Search

Natural surfaces are mostly anisotropic emitters, contributing to the anisotropic behavior of Land Surface Temperature (LST). This characteristic of thermal infrared emissivity is well known and several studies have tried to simulate this behavior either with physical or empirical models. However, given the high heterogeneity of land surfaces, the translation of the angular dependence of emissivity as provided from measurements or models into satellite pixel scale anisotropy is generally very difficult. Here we propose a reformulation of the Mean Minimum-Maximum Difference (MMD) curve of the Temperature-Emissivity Separation (TES) algorithm to allow a correct adjustment of the TES retrievals by taking into account the emissivity angular distribution. For that purpose, the Multi-Sensor method is used to obtain directional emissivities at different sites in the Saharan and Namib desert. The data is then used to calibrate the view-angle dependence of the new MMD formulation. The TES retrievals obtained with the new formulation show a good agreement with the Multi-Sensor data. Results also suggest that the new coefficients of the MMD can be applied to other sensors with similar spectral channels. The new angle-dependent emissivities may lead to a reduction of LST bias as high as 2 K for view angles above 50o. The proposed formulation is currently only valid over barren surfaces. • We propose updating the TES algorithm to incorporate emissivity directionality. • The multi-sensor method is used to create a calibration dataset. • The MMD equation was revised to include a view angle dependence. • The new formulation can be applied to sensors with similar spectral configurations. [ABSTRACT FROM AUTHOR]

Monitoring long-term landslide activity is of importance for risk assessment and land management. Daytime airborne drones or very high-resolution optical satellites are often used to create landslide maps. However, such imagery comes at a high cost, making long-term risk analysis cost-prohibitive. Despite the widespread use of open-access 30 m Landsat imagery, their utility for landslide detection is often limited due to low classification accuracy. One of the major challenges is to separate landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), with multi-seasonal daytime optical Landsat time-series, and digital elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We employed a non-parametric machine-learning classifier, random forest, to classify the satellite imagery. The classifier was trained with data from three years (2005, 2010, and 2015), and was validated with an independent reference sample from twelve years. Our results demonstrated that combining nighttime light data and multi-seasonal imagery significantly improved the classification (p < 0.001), compared to conventional methods based on single-season optical imagery. The results confirmed that the developed classification model enabled mapping of landslides across Taiwan over a long period with annual overall accuracy varying between 96% and 97%, user's and producer's accuracies between 73% and 86%. Spatiotemporal analysis of the landslide inventories from 1998 to 2017 revealed different temporal patterns of landslide activities, showing those areas where landslides were persistent and other areas where landslides tended to reoccur after vegetation regrowth. In sum, we provide a robust method to detect long-term landslide activities based on freely available satellite imagery, which can be applied elsewhere. Our mapping effort of landslide spatiotemporal patterns is expected to be of high importance in developing effective landslide remediation strategies. • Free satellite imagery accurately mapped large-scale historical landslides. • VIIRS and DMSP nighttime light data boosted landslide detection accuracy. • Multi-seasonal Landsat imagery improved landslide mapping accuracy. • A north-south gradient of landslide occurrence identified in Taiwan over 20 years. [ABSTRACT FROM AUTHOR]

The dark spectrum fitting (DSF) atmospheric correction method for aquatic application of metre-scale resolution optical satellite imagery is adapted to Landsat and Sentinel-2 (L/S2), including an automated tiled processing of full scene imagery and an optional image based glint correction. The DSF uses multiple dark targets in the subscene to construct a "dark spectrum" which is used to estimate the atmospheric path reflectance (ρ path) according to the best fitting aerosol model. This method is fully automated and can be used for full mission archive processing, as demonstrated here for a study region in the North Sea. The new approach overcomes common issues with the SWIR based exponential extrapolation approach (EXP). An evaluation of both methods is presented using L w measurements from 19 sites in the AERONET-OC network over a 15 year period and 5 satellite sensors. Overall, the DSF performs better than the EXP, with a notable improvement in the blue spectral region. The tiled processing allows for a smooth ρ path estimation for full and merged L/S2 scenes, over clear and turbid coastal waters, inland waters, and land. The DSF selects the most appropriate band automatically, i.e. the one giving the lowest atmospheric path reflectance, and hence largely avoids amplification of glint and adjacency effects in the atmospheric correction. After application of the DSF, sun glint reflectance can be estimated from the SWIR bands, and the application of a sun glint correction significantly improves data availability for these nadir viewing sensors. A consistent processing across sensors allows for the exploitation of the >30 year L/S2 archive, including Landsat 5 imagery dating back to 1984. A practical application of the DSF and the L/S2 archive is presented, where the remotely sensed water turbidity from 5 satellites is compared with in situ measurements from a long-term (2000–present) monitoring station in the southern North Sea. Unlabelled Image [ABSTRACT FROM AUTHOR]

Combining observations from multiple optical and synthetic aperture radar (SAR) satellites can provide temporally dense and regular information at medium resolution scale, independently of weather, season, and location. This has the potential to improve near real-time deforestation monitoring in dry tropical regions, where traditional optical only monitoring systems typically suffer from limited data availability due to persistent cloud cover. In this context, the recently launched Sentinel-1 satellites promise unprecedented potential, because for the first time dense and regular SAR observations are free and openly available. We demonstrate multi-sensor near real-time deforestation detection in tropical dry forests, through the combination of Sentinel-1 C-band SAR time series with ALSO-2 PALSAR-2 L-band SAR, and Landsat-7/ETM+ and 8/OLI. We used spatial normalisation to reduce the dry forest seasonality in the optical and SAR time series, and combined them within a probabilistic approach to detect deforestation in near real-time. Our results for a dry tropical forest site in Bolivia, showed that, as a result of high observation availability of Sentinel-1, deforestation events were detected more timely with Sentinel-1 than compared to Landsat and ALOS-2 PALSAR-2. The spatial and temporal accuracies of the multi-sensor approach were higher than the single-sensor results. We improved the precision of the reference data derived from the multi-sensor satellite time series, which enabled a more robust estimation of the temporal accuracy. We quantified how the near real-time deforestation detection is associated with a trade-off between the confidence in detection and the temporal accuracy. We showed that the trade-off affects the choice on how to use the near-real time data for different applications such as fast alerting with high temporal accuracy but lower confidence versus accurate detection at lower temporal detail. When aiming for a high confidence in change area estimates for example, deforestation was detected with a user's accuracy of 88%, a producer's accuracy of 89% (low area bias), and a mean time lag of 31 days using all sensors. This is on average 7 days earlier than when using only Sentinel-1 observations, and six weeks earlier than when relying only on Landsat observations. We showed that confident near real-time deforestation alerts can be provided with a mean time lag of 22 days, but these are associated with a higher commission error. With more dense time series data expected from the Sentinel-1 and -2 sensors for the upcoming decade, spatial and temporal detection accuracy of multi-sensor deforestation monitoring in the tropics will improve further. [ABSTRACT FROM AUTHOR]

The major global biogeophysical products obtained from remote sensing are usually composites of 8 or 16 days, and are almost always retrieved from a single sensor. Subsequently, the applications of these products are limited in cases requiring higher temporal frequency. With the increasing number of freely available moderate resolution remote sensing datasets, multi-sensor synergy to increase temporal sampling is warranted. However, radiometric consistency of multi-sensor data is not as good as expected; therefore, first the constituency should be evaluated. A new cross-calibration approach of reflective bands for moderate resolution remotely sensed data is proposed in this paper. The new approach uses a time series of MODIS data from both Terra and Aqua to retrieve both the bidirectional reflectance distribution function (BRDF) and aerosol optical depth (AOD) simultaneously over an invariant desert target. The MODIS retrieved BRDF and AOD are then used to cross-calibrate the medium resolution spectral imager (MERSI) and visible and infra-red radiometer (VIRR) onboard FengYun3-A and B satellites (FY3A/B) and advanced very high resolution radiometer (AVHRR) onboard NOAA-16, 17, and 18. The cross-calibration method is validated in two ways: 1) by comparing with the vicarious calibration; and 2) the top of atmosphere (TOA) reflectance before and after calibration, which show that the calibration error of this new approach is consistent within 5%. Compared with most cross-calibration methods, our new method is generic and better consider on BRDF models; furthermore, the well-built BRDF of the calibration site and simultaneously retrieved AOD make the method fully automatic and cost effective for the calibration of most moderate resolution remote sensors, especially Chinese sensors. [ABSTRACT FROM AUTHOR]

The Multi-scale Ultra-high Resolution (MUR) sea surface temperature (SST) analysis presents daily SST estimates on a global 0.01° ×0.01° grid. The current version (Version 4.1, http://dx.doi.org/10.5067/GHGMR-4FJ04 ) features the 1-km resolution MODIS retrievals, which are fused with AVHRR GAC, microwave, and in-situ SST data by applying internal correction for relative biases among the data sets. Only the night-time (dusk to dawn locally) satellite SST retrievals are used to estimate the foundation SST. The MUR SST values agree with the GHRSST Multi-Product Ensemble (GMPE) SST field to 0.36°C on average, except in summer-time Arctic region where the existing SST analysis products are known to disagree with each other. The feature resolution of the MUR SST analysis is an order of magnitude higher than most existing analysis products. The Multi-Resolution Variational Analysis (MRVA) method allows the MUR analysis to use multiple synoptic time scales, including a 5-day data window used for reconstruction of mesoscale features and data windows of only few hours for the smaller scale features. Reconstruction of fast evolving small scale features and interpolation over persistent large data voids can be achieved simultaneously by the use of multiple synoptic windows in the multi-scale setting. The MRVA method is also a “mesh-less” interpolation procedure that avoids truncation of the geolocation data during gridding and binning of satellite samples. Future improvements of the MUR SST analysis will include ingestion of day-time MODIS retrievals as well as more recent high-resolution SST retrievals from VIIRS. [ABSTRACT FROM AUTHOR]

Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, R = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 108 m3). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China. • A first country-wide 10 m building height map for China. • Shading index is the most important variable in estimating building height. • High degree of building height accuracy with RMSE of 6.1 m. [ABSTRACT FROM AUTHOR]

This paper presents a remote sensing-based method to efficiently derive multi-temporal landslide inventories over large areas, which allows for the spatiotemporal analysis of landslide activity, which is an important prerequisite in systematic regional landslide hazard and risk assessment. The developed method uses globally archived satellite remote sensing data for a retrospective systematic assessment of past multi-temporal landslide activity. Landslides are automatically identified as spatially explicit objects based on landslide-specific vegetation cover changes using temporal NDVI-trajectories and complementary relief-oriented parameters. To enable the long-term analysis of large areas with highest possible temporal resolution, the developed method facilitates the use of a large amount of optical multi-sensor time series data. The database of this study consists of 212 datasets that comprise freely available Landsat TM & ETM + data and SPOT 1 & 5, IRS1-C LISSIII, ASTER, and RapidEye data. These data were acquired between 1986 and 2013 and cover a landslide-prone area of 2500 km 2 in southern Kyrgyzstan. We identified 1583 landslide objects ranging in size between 50 m 2 and 2.8 km 2 . Spatiotemporal analysis of the landslides that were detected during these 27 years reveals continuous landslide activity of varying intensity. The highest overall landslide rates occurred in 2003 and 2004, exceeding the long-term annual average rate of 57 landslides per year by more than a factor of five. The areas of highest landslide activity are also determined, whereas most of these areas were persistent over time. [ABSTRACT FROM AUTHOR]

The water clarity of many inland water bodies is under threat due to intensifying land use pressures in conjunction with changes in water levels that result from increasing demand and climate variability. The recent launch of Landsat 8 coupled with Geoscience Australia's recent reprocessing of the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM +) archives over the whole of Australia to a consistent surface reflectance product enables sub continental scale spatio-temporal analysis of freshwater optical water quality in support of monitoring and decision making for water management agencies. In this research, we present an objective assessment of the potential of Landsat 5 TM, Landsat 7 ETM + and Landsat 8 Operational Land Imager (OLI) data for monitoring inland water quality dynamics over a number of lakes and reservoirs with a range of optical water types in New South Wales and Queensland, Australia. We used bio-optical modelling to develop sensor-specific total suspended matter (TSM) retrieval algorithms that account for the difference in relative spectral response between Landsat 7 ETM + and Landsat 8 OLI. We were able to compare the suitability of the different sensors for optical water quality measurements using water bodies that fell within Landsat path overlaps where Landsat images of surface reflectance were acquired within 24 h between Landsat 5 TM and Landsat 7 ETM + or Landsat 7 ETM + and Landsat 8 OLI. These water bodies represent a range of hydrological and limnological conditions, and enabled us to assess: 1) the comparability of TSM measurements retrieved from each sensor, and 2) the surface reflectance to image noise characteristics of Landsat 7 ETM + and Landsat 8 OLI. Comparisons of lake surface reflectance and noise equivalent reflectance difference show that the improved radiometric resolution and increased quantization of Landsat 8 OLI relative to Landsat 7 ETM + significantly reduce image noise and spectral heterogeneity, indicating that Landsat 8 OLI data are expected to provide more precise water quality retrievals relative to Landsat 7 ETM +. We found that: 1) the TSM retrievals from the different sensors are highly comparable; 2) Landsat 5 TM overestimated TSM relative to Landsat 7 ETM + by 6.4%; and 3) Landsat 7 ETM + overestimated TSM relative to Landsat 8 OLI by only 1.4%. Retrieved TSM values were highly correlated with independent in situ data acquired within 24 h of satellite overpass ( r = 0.99) with a mean average error of 14 mg/L. The results demonstrate that time series analysis of TSM retrievals can be conducted across a wide range of lakes at the sub-continental scale to characterise the multi-decadal TSM dynamics. [ABSTRACT FROM AUTHOR]

The synergy of a time series of optical satellite observations from a variety of sensors can be exploited to improve the retrieval of biophysical variables. Information from different sensors may assist in the variable retrieval by limiting potential ambiguities. This involves observations at different spatial, spectral, temporal and angular resolutions, etc. Furthermore, using timely data is of much importance for vegetation monitoring in environmental modeling. While the other necessary variables for such models can be collected daily (e.g. meteorological variables), the temporal resolution of optical sensors (high to intermediate spatial resolutions) does not allow having temporally frequent products of vegetation characteristics due to the revisit time of the sensors and cloud coverage. A multi-temporal, multi-sensor approach applied to a temporal sequence of radiometric data acquired by different sensors can improve mapping and monitoring of vegetation state variables over time. Even when no observations are available due to cloudiness or orbital configuration, the prior retrievals are taken. The study provides a prototype proof of concept for a multi-temporal, multi-sensor approach to retrieve vegetation state variables using data collected by different imaging spectral-radiometers over time. Focus is given to the retrieval of LAI, fCover and chlorophyll content over the agricultural test site in Barrax, Spain. The approach is evaluated over a limited number of remotely sensed data acquired during a short period in the Sentinel-3 Experiment (SEN3EXP 2009) field campaign. A variety of satellite observations including CHRIS-Proba, Landsat TM and ASTER are integrated and combined by means of the multi-temporal, multi-sensor approach and through inversion of a coupled surface-atmosphere radiative transfer model. We applied the iterative Bayesian model inversion in which retrievals of the current observation are incorporated as prior information to the successor observation. This paper presents an overview of the results and challenges in utilizing the multi-temporal, multi-sensor approach and the Bayesian inversion technique in the retrieval of terrestrial vegetation properties. Overall, the accuracy obtained with data acquired by multiple sensors over time was higher than when using a single sensor. LAI and fCover were retrieved with RMSE = 0.7 (m 2 /m 2 ) and 0.1 respectively (multiple sensors), while RMSE = 1.09 (m 2 /m 2 ) and 0.15 respectively, when using data acquired by a single sensor. [ABSTRACT FROM AUTHOR]

Abstract: A multi-sensor bias correction method has been developed using satellite sea surface temperature (SST) products from one microwave and five infrared sensors that cover the Arctic Ocean. The correction method has been used to construct improved single sensor and multi-sensor, merged and interpolated satellite SST products from January to December 2008. The validation of the satellite products ingested in the level 4 production shows that large biases can persist for months in this region. The SST products from the AATSR sensor on ENVISAT and the NAVOCEANO AVHRR GAC are the most stable and reliable products for the Arctic region. These products have therefore been used to construct the reference product against which the other satellite products have been corrected. The bias correction method has been developed using detailed error characteristics and thorough time-space analysis, and the bias corrected fields are validated against in situ observations from drifting buoys. All the individual satellite products show improvement in both bias and standard deviation after correction. Largest improvements are found for the Modis sensor on the Terra satellite, where biases are improved from −0.46K to 0.02K with the correction method. Temporal validation statistics reveal that extended periods with significant biases are also removed by the bias correction method. A significant improvement is seen when the corrected satellite products are used for the SST analysis. When compared against drifting buoys, not included in the analysis, the corrected level 4 satellite SST product shows a bias of −0.04K and standard deviation of 0.54K, compared to an original bias of −0.28K and standard deviation of 0.61K. The effect of a missing reference sensor is assessed for the full period. Level 4 test runs using only one reference sensor demonstrates that improvements can be obtained with a single sensor bias correction method, and that the AATSR sensor gives the largest improvements. However, using both reference sensors in the bias calculation gives significantly better performance than using just one. [Copyright &y& Elsevier]

Abstract: The magnitude, duration, and frequency of forest disturbance caused by the spruce budworm and forest tent caterpillar in northern Minnesota and neighboring Ontario, Canada have increased over the last century due to a shift in forest species composition linked to historical fire suppression, forest management, and pesticide application that has fostered increased dominance of host tree species. Modeling approaches are currently being used to understand and forecast potential management effects in changing insect disturbance trends. However, detailed forest composition data needed for these efforts is often lacking. We used partial least squares (PLS) regression to integrate different combinations of satellite sensor data including Landsat, Radarsat-1, and PALSAR, as well as pixel-wise forest structure information derived from SPOT-5 sensor data (Wolter et al., 2009), to determine the best combination of sensor data for estimating near species-level proportional forest composition (12 types: 10 species and 2 genera). Single-sensor and various multi-sensor PLS models showed distinct species-dependent sensitivities to relative basal area (BA), with Landsat variables showing greatest overall sensitivity. However, best results were achieved using a combination of data from all these sensors, with several C-band (Radarsat-1) and L-band (PALSAR) variables showing sensitivity to the composition and abundance of specific species. Pixel-level forest structure estimates derived from SPOT-5 data were generally more sensitive to conifer species abundance (especially white pine) than to hardwood species composition. Relative BA models accounted for 68% (jack pine) to 98% (maple spp.) of the variation in ground data with RMSE values between 2.46% and 5.65% relative BA, respectively. Receiver operating characteristic (ROC) curves were used to determine the effective lower limits of usefulness of species relative BA estimates which ranged from 5.94% (jack pine) to 39.41% (black ash). These estimates were then used to produce a dominant forest species map for the study region with an overall accuracy of 78%. Most notably, this approach facilitated discrimination of aspen from paper birch as well as spruce and fir from other conifer species which is crucial for the study of forest tent caterpillar and spruce budworm dynamics in the Upper Midwest. We also demonstrate that PLS regression is an effective data fusion strategy for mapping composition of heterogeneous forests using satellite sensor data. [Copyright &y& Elsevier]

Abstract: In our increasingly urbanized world, monitoring and mapping of urban growth and thereby induced land-use and land-cover change (LULCC) is of emergent importance. Remote sensing can reveal spatio-temporal growth trajectories of cities, which again allow a thorough understanding of the impacts of urbanization on ecosystems and ecosystem services. However, the mapping of urban areas remains one of the most challenging tasks of remote sensing data analysis. This paper presents an approach to map urban growth from multi-sensoral data, exemplified for the Dhaka megacity region in Bangladesh between 1990 and 2006. The approach is globally applicable and can facilitate regional urban growth maps in arbitrary complex and dynamic environments. Dhaka''s densified urban landscape, its deltaic locality and the highly dynamic monsoon-related phenology call for a sophisticated analysis approach that is able to separate intra-annual land-cover variations from actual urbanization. Imagery from the Landsat series of satellites is a great asset for such an analysis due to its synoptic coverage of large urban areas as well as its unique historical archives. In our approach, we solve problems of spectral ambiguities and seasonal phenological dynamics through incorporating multi-temporal imagery for each monitoring year (1990, 2000, and 2006) and by extending the spectral feature space with synthetic aperture radar (SAR) data. The resulting datasets are heterogeneous and comprise measurements of unequal scaling. Non-parametric classification algorithms are required to delineate multi-modal and non-Gaussian class distributions of heterogeneous as well as temporally and spectrally complex land-cover classes of interest in such an extended feature space. We therefore used a support vector machine (SVM) classifier and post classification comparison to reveal spatio-temporal patterns of urban land-use and land-cover changes. An SVM based forward feature selection procedure allowed deriving in-depth information about the individual contribution of different input bands. Our methodology delineated relevant land-cover classes and resulted in overall accuracies better than 83% for all years considered. Change analysis unveiled a profound expansion of urban areas at the expense of prime agricultural areas and wetlands. During the 1990s, change was primarily characterized by a densification of urban fabric, whereas more recent changes included vast in-filling of low lying land and an extensive industrial sprawl into Dhaka''s peri-urban areas. Our multi-sensoral and multi-temporal mapping approach allowed for delineating temporally dynamic LULCC, which again allowed for an insightful characterization of land system changes in the megacity region of Dhaka. [Copyright &y& Elsevier]

Abstract: A sequential model is developed to disaggregate microwave-derived soil moisture from 40km to 4km resolution using MODIS (Moderate Imaging Spectroradiometer) data and subsequently from 4km to 500m resolution using ASTER (Advanced Scanning Thermal Emission and Reflection Radiometer) data. The 1km resolution airborne data collected during the three-week National Airborne Field Experiment 2006 (NAFE''06) are used to simulate the 40km pixels, and a thermal-based disaggregation algorithm is applied using 1km resolution MODIS and 100m resolution ASTER data. The downscaled soil moisture data are subsequently evaluated using a combination of airborne and in situ soil moisture measurements. A key step in the procedure is to identify an optimal downscaling resolution in terms of disaggregation accuracy and sub-pixel soil moisture variability. Very consistent optimal downscaling resolutions are obtained for MODIS aboard Terra, MODIS aboard Aqua and ASTER, which are 4 to 5 times the thermal sensor resolution. The root mean square error between the 500m resolution sequentially disaggregated and ground-measured soil moisture is 0.062vol./vol. with a bias of −0.045vol./vol. and values ranging from 0.08 to 0.40vol./vol. [Copyright &y& Elsevier]

A multi-sensor/multi-platform approach to water and energy cycle prediction is demonstrated in an effort to understand the variability and feedback of land surface and atmospheric processes over large space and time scales. Remote sensing-based variables including soil moisture (from AMSR-E), surface heat fluxes (from MODIS) and precipitation rates (from TRMM) are combined with North American Regional Reanalysis derived atmospheric components to examine the degree of hydrological consistency throughout these diverse and independent hydrologic data sets. The study focuses on the influence of the North American Monsoon System (NAMS) over the southwestern United States, and is timed to coincide with the SMEX04 North American Monsoon Experiment (NAME). The study is focused over the Arizona portion of the NAME domain to assist in better characterizing the hydrometeorological processes occurring across Arizona during the summer monsoon period. Results demonstrate that this multi-sensor approach, in combination with available atmospheric observations, can be used to obtain a comprehensive and hydrometeorologically consistent characterization of the land surface water cycle, leading to an improved understanding of water and energy cycles within the NAME region and providing a novel framework for future remote observation and analysis of the coupled land surface–atmosphere system. [Copyright &y& Elsevier]

Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and environmental data. Our results show high overall accuracy and plausible class accuracies for the most dominant crop types across different years despite the strong inter-annual meteorological variability and the presence of drought and non-drought years. The maps show high spatial consistency and good delineation of field parcels. Combining optical, SAR, and environmental data increased overall accuracies by 6% to 10% compared to single sensor approaches, in which optical data outperformed SAR. Overall accuracy ranged between 78% and 80%, and the mapped areas aligned well with agricultural statistics at the regional and national level. Based on the multi-year dataset we mapped major crop sequences of cereals and leaf crops. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. We showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. Our results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping. • Large-area crop type mapping without region−/class-specific feature selection. • Integration of data describing local and seasonal environmental conditions. • 24 agricultural land cover classes at national scale and for multiple years. • High accuracy despite strong inter-annual meteorological variability. • Combined crop type maps enable crop sequence analysis at national scale. [ABSTRACT FROM AUTHOR]

Dense time series of Landsat 8 and Sentinel-2 imagery are creating exciting new opportunities to monitor, map, and characterize temporal dynamics in land surface properties with unprecedented spatial detail and quality. By combining imagery from the Landsat 8 Operational Land Imager and the MultiSpectral Instrument on-board Sentinel-2A and -2B, the remote sensing community now has access to moderate (10–30 m) spatial resolution imagery with repeat periods of ~3 days in the mid-latitudes. At the same time, the large combined data volume from Landsat 8 and Sentinel-2 introduce substantial new challenges for users. Land surface phenology (LSP) algorithms, which estimate the timing of phenophase transitions and quantify the nature and magnitude of seasonality in remotely sensed land surface conditions, provide an intuitive way to reduce data volumes and redundancy, while also furnishing data sets that are useful for a wide range of applications including monitoring ecosystem response to climate variability and extreme events, ecosystem modelling, crop-type discrimination, and land cover, land use, and land cover change mapping, among others. To support the need for operational LSP data sets, here we describe a continental-scale land surface phenology algorithm and data product based on harmonized Landsat 8 and Sentinel-2 (HLS) imagery. The algorithm creates high quality times series of vegetation indices from HLS imagery, which are then used to estimate the timing of vegetation phenophase transitions at 30 m spatial resolution. We present results from assessment efforts evaluating LSP retrievals, and provide examples illustrating the character and quality of information related to land cover and terrestrial ecosystem properties provided by the continental LSP dataset that we have developed. The algorithm is highly successful in ecosystems with strong seasonal variation in leaf area (e.g., deciduous forests). Conversely, results in evergreen systems are less interpretable and conclusive. • We present a new algorithm that maps continental scale land surface phenology at 30 m resolution. • Results capture fine scale variation in phenology related to land use, land cover and topography. • Comparisons against PhenoCam and MODIS data demonstrate the high quality of estimated metrics. [ABSTRACT FROM AUTHOR]

Accurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi-temporal change detection techniques have contributed greatly to landslide inventory preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi-sensor data. The results demonstrate that the improved δ NDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F -measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data. • NDVI, PCA, and ICA are integrated into MRF for landslide mapping. • Improved CDMRF for landslide mapping from multi-sensor data • NDVI-, PCA-, and ICA-based MRF outperform CVA-based MRF significantly • Applicable to map rainfall-, typhoon-, and earthquake-triggered landslides [ABSTRACT FROM AUTHOR]