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Water table constitutes a master control of the general biogeochemistry in northern peatlands. The performance of peatland simulations in global ecosystem models is strongly hampered by the accuracy of the water table predictions. We examined the applicability of the Optical TRApezoid Model (OPTRAM) to monitor the temporal fluctuations in water table over 53 intact, restored, and drained northern peatlands in Finland, Estonia, Sweden, Canada, and the USA from 2018 through 2021. Various OPTRAM were computed based on Sentinel-2 data with the Google Earth Engine cloud platform. We found that (i) the choice of vegetation index utilised in OPTRAM does not significantly affect OPTRAM performance; (ii) the tree cover density is a significant factor controlling the sensitivity of OPTRAM to water table dynamics; (iii) the relationship between water table and OPTRAM often disappears for deep water tables. Based on an anomaly analysis, we further found that OPTRAM seems to be in particular suitable to monitor long-term (i.e., interannual) water table variability while the performance for short-term changes (e.g., response to individual rain events) was lower. Overall, our results support the application of OPTRAM to monitor water table dynamics in intact and restored northern peatlands with low tree cover density when the water table is shallow to moderately deep.

Over the recent two decades, land use/land cover (LULC) drastically changed in Estonia. Even though the population decreased by 11%, noticeable agricultural and forest land areas were turned into urban land. In this work, we analyzed those LULC changes by mapping the spatial characteristics of LULC and urban expansion in the years 2000-2019 in Estonia. Moreover, using the revealed spatiotemporal transitions of LULC, we simulated LULC and urban expansion for 2030. Landsat 5 and 8 data were used to estimate 147 spectral-textural indices in the Google Earth Engine cloud computing platform. After that, 19 selected indices were used to model LULC changes by applying the hybrid artificial neural network, cellular automata, and Markov chain analysis (ANN-CA-MCA). While determining spectral-textural indices is quite common for LULC classifications, utilization of these continues indices in LULC change detection and examining these indices at the landscape scale is still in infancy. This country-wide modeling approach provided the first comprehensive projection of future LULC utilizing spectral-textural indices. In this work, we utilized the hybrid ANN-CA-MCA model for predicting LULC in Estonia for 2030; we revealed that the predicted changes in LULC from 2019 to 2030 were similar to the observed changes from 2011 to 2019. The predicted change in the area of artificial surfaces was an increased rate of 1.33% to reach 787.04 km

This research is focused on identifying urban sprawl pattern and extent in two rapidly growing major Ghanaian cities (Accra and Kumasi) and how urban expansion affected heat island effect over the period of 2002–2017 using remote sensing imagery. The research employed remotely sensed images from Landsat 7 and 8 missions for mapping the urban sprawl. Land cover classification was done by using object-based image analysis, and for land surface temperature estimation single-channel algorithm was used. The intensity and magnitude of urban heat island were estimated. The results showed that urban expansion was more dominant process than densification in both cities. A significant area of bare soils and sparsely vegetated lands became built-up accompanied by total disappearance of forest belt of Kumasi. The intensity and magnitude values indicated the presence and expansion of urban heat island in both cities. However, there was a significant amount of bare lands and sparsely vegetated areas with relatively high surface temperature in and around these cities. From the results of this work, we note that bare or sparsely vegetated land cover types in urban areas located in tropical climates can escalate overall urban temperatures. The urban heat island magnitude values were relatively higher compared to values for European cities during the heat wave of 2016. Although urban configurations and climatic conditions may be the reason for the differences, this shows how alarming and dangerous urban heat islands could be in tropical cities.

The estimation of root traits and root growth dynamics is still a major challenge in plant belowground ecology. The urgent need for root data has stimulated the development of methodologies that led to an explosive increase in root trait data in recent years. To understand the challenges raising from rapid innovation, we did a systematic review 157 articles, published from 2000 to 2021, where the root traits measurements were performed using RGB images. Our goal was to explore 1) how the imaging technologies used in root research have progressed in recent 20-years, 2) what are the dominating sensors and software used for imaging and image processing, 3) what are the challenges on our way towards machine readability of root traits. We discussed our results of the timeline dynamics analysis of root traits measurements based on RGB images, utilised sensors, type of root images and software used for image processing, but also the developments in the national and international collaborations in root research. Finally, and most importantly, we explored what are the most examined functional groups of plants, average number of species included in the study and the age of plants which roots were studied. We identified the most measured root traits and analysed the variety of terms used for definition of root traits. In the light of this, we discussed the challenges in root trait terminology on our way towards machine-readable root data.

A new insight in root growth dynamics is presented in this study, where pictures of root growth were taken with personal mobile phones and analysed by the machine learning based program RootPainter (Smith et al. 2020). Todays’ smartphones provide high-quality photos and user-friendly free software enables rapid processing of these images. We aimed to explore 1) how accurate the results are of the deep learning segmentation models created for assessing root growth, 2) how the changes in relative air humidity and dominating soil nitrogen source and their interactions influence root growth.Picea abies trees were grown separately in transparent boxes in growth chambers in moderate or elevated air humidity and on nitrate or ammonium soil source. The pictures of roots were made from each side of boxes every week, together six sessions. The pictures were analysed with RootPainter twice, one where total root projection area was measured, second with only young white roots.The total root growth was highest in trees growing in moderate air humidity and on ammonium source, lowest in elevated air humidity grown on nitrate source, 9.4 ± 1.9 and 3.9 ± 0.6 cm2, respectively. The young root projection area was highest in the beginning of experiment, and was affected by the soil nitrogen source. The amount of lignified roots increased over time and was affected by the air humidity treatment. The F measure was 0.88, when we compared a subset of automatically measured pictures to manually annotated pictures. We will further discuss about the magnitude of the errors 1) where the program identified “root as soil” and “soil as root”, and 2) where the root projection area of young roots was greater than the total root projection area. We did not discover treatment-specific bias in our error measurements. We conclude that the combination of smartphone images and RootPainter gives accurate and reliable results and is easy to use in plant growth manipulation experiments in the future.Smith AG, Han E, Petersen J, Olsen NAF, Giese C, Athmann M, Dresbøll DB, Thorup-Kristensen K. 2020. RootPainter: Deep learning segmentation of biological images with corrective annotation. bioRxiv, doi:10.1101/2020.04.16.044461

Over the past decade, we witnessed a rapid growth in the use of social media data when assessing cultural ecosystem services (CESs), like modelling the supply-demand relationships. Researchers increasingly use user-generated content (predominantly geotagged pictures and texts from Flickr, Twitter, VK.com) as a spatially explicit proxy of CES demand. However, for modelling CES supply most of such studies relied on simplistic geospatial data, such as land cover and digital elevation models. As a result, our understanding of the favourable environmental conditions underlying good landscape experience remains weak and overly generic.Our study aims to detect the spatial disparities between population density and CES supply in Estonia in order to prioritise them for further in-depth CES assessment and green and blue infrastructure improvements. We relied on Flickr and VK.com photographs to detect the usage of three CESs: passive landscape watching, active outdoor recreation, and wildlife watching (biota observations at organism and community levels) with automated image content recognition via Clarifai API and subsequent topic modelling. Then, we used Landsat-8 cloudless mosaic, digital elevation and digital surface models, as well as land cover model to derive 526 environmental variables (textural, spectral indices and other indicators of landscape physiognomy) via the Google Earth Engine platform. We conducted an ensemble environmental niche modelling to analyse the relative strength and directions of relationships between these predictors and the observed occurrence of CES demand. Based on multicollinearity and relative importance analysis, we selected 21 relevant and non-collinear indicators of CES supply. With these indicators as inputs, we then trained five models, popular in environmental niche modelling: Boosted Regression Trees, Generalized Linear Model, Multivariate Adaptive Regression Spline, Maxent, and Random Forest. Random Forest performed better than the other models for all three CES types, with the average 10-fold cross-validation area under curve > 0.9 for landscape watching, >0.87 for outdoor recreation, and >0.85 for wildlife watching. Our modelling allowed us to estimate the share of the Estonian population residing in the spatial clusters of systematically high and low environmental suitability for three considered CESs. The share of the population residing in the clusters of low environmental suitability for landscape watching, outdoor recreation, and wildlife watching is 5.5%, 3.1%, and 7.3%, respectively. These results indicate that dozens of thousands of people in Estonia (population is >1.3 million) likely have fewer opportunities for everyday usage of considered CESs. However, these results are biased as there was not enough evidence in social media for CES use in some of these areas.Although our results should be treated with caution, because social media data are likely to contain a considerable sampling bias, we have demonstrated the added value of remote sensing data for CES supply estimation. Given nearly global and continuously updated satellite imagery archives, remote sensing opens new perspectives for monitoring the loss and gains in landscape suitability for CES across temporal and spatial scales. As such, we can better account for the intangible underlying geospatial features that can influence economic and environmental decision-making.

https://doi.org/10.1029/2021JG006411 Remotely sensed land surface temperature (LST) enables global modeling and monitoring of CO2 fluxes from peatlands. We aimed to provide the first overview of the potential for using LST to monitor ecosystem respiration (R-eco) in disturbed (drained and extracted) peatlands. We used chamber-measured data (2017-2020) from five disturbed and two intact northern peatlands and LST data from Landsat 7, 8, and MODIS missions. First, we studied the strength of the relationships between fluxes and their in situ drivers (i.e., thermal and moisture conditions). Second, we examined the association between LST and in situ temperatures. Third, we compared chamber-measured R-eco with the modeled R-eco driven by in situ measured water table depth and (a) in situ measured surface temperature and (b) remotely sensed MODIS LST data. In situ temperatures were a stronger driver of CO2 fluxes in disturbed sites (repeated measures correlation rmR = 0.8-0.9) than in intact ones (rmR = 0.5-0.8). LST had a higher association with in situ measured temperatures in disturbed sites (mean rmR = 0.79 for MODIS) and weaker in the intact (hummocks and hollows) peatlands (mean rmR = 0.38 for Landsat and 0.48 for MODIS). R-eco models driven by MODIS LST and in situ surface temperature yielded similar accuracy: R-2 was 0.27, 0.66, and 0.67 and 0.29, 0.70, and 0.66 for intact and for drained and extracted sites, respectively. Overall, these findings suggest the applicability of LST as a proxy of the thermal regime in R-eco models, particularly for disturbed peatlands.

Funding Information: This research was supported by European Social Fund?s Dora Plus Programme (Grant No. 4-12/89). Authors are grateful to Ms Joanna Storie (Estonian University of Life Sciences) for English editing and proofreading, applied to the text. Publisher Copyright: © 2019, Springer Nature B.V. In 1939, Carl Troll pointed out that “air photo interpretation is to a large extent landscape ecology”. From that time forward, remote sensing has been applied across different disciplines to comprehend the holistic and dynamic spatial layout of the visual Earth environment. However, its applicability in the domain of landscape character assessment, landscape design and planning is still questionable. The purpose of this paper was to synthesise some historical and current applications of remote sensing for the decomposition of the continual visual landscape from a bird’s eye perspective and to explore the potential for bridging geographic processes with visual perception and an appreciation of the landscape pattern. From the point of view of landscape ecology, the organisation of the landscape pattern [namely, the size, shape (form), number, density and diversity, the complexity of landscape elements, and colours and textures of the land cover] is crucial for the cognition of both the visual landscape experience and the geographic processes. There are numerous pieces of evidence from the literature that remote sensing data are widely implemented in the modelling of physiognomic landscape. The synthesis of the literature concludes with perspective directions of remote sensing applications, such as mapping the status of the ecosystem (landscape) services provision, the delineation of the boundaries of the protected areas based on the quality of the visual environment, and the assessment of the sustainability of the land use practices, regarding their impact on landscape aesthetics extent.

Fluctuations of water table depth (WTD) affect many processes in peatlands, such as vegetation development and emissions of greenhouse gases. Here, we present the OPtical TRApezoid Model (OPTRAM) as a new method for satellite-based monitoring of the temporal variation of WTD in peatlands. OPTRAM is based on the response of short-wave infrared reflectance to the vegetation water status. For five northern peatlands with long-term in-situ WTD records, and with diverse vegetation cover and hydrological regimes, we generate a suite of OPTRAM index time series using (a) different procedures to parametrise OPTRAM (peatland-specific manual vs. globally applicable automatic parametrisation in Google Earth Engine), and (b) different satellite input data (Landsat vs. Sentinel-2). The results based on the manual parametrisation of OPTRAM indicate a high correlation with in-situ WTD time-series for pixels with most suitable vegetation for OPTRAM application (mean Pearson correlation of 0.7 across sites), and we will present the performance differences when moving from a manual to an automatic procedure. Furthermore, for the overlap period of Landsat and Sentinel-2, which have different ranges and widths of short-wave infrared bands used for OPTRAM calculation, the impact of the satellite input data to OPTRAM will be analysed. Eventually, the challenge of merging different satellite missions in the derivation of OPTRAM time series will be explored as an important step towards a global application of OPTRAM for the monitoring of WTD dynamics in northern peatlands.

This study explored the potential of optical and thermal satellite imagery to monitor temporal and spatial changes in the position of the water table depth (WTD) in the peat layer of northern bogs. We evaluated three different trapezoid models that are proposed in the literature for soil moisture monitoring in regions with mineral soils. Due to the tight capillary connection between water table and surface soil moisture, we hypothesized that the soil moisture indices retrieved from these models would be correlated with WTD measured in situ. Two trapezoid models were based on optical and thermal imagery, also known as Thermal-Optical TRApezoid Models (TOTRAM), and one was based on optical imagery alone, also known as the OPtical TRApezoid Model (OPTRAM). The models were applied to Landsat imagery from 2008 to 2019 and the derived soil moisture indices were compared with in-situ WTD from eight locations in two Estonian bogs. Our results show that only the OPTRAM index was significantly (p-value < 0.05) correlated in time with WTD (average Pearson correlation coefficient of 0.41 and 0.37, for original and anomaly time series, respectively), while the two tested TOTRAM indices were not. The highest temporal correlation coefficients (up to 0.8) were observed for OPTRAM over treeless parts of the bogs. An assessment of the spatial correlation between soil moisture indices and WTD indicated that all three models did not capture the spatial variation in water table depth. Instead, the spatial patterns of the indices were primarily attributable to vegetation patterns.

Groundwater table depth and peat moisture, exert a first order control on a range of biogeochemical and -physical peatland processes, and the susceptibility to peat fires. Therefore, one of the first critical measures to identify “peatlands under pressure” is the change of hydrological conditions, e.g. due to changing climatic conditions or direct “hydraulic” human influence. In this presentation, we introduce a new opportunity for the global-scale monitoring of moisture conditions in peatlands. We assimilate L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) into the Catchment land surface model (CLSM) to improve the simulation of Northern peatland hydrology from 2010 through 2019. We compare four simulation experiments: two open loop and two data assimilation simulations, either using the default CLSM or a recently-developed peatland-specific adaptation of it (PEATCLSM, Bechtold et al. 2019). The assimilation system uses a spatially distributed ensemble Kalman filter to update soil moisture and groundwater table depth. The simulation experiments are evaluated against an in-situ dataset of groundwater table depth in about 20 natural and semi-natural peatlands that are large enough to be dominant in the corresponding 81-km2 model grid cells. For PEATCLSM, Tb data assimilation increases the temporal Pearson correlation (R) and anomaly correlation (aR) between simulated and measured groundwater table from 0.53 and 0.38 (open-loop) to 0.58 and 0.45 (analysis), respectively. Time series comparison at monitoring sites demonstrates how the assimilation effectively corrects for remaining deficiencies in model physics and/or errors of the global meteorological data forcing the model. The generally lower coefficients of 0.30 (R) and 0.09 (aR) for the default CLSM also improve after Tb data assimilation to values of 0.39 (R) and 0.28 (aR). However, even with Tb data assimilation, the skill of CLSM remains inferior to that of PEATCLSM. The more realistic model physics of PEATCLSM are also supported by a reduction of the Tb misfits (observed Tb – forecasted Tb) over 94 % of the Northern peatland area. The temporal variance of Tb misfits is reduced by 20 % on average and is largest over the large peatland areas of the Western Siberian (25 %) and Hudson Bay Lowlands (40 %). This study demonstrates, for the first time, an improved estimation of the peatland hydrological dynamics by the assimilation of SMOS L-band brightness data into a global land surface model and suggests a new route of research focusing on the incorporation of additional satellite observations into peatland-specific modeling schemes.Bechtold, M., De Lannoy, G.J M., Koster, R.D., Reichle, R.H., et al. (2019). PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 11 (7), 2130-2162. doi: 10.1029/2018MS001574.

Groundwater table depth and peat moisture content are of crucial importance for many peatland processes, like for example their greenhouse gas budget. Thus, there is a strong need for remote sensing techniques that allow to spatially monitor these critical moisture conditions to quantify the hydrological responses to climate change and other anthropogenic disturbances. Previous studies have demonstrated the usefulness but also limitations of microwave observations for peatland moisture monitoring at the large scale. Here, we explore the potential of techniques based on optical and thermal imagery for smaller scale applications.Satellite-derived land surface temperature (LST) as well as shortwave infrared transformed reflectance (STR) are sensitive to soil moisture conditions in mineral soils. Both data form, together with remotely sensed vegetation indices (VIs), trapezoids in the LST-VI and STR-VI space with the highest range of possible LST and STR for bare soil conditions. The lowest and highest LST and STR along the vegetation cover gradient define the wet and dry edge, respectively. In this study, we used Landsat 7 and Landsat 8 satellite data for the vegetation periods from 2008 through 2019 to calculate various VIs, LST and STR for hemiboreal raised bogs in Estonia. Two common approaches for the determination of wet and dry edges for the LST-based method were applied and compared. The first approach estimates the edges directly from the observed values of VIs and LST for each scene; while the second one relies on modelled theoretical edges for each scene. In contrast, the STR-VI trapezoid is derived from observed values from all scenes as proposed in literature. The trapezoids are used to calculate the dryness index of each Landsat pixel by linearly scaling between the wet and dry edge. These indices are evaluated with measured groundwater table depth time series. Preliminary results indicate that, for our study area, suitable LST-based trapezoids cannot be derived from satellite observations alone, indicated by the low dependency of the resulting dryness index on groundwater table depth. Evaluation of the theoretically-derived trapezoids and the STR-VI is ongoing and will be discussed.

There is an urgent need to include northern peatland hydrology in global Earth system models to better understand land-atmosphere interactions and sensitivities of peatland functions to climate change, and, ultimately, to improve climate change predictions. In this study, we introduced for the first time peatland-specific model physics into an assimilation scheme for L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) mission to improve groundwater table estimates. We conducted two sets of model-only and data assimilation experiments using the Catchment Land Surface Model (CLSM), applying (over peatlands only) in one of them a peatland-specific adaptation (PEATCLSM). The evaluation against in-situ measurements of peatland groundwater table depth indicates the superiority of PEATCLSM model physics and additionally improved performance after assimilating SMOS Tb observations. The better performance of PEATCLSM over nearly all Northern Hemisphere peatlands is further supported by the better agreement between SMOS Tb observations and Tb estimates from the model-only and data assimilation runs. Within the data assimilation scheme, PEATCLSM reduces Tb observation-minus-forecast residuals and leads to reduced data assimilation updates of water storage components and, thus, reduced water budget imbalances in the assimilation system. ispartof: Remote Sensing Of Environment vol:246 status: Published online