scholarly journals GO WIDE TO GO DEEP: AFFORDABLE, REPLICABLE ROBOTIC MINIRHIZOTRON SAMPLING FOR PHENOLOGY STUDIES

2022 ◽  
Author(s):  
Richard Nair ◽  
Martin Strube ◽  
Martin Hertel ◽  
Olaf Kolle ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Root Biomass ◽  
Vegetation Index ◽  
Current Method ◽  
Belowground Biomass ◽  
Time Of Day ◽  
Ground Vegetation ◽  
High Temporal Resolution ◽  
High Resolution Data

Minirhizotrons (paired camera systems and buried observatories) are the best current method to make repeatable measurements of fine roots in the field. Automating the technique is also the only way to gather high resolution data necessary for comparison with phenology-relevant above-ground remote sensing, and, when appropriately validated, to assess with high temporal resolution belowground biomass, which can support carbon budgets estimates. Minirhizotron technology has been available for half a century but there are many challenges to automating the technique for global change experiments. Instruments must be cheap enough to replicate on field scales given their shallow field of view, and automated analysis must both be robust to changeable soil and root conditions because ultimately, image properties extracted from minirhizotrons must have biological meaning. Both digital photography and computer technology are rapidly evolving, with huge potential for generating belowground data from images using modern technological advantages. Here we demonstrate a homemade automatic minirhizotron scheme, built with off-the-shelf parts and sampling every two hours, which we paired with a neural network-based image analysis method in a proof-of-concept mesocosm study. We show that we are able to produce a robust daily timeseries of root cover dynamics. The method is applied at the same model across multiple instruments demonstrating good reproducibility of the measurements and a good pairing with an above-ground vegetation index and root biomass recovery through time. We found a sensitivity of the root cover we extracted to soil moisture conditions and time of day (potentially relating to soil moisture), which may only be an issue with high resolution automated imagery and not commonly reported as encountered when training neural networks on traditional, time-distinct minirhizotron studies. We discuss potential avenues for dealing with such issues in future field applications of such devices. If such issues are dealt with to a satisfactory manner in the field, automated timeseries of root biomass and traits from replicated instruments could add a new dimension to phenology understanding at ecosystem level by understanding the dynamics of root properties and traits.

scholarly journals A pan-African high-resolution drought index dataset

2019 ◽  
Author(s):  
Jian Peng ◽  
Simon Dadson ◽  
Feyera Hirpa ◽  
Ellen Dyer ◽  
Thomas Lees ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Root Zone ◽  
Environmental Data ◽  
Climatic Research Unit ◽  
Drought Vulnerability ◽  
Coarse Resolution ◽  
Mass Migration

Abstract. Droughts in Africa cause severe problems such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security over Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectorial perspective that includes crops, hydrological systems, rangeland, and environmental systems. Such assessments are essential for policy makers, their advisors, and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time-Series (TS) datasets, and Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project, as well as with root zone soil moisture modelled by GLEAM. Agreement found between coarse resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information to study drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) with link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a)

scholarly journals Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1726 ◽  
Author(s):  
Yizhi Han ◽  
Xiaojing Bai ◽  
Wei Shao ◽  
Jie Wang
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Vegetation Index ◽  
Coupled Model ◽  
Agricultural Drought ◽  
Agricultural Fields ◽  
Backscattering Coefficient ◽  
Modis Data ◽  
Vegetation Parameters ◽  
Scattering Contribution

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

scholarly journals Electric vehicle charging stations in the workplace with high-resolution data from casual and habitual users

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Omar Isaac Asensio ◽  
M. Cade Lawson ◽  
Camila Z. Apablaza
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Electric Vehicle ◽  
Optimal Power Flow ◽  
Service Providers ◽  
High Temporal Resolution ◽  
High Resolution Data ◽  
Demand Models ◽  
Charging Stations ◽  
Resolution Data

AbstractProblems of poor network interoperability in electric vehicle (EV) infrastructure, where data about real-time usage or consumption is not easily shared across service providers, has plagued the widespread analysis of energy used for transportation. In this article, we present a high-resolution dataset of real-time EV charging transactions resolved to the nearest second over a one-year period at a multi-site corporate campus. This includes 105 charging stations across 25 different facilities operated by a single firm in the U.S. Department of Energy Workplace Charging Challenge. The high-resolution data has 3,395 real-time transactions and 85 users with both paid and free sessions. The data has been expanded for re-use such as identifying charging behaviour and segmenting user groups by frequency of usage, stage of adoption, and employee type. Potential applications include but are not limited to simulating and parameterizing energy demand models; investigating flexible charge scheduling and optimal power flow problems; characterizing transportation emissions and electric mobility patterns at high temporal resolution; and evaluating characteristics of early adopters and lead user innovation.

scholarly journals A High-Resolution Dataset of Water Fluxes and States for Germany Accounting for Parametric Uncertainty

2016 ◽  
Author(s):  
Matthias Zink ◽  
Rohini Kumar ◽  
Matthias Cuntz ◽  
Luis Samaniego
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Recharge ◽  
Land Surface ◽  
Large Scale ◽  
Parametric Uncertainty ◽  
Model Parameters ◽  
High Resolution Data ◽  
Daily Discharge ◽  
Resolution Dataset

Abstract. Long term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture and generated runoff at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed runoff in 222 catchments, which are not used for model calibration. The mean (standard deviation) of the ensemble median NSE estimated for these catchments is 0.68 (0.09) for daily discharge simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.

The synergistic use of Sentinel SAR and optical remote sensing for mapping high-resolution soil moisture

2020 ◽  
Author(s):  
Jianxiu Qiu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Large Scale ◽  
Vegetation Index ◽  
Estimation Accuracy ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Biological Variables ◽  
Research Activities

<p>The launch of series of Sentinel constellations has provided data continuity of ERS, Envisat, and SPOT-like observations, in order to meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities. The synergistic use of this publicly-accessible SAR images and temporally collocated optical remote sensing datasets has provided great potential for estimating high-resolution soil moisture information. In this study, advanced integral equation model (AIEM) which simulates the backscattering coefficient of bare soil and the Water-Cloud Model (WCM) accounting for the scattering effect from vegetation, are coupled to map high-resolution soil moisture. Validation conducted in large-scale campaign of Heihe Watershed Allied Telemetry Experimental Research (HiWATER-MUSOEXE) in northwest of China showed RMSE of 0.04~0.071 m3m3. In addition, the accuracies in describing vegetation contribution from backscatter coefficient were intercompared between different models including WCM and ratio vegetation model. Sensitivity analysis of soil moisture estimation accuracy to vegetation index also extends to different optical remote sensing data sets including Sentinel-2, Landsat 8 and MODIS.</p>

scholarly journals Technical note: Improving the AWAT filter with interpolation schemes for advanced processing of high resolution data

2016 ◽  
Author(s):  
Andre Peters ◽  
Thomas Nehls ◽  
Gerd Wessolek
Keyword(s):  
High Resolution ◽  
Spline Interpolation ◽  
Selection Criterion ◽  
Adaptive Threshold ◽  
Technical Note ◽  
Measuring System ◽  
High Temporal Resolution ◽  
Data Set ◽  
High Resolution Data ◽  
Linear Scheme

Abstract. Weighing lysimeters with appropriate data filtering yield the most precise and unbiased information for precipitation (P) and evapotranspiration (ET). A recently introduced filter scheme for such data is the AWAT (Adaptive Window and Adaptive Threshold) filter [Peters, A., Nehls, T., Schonsky, H., and Wessolek, G.: Separating precipitation and evapotranspiration from noise – a new filter routine for high-resolution lysimeter data, Hydrol. Earth Syst. Sci., 18, 1189–1198, doi:10.5194/hess-18-1189-2014, 2014]. The filter applies an adaptive threshold to separate significant from insignificant mass changes, guaranteeing that P and ET are not overestimated, and uses a step interpolation between the significant mass changes. In this contribution we show that the step interpolation scheme, which reflects the resolution of the measuring system, can lead to unrealistic prediction of P and ET, especially if they are required in high temporal resolution. We introduce linear and spline interpolation schemes to overcome these problems. To guarantee that medium to strong precipitation events abruptly following low or zero fluxes are not smoothed in an unfavourable way, a simple heuristic selection criterion is used, which attributes such precipitations to the step interpolation. The three interpolation schemes (step, linear and spline) are tested and compared using a data set from a grass-reference lysimeter with one minute resolution, ranging from 1 January to 5 August 2014. The selected output resolutions for P and ET prediction are one day, one hour and 10 minutes. As expected, the step scheme yielded reasonable flux rates only for a resolution of one day, whereas the other two schemes are well able to yield reasonable results for any resolution. The spline scheme returned slightly better results than the linear scheme concerning the differences between filtered values and raw data. Moreover, this scheme allows continuous differentiability of filtered data so that any output resolution for the fluxes is sound. Since computational burden is not problematic for any of the interpolation schemes, we suggest to use always the spline scheme.

scholarly journals Recent highlights from Cluster, the first 3-D magnetospheric mission

2015 ◽  
Vol 33 (10) ◽  
pp. 1221-1235 ◽  
Author(s):  
C. P. Escoubet ◽  
A. Masson ◽  
H. Laakso ◽  
M. L. Goldstein
Keyword(s):  
Solar Wind ◽  
High Resolution ◽  
Magnetic Reconnection ◽  
High Temporal Resolution ◽  
Science Data ◽  
High Resolution Data ◽  
Cluster Data ◽  
Time Period ◽  
Wide Range ◽  
First Time

Abstract. The Cluster mission has been operated successfully for 14 years. During this time period, the evolution of the orbit has enabled Cluster to sample many more magnetospheric regions than was initially anticipated. So far, the separation of the Cluster spacecraft has been changed more than 30 times and has ranged from a few kilometres up to 36 000 km. These orbital changes have enabled the science team to address a wide variety of scientific objectives in key regions of Earth's geospace environment: the solar wind and bow shock, the magnetopause, polar cusps, magnetotail, plasmasphere and the auroral acceleration region. Recent results have shed new light on solar wind turbulence. They showed that the magnetosheath can be asymmetric under low Mach number and that it can contain density enhancement that may affect the magnetosphere. The magnetopause was found to be thinner and to have a higher current density on the duskside than on the dawnside. New methods have been used to obtain characteristic of the magnetotail current sheet and high-temporal-resolution measurements of electron pitch angle within flux transfer events (FTEs). Plasmaspheric wind has been discovered, and the refilling of the plasmasphere was observed for the first time over a very wide range of L shells. New models of global electric and magnetic fields of the magnetosphere have been obtained where Cluster, due to its polar orbit, has been essential. Finally, magnetic reconnection was viewed for the first time with high-resolution wave and electron measurements and acceleration of plasma was observed during times of varying rate of magnetic reconnection. The analysis of Cluster data was facilitated by the creation of the Cluster Science Data System (CSDS) and the Cluster Science Archive (CSA). Those systems were implemented to provide, for the first time for a plasma physics mission, a long-term public archive of all calibrated high-resolution data from all instruments.

scholarly journals A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty

2017 ◽  
Vol 21 (3) ◽  
pp. 1769-1790 ◽  
Author(s):  
Matthias Zink ◽  
Rohini Kumar ◽  
Matthias Cuntz ◽  
Luis Samaniego
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Recharge ◽  
Land Surface ◽  
Large Scale ◽  
Parametric Uncertainty ◽  
Model Parameters ◽  
High Resolution Data ◽  
Daily Streamflow ◽  
Resolution Dataset

Abstract. Long-term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model (mHM) is employed to derive an ensemble (100 members) of evapotranspiration, groundwater recharge, soil moisture, and runoff generated at high spatial and temporal resolutions (4 km and daily, respectively) for the period 1951–2010. The model is cross-evaluated against the observed daily streamflow in 222 basins, which are not used for model calibration. The mean (standard deviation) of the ensemble median Nash–Sutcliffe efficiency estimated for these basins is 0.68 (0.09) for daily streamflow simulations. The modeled evapotranspiration and soil moisture reasonably represent the observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in model-derived hydrological datasets.

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