the strength of the relationship appears underestimated with remote sensing-based soil moisture compared to SPI-based estimates, particularly in regions of strong soil moisture–temperature coupling. This is mainly due to the fact that the temporal hydrological variability is less pronounced in the remote sensing data than in the SPI estimates in these regions, and large dry/wet anomalies appear underestimated. Comparing temporal variabilities of surface and root-zone soil moisture in in-situ observations reveals a drop of surface-layer variability below that of root-zone when dry conditions are considered. This feature is a plausible explanation for the observed weaker relationship of remote sensing-based soil moisture (representing the surface layer) with NHD as it leads to a gradual decoupling of the surface layer from temperature under dry conditions, while root-zone soil moisture sustains more of its temporal variability.
Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review
Berger, Katja; Machwitz, Miriam; Kycko, Marlena; et al. (2022)
Remote Sensing of Environment
Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agri-culture. Ongoing developments in optical remote sensing technologies have provided tools to increase our un-derstanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric ap-proaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor com-binations. The majority of reviewed studies compared stress proxies calculated from single-source sensor do-mains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analysing multiple stress responses simultaneously (holistic view); (2) simultaneous retrieval of plant traits combining multi-domain radiative transfer models and machine learning methods; (3) assimilation of estimated plant traits from distinct spectral domains into integrated crop growth models. As a future outlook, we recom-mend combining multiple remote sensing data streams into crop model assimilation schemes to build up Digital Twins of agroecosystems, which may provide the most efficient way to detect the diversity of environmental and biotic stresses and thus enable respective management decisions.
Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX)
Schaepman, Michael E.; Jehle, Michael; Hueni, Andreas; et al. (2015)
Remote Sensing of Environment
We present the Airborne Prism Experiment (APEX), its calibration and subsequent radiometric measurements as well as Earth science applications derived from this data. APEX is a dispersive pushbroom imaging spectrometer covering the solar reflected wavelength range between 372 and 2540 nm with nominal 312 (max. 532) spectral bands. APEX is calibrated using a combination of laboratory, in-flight and vicarious calibration approaches. These are complemented by using a forward and inverse radiative transfer modeling approach, suitable to further validate APEX data. We establish traceability of APEX radiances to a primary calibration standard, including uncertainty analysis. We also discuss the instrument simulation process ranging from initial specifications to performance validation. In a second part, we present Earth science applications using APEX. They include geometric and atmospheric compensated as well as reflectance anisotropy minimized Level 2 data. Further, we discuss retrieval of aerosol optical depth as well as vertical column density of NOx, a radiance data-based coupled canopy–atmosphere model, and finally measuring sun-induced chlorophyll fluorescence (Fs) and infer plant pigment content. The results report on all APEX specifications including validation. APEX radiances are traceable to a primary standard with < 4% uncertainty and with an average SNR of > 625 for all spectral bands. Radiance based vicarious calibration is traceable to a secondary standard with ≤ 6.5% uncertainty. Except for inferring plant pigment content, all applications are validated using in-situ measurement approaches and modeling. Even relatively broad APEX bands (FWHM of 6 nm at 760 nm) can assess Fs with modeling agreements as high as R2 = 0.87 (relative RMSE = 27.76%). We conclude on the use of high resolution imaging spectrometers and suggest further development of imaging spectrometers supporting science grade spectroscopy measurements.
Scattering-feature-driven change matrix for crop growth monitoring based on the co-polar complex coherence
Luo, Jiayin; Lopez-Sanchez, Juan M.; Hajnsek, Irena; et al. (2026)
Remote Sensing of Environment
In SAR imagery, the backscattered power from scenes covering agricultural areas contains information from both soil and vegetation. External factors such as precipitation and irrigation modify soil and vegetation moisture and consequently change the backscattered power, making it difficult to isolate changes related to crop growth from them. To address this issue, the co-polar complex coherence was recently proposed as an effective tool for crop growth monitoring. In this study, a new method for extracting scattering features from the co-polar complex coherence is proposed, which is based on the distances on the complex plane with respect to the coherences associated with elementary targets. In addition, to further enhance the sensitivity to the changes in scattering features between acquisition dates, associated with phenological development, here we define a new change matrix based on these scattering features. This matrix is constructed to characterize the magnitude, direction, and type of change between any two given dates. Airborne quad-pol SAR data gathered during the AGRISAR 2006 and CROPEX 2014 campaigns were used to assess the proposed monitoring approach for three representative crop types: corn, barley, and canola. Besides a physical and a visual interpretation of the change matrix, a phenological clustering was carried out based on it. A comparison with other advanced methods used for decomposition and for generating the change matrix indicates that the proposed approach provides results less affected by fluctuations due to environmental conditions and more consistent across data sets, improving phenological clustering as a final application.

Publications 1 - 10 of 76

Journal: Remote Sensing of Environment

Abbreviation

Publisher

Journal Volumes

ISSN

Description

Filters

Search Results