Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations

The Sentinel satellite missions are designed to provide remote-sensing observational capability to many diverse operational applications, including in the field of agriculture and food security. They do this by acquiring frequent observations from a combination of optical, thermal and microwave sensors at various spatial resolutions. However, one currently missing capability, that would enable monitoring of evapotranspiration, crop water stress and water use at field scale, is the lack of high-resolution (tens of meters) thermal sensor. In this study we evaluate a methodology for bridging this data gap by employing a machine learning algorithm to sharpen low-resolution thermal observations from the Sentinel-3 satellites using images acquired by high-resolution optical sensors on the Sentinel-2 satellites. The resulting dataset is then used as input to land-surface energy balance model to estimate evapotranspiration. The methodology is tested using Terra and Landsat satellite observations, due to lack of sufficiently long time-series of Sentinel observations, and benchmarked against fluxes derived with high-resolution thermal observations acquired by the Landsat satellites. We then apply the methodology to Sentinel-2 and Sentinel-3 images to confirm its applicability to this type of data. The results show that the fluxes derived with sharpened thermal data are of acceptable accuracy (relative error lower than 20%) and provide more information at flux-tower footprint scale than the corresponding low-resolution fluxes. They also replicate the spatial and temporal patterns of fluxes derived with high-resolution thermal observations. However, the increase in error of the modelled fluxes compared to using high-resolution thermal observations and the inherent limitations of the sharpening approach point to the need to add high-resolution thermal mission to the Sentinels' constellation.info:eu-repo/semantics/publishedVersio

Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day-night MODIS observations

The Dual Temperature Difference (DTD) model, introduced by Norman et al. (2000), uses a two source energy balance modelling scheme driven by remotely sensed observations of diurnal changes in land surface temperature (LST) to estimate surface energy fluxes. By using a time-differential temperature measurement as input, the approach reduces model sensitivity to errors in absolute temperature retrieval. The original formulation of the DTD required an early morning LST observation (approximately 1 h after sunrise) when surface fluxes are minimal, limiting application to data provided by geostationary satellites at sub-hourly temporal resolution. The DTD model has been applied primarily during the active growth phase of agricultural crops and rangeland vegetation grasses, and has not been rigorously evaluated during senescence or in forested ecosystems. In this paper we present modifications to the DTD model that enable applications using thermal observations from polar orbiting satellites, such as Terra and Aqua, with day and night overpass times over the area of interest. This allows the application of the DTD model in high latitude regions where large viewing angles preclude the use of geostationary satellites, and also exploits the higher spatial resolution provided by polar orbiting satellites. A method for estimating nocturnal surface fluxes and a scheme for estimating the fraction of green vegetation are developed and evaluated. Modification for green vegetation fraction leads to significantly improved estimation of the heat fluxes from the vegetation canopy during senescence and in forests. When the modified DTD model is run with LST measurements acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites, generally satisfactory agreement with field measurements is obtained for a number of ecosystems in Denmark and the United States. Finally, regional maps of energy fluxes are produced for the Danish Hydrological ObsErvatory (HOBE) in western Denmark, indicating realistic patterns based on land use

Estimating evaporation with thermal UAV data and two-source energy balance models

Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperature-difference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST

Remote sensing of water use and water stress in the African savanna ecosystem at local scale – Development and validation of a monitoring tool

Savannas are among the most productive biomes of Africa, where they comprise half of its surface. They support wildlife, livestock, rangelands, crops, and livelihoods, playing an important socioeconomic role in rural areas. These water-limited ecosystems with seasonal water availability are highly sensitive to changes in both climate conditions, and in land-use/management practices. Although monitoring programs for African savanna water use have been established in certain areas, most of them are largely restricted to point based measurements or coarse scales, and are not fully capable to provide distributed timely information for planning purposes. In this study we develop a mechanism for monitoring the water used by African savanna from fine scale (meters) to watershed scale, integrating the effects of the water stress. Our hypothesis is that the Ecosystem Stress Index (ESI) is a valuable tool to downscale estimates of actual evapotranspiration at coarse scale, to high resolutions. To monitor savanna water fluxes in a semi-continuous way this study integrates two different ET-estimation approaches: KC-FAO56 model, integrating reflectance-based “crop” coefficients (SPOT 4 & 5 satellites), is used to derive unstressed savanna evapotranspiration (with high spatial resolution), and the two-source surface energy balance model -TSEB, integrating radiometric surface temperature (AATSR satellites) allows the determination of water stress across savannas (ESI, with low spatial resolution). The difference between estimated and observed surface fluxes derived from TSEB (RMSDLE = 53 Wm-2, RMSDH = 50 Wm-2, RMSDRn = 60 Wm-2, RMSDG = 21 Wm-2) were of the same magnitude as the uncertainties derived from the flux measurement system, being sufficiently accurate to be employed in a distributed way and on a more regular basis. The approach of ESI to downscale ET proved to be useful, and errors between estimated and observed daily ET (RMSD 0.6 mmday−1) were consistent with the results of other studies in savanna ecosystems. The modelling framework proposed provided an accurate representation of the natural landscape heterogeneity and local conditions, with the potential of providing information suitable from local to broader scales.info:eu-repo/semantics/publishedVersio

An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration

We present a network of sites across Fennoscandia for optical sampling of vegetation properties relevant for phenology monitoring and satellite data calibration. The network currently consists of five sites, distributed along an N-S gradient through Sweden and Finland. Two sites are located in coniferous forests, one in a deciduous forest, and two on peatland. The instrumentation consists of dual-beam sensors measuring incoming and reflected red, green, NIR, and PAR fluxes at 10-min intervals, year-round. The sensors are mounted on separate masts or in flux towers in order to capture radiation reflected from within the flux footprint of current eddy covariance measurements. Our computations and model simulations demonstrate the validity of using off-nadir sampling, and we show the results from the first year of measurement. NDVI is computed and compared to that of the MODIS instrument on-board Aqua and Terra satellite platforms. PAR fluxes are partitioned into reflected and absorbed components for the ground and canopy. The measurements demonstrate that the instrumentation provides detailed information about the vegetation phenology and variations in reflectance due to snow cover variations and vegetation development. Valuable information about PAR absorption of ground and canopy is obtained that may be linked to vegetation productivity

Enabling the Use of Earth Observation Data for Integrated Water Resource Management in Africa with the Water Observation and Information System

The Water Observation and Information System (WOIS) is an open source software tool for monitoring, assessing and inventorying water resources in a cost-effective manner using Earth Observation (EO) data. The WOIS has been developed by, among others, the authors of this paper under the TIGER-NET project, which is a major component of the TIGER initiative of the European Space Agency (ESA) and whose main goal is to support the African Earth Observation Capacity for Water Resource Monitoring. TIGER-NET aims to support the satellite-based assessment and monitoring of water resources from watershed to cross-border basin levels through the provision of a free and powerful software package, with associated capacity building, to African authorities. More than 28 EO data processing solutions for water resource management tasks have been developed, in correspondence with the requirements of the participating key African water authorities, and demonstrated with dedicated case studies utilizing the software in operational scenarios. They cover a wide range of themes and information products, including basin-wide characterization of land and water resources, lake water quality monitoring, hydrological modeling and flood forecasting and mapping. For each monitoring task, step-by-step workflows were developed, which can either be adjusted by the user or largely automatized to feed into existing data streams and reporting schemes. The WOIS enables African water authorities to fully exploit the increasing EO capacity offered by current and upcoming generations of satellites, including the Sentinel missions

Comparison of vegetation indices to determine their accuracy in predicting spring phenology of Swedish ecosystems

Phenological observations of terrestrial ecosystems are useful in monitoring the changes in the local climate due to their relatively long time series and high temporal resolution. However performing field based phenological observations can be a labour intensive and time consuming process. Using satellite-based remotely sensed data can make the process much more efficient. Although the satellites do not measure the plant phenology directly they can be used to observe seasonal changes on a landscape scale and to estimate the dates of a number of phenological events, such as the onset of greenness or the beginning of the leaf senescence. The launch of new Earth observation satellites over the last decade, with improved spatial, temporal and spectral resolutions, presented an opportunity to develop new vegetation indices (VI) which could potentially be suited to the observation of phenological changes. The present study compares four VIs on how accurately they can be used to estimate the timing of spring phenological events in ecosystems in the north, centre and south of Sweden. The indices under study are the NDVI, WDRVI, EVI2 and NDWI. The reference data comes from tower mounted or hand held instruments which measure the photosynthetically active radiation (PAR). The phenological events being looked at are the onset of the green season (when green vegetation appears in spring either through being exposed from underneath the melting snow or through fresh growth) and the onset of the growing season (when the new vegetation, especially tree leaves, begin to grow). The results of the study indicate that NDWI is the only index that can estimate the onset of the leaf growing season in deciduous forests both in the north and south of Sweden. The other indices are only able to predict the start of the green season in this type of ecosystem. In coniferous forests EVI2 seems to be the most appropriate index to use to estimate the start of the growing season. In low vegetation ecosystems the findings are more inconclusive but it appears that EVI2 also performs the best in estimating the start of the green season. The study also found that it is necessary to use under-the-canopy upward pointing PAR sensors to observe the start of the leaf growing season in deciduous forests and over-the-canopy downward pointing PAR sensors to observe the start of the growing season in coniferous forests

Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment

Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distributed hydrological model, while the energy-balance approach is often used with remotely sensed observations of, for example, the land surface temperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case of the MIKE SHE ET module). However, all three of them use the same ancillary data (meteorological measurements, land cover type and leaf area index, etc.) and produce output at similar spatial resolution (1 km for the TSEB models, 500 m for MIKE SHE). The comparison is performed on the spatial patterns of the fluxes present within the catchment area as well as on temporal patterns on the whole catchment scale in 8-year long time series. The results show that the spatial patterns of latent heat flux produced by the remote sensing models are more similar to each other than to the fluxes produced by MIKE SHE. The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r ≈ 0.8). This indicates potential benefits to the hydrological modelling community of integrating spatial information derived through remote sensing methodology (contained in the ET maps derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.The work has been carried out under the HOBE project funded by the VILLUM FOUNDATIONPeer reviewe

pyTSEB 1.1. A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data

Changes from version 1.0 Improved iterative procedure for reducing the Priestley-Taylor coefficient in TSEB-PT and DTD Use consistent temperature units (Kelvin) in both input and output Option to use differential temperatures in the One Source Energy Balance model, similarly as in DTD Improved code readabilit