Precision irrigation is a promising method to mitigate the impacts of drought stress on crop production with the optimal use of water resources. However, the reliable assessment of plant water status has not been adequately demonstrated, and unmanned aerial systems (UAS) offer great potential for spatiotemporal improvements. This study utilized UAS equipped with multispectral and thermal sensors to detect and quantify drought stress in winter wheat (Triticum aestivum L.) using the Water Deficit Index (WDI). Biennial field experiments were conducted on coarse sand soil in Denmark and analyses were performed at both diurnal and seasonal timescales. The WDI was significantly correlated with leaf stomatal conductance (R2 = 0.61–0.73), and the correlation was weaker with leaf water potential (R2 = 0.39–0.56) and topsoil water status (the highest R2 of 0.68). A semi-physical model depicting the relationship between WDI and fraction of transpirable soil water (FTSW) in the root zone was derived with R2 = 0.74. Moreover, WDI estimates were improved using an energy balance model with an iterative scheme to estimate the net radiation and land surface temperature, as well as the dual crop coefficient. The diurnal variation in WDI revealed a pattern of the ratio of actual to potential evapotranspiration, being higher in the morning, decreasing at noon hours and ‘recovering’ in the afternoon. Future work should investigate the temporal upscaling of evapotranspiration, which may be used to develop methods for site-specific irrigation recommendations.

The present project presents the technical implementation, testing and validation of a soil moisture retrieval algorithm in Python using C-band Sentinel-1 data at high incidence angle (∼42°). The retrieval algorithm is based on the alpha approximation, first developed by [Balenzano et al. 2011]. The alpha approximation utilizes the dense temporal coverage of the Sentinel-1 mission, assuming that changes in backscatter between subsequent acquisitions are only due to variations in soil moisture, such that vegetation and roughness can be neglected. The area used for testing the algorithm was chosen to be the region surrounding the Foulum test center for agricultural studies in Denmark, due to the availability of time series from 2018 of in situ soil moisture measurements to be used for validation. Masking of too densely vegetated areas have been performed using the cross-polarized component of the SAR backscatter, which have been validated using NDVI maps. Auxiliary data, including land cover maps and parcel borders enable the computation of backscatter field means, significantly reducing the impact of speckle noise and thus decreasing uncertainty of the estimated soil moisture. Consequently, the results have field scale resolution (i.e. ∼0.1 km). The permittivity to soil moisture inversion is performed using a polynomial model by [Hallikainen et al. 1985], where a soil texture map provide the information necessary to obtain precise results. Further work will aim toward applying a change detection algorithm in order to detect sudden temporal changes in vegetation and surface roughness, as the alpha approximation is inherently sensitive to such sudden changes. The study has received partial funding from Innovation Fund Denmark, contract number: 7049-00004B (MOIST).

The Sentinel-2 and Sentinel-3 satellite constellation contains most of the spatial, temporal and spectral characteristics required for accurate, field-scale actual evapotranspiration (ET) estimation. The one remaining major challenge is the spatial scale mismatch between the thermal-infrared observations acquired by the Sentinel-3 satellites at around 1 km resolution and the multispectral shortwave observations acquired by the Sentinel-2 satellite at around 20 m resolution. In this study we evaluate a number of approaches for bridging this gap by improving the spatial resolution of the thermal images. The resulting data is then used as input into three ET models, working under different assumptions: TSEB, METRIC and ESVEP. Latent, sensible and ground heat fluxes as well as net radiation produced by the models at 20 m resolution are validated against observations coming from 11 flux towers located in various land covers and climatological conditions. The results show that using the sharpened high-resolution thermal data as input for the TSEB model is a sound approach with relative root mean square error of instantaneous latent heat flux of around 30% in agricultural areas. The proposed methodology is a promising solution to the lack of thermal data with high spatio-temporal resolution required for field-scale ET modelling and can fill this data gap until next generation of thermal satellites are launched.

Data assimilation optimally merges model forecasts with observations taking into account both model and observational uncertainty. This paper presents a new data assimilation framework that enables the many Open Model Interface (OpenMI) 2.0 .NET compliant hydrological models already available, access to a robust data assimilation library. OpenMI is an open standard that allows models to exchange data during runtime, thus transforming a complex numerical model to a ‘plug and play’ like component. OpenDA is an open interface standard for a set of tools, filters, and numerical techniques to quickly implement data assimilation. The OpenDA–OpenMI framework is presented and tested on a synthetic case that highlights the potential of this new framework. MIKE SHE, a distributed and integrated hydrological model is used to assimilate hydraulic head in a catchment in Denmark. The simulated head over the entire domain were significantly improved by using an ensemble based Kalman filter.

Subsurface soil temperature is a key variable of land surface processes and not only responds to but also modulates the interactions of energy fluxes at the Earth's surface. Thermal remote sensing has traditionally been regarded as incapable of detecting the soil temperature beneath the skin-surface. This study shows that thermal remote sensing can be used to estimate soil temperatures. Our results provide insights into thermal observations collected with tandem polar-orbiting satellites when used toward obtaining soil temperatures under clear-sky conditions without the use of any ground-based information or field-measured soil properties.We designed an analytical three-time-scale (3-scale, for short) model, dividing the annual cycle of soil temperatures into three subcycles: the annual temperature cycle (ATC), which represents the daily-averaged temperature; the diurnal temperature cycle (DTC), which represents the instantaneous temperature; and the weather-change temperature cycle (WTC), which is divided into two parts to represent both the daily-averaged (WTCavg) and the instantaneous temperature (WTCinst). The DTC and WTCinst were further parameterized into four undetermined variables, including the daily-averaged temperature, thermal inertia, upward surface flux factor, and day-to-day change rate. Thus, under clear-sky conditions, the four thermal measurements in a diurnal cycle recorded with tandem polar-orbiting satellites are sufficient for reconstructing the DTC of both land surface and soil temperatures. Polar-orbiting satellite data from MODIS are used to show the model's capability. The results demonstrate that soil temperatures with a spatial resolution of 1km under snow-free conditions can be generated at any time of a clear-sky day. Validation is performed by using a comparison between the MODIS-inverted and ground-based soil temperatures. The comparison shows that the accuracy of inverted soil temperatures lies between 0.3 and 2.5K with an average of approximately 1.5K. These results open a new frontier in the application of thermal remote sensing wherein soil temperatures with high spatial and temporal resolutions can be remotely estimated.

Remote sensing has proved to be a consistent tool for monitoring water fluxes at regional scales. The triangle method, in particular, estimates the evaporative fraction (EF), defined as the ratio of latent heat flux (LE) to available energy, based on the relationship between satellite observations of land surface temperature and a vegetation index. Among other methodologies, this approach has been commonly used as an approximation to estimate LE, mainly over large semi-arid areas with uniform landscape features. In this study, an interpretation of the triangular space has been applied over a heterogeneous area in central Spain, using Landsat5-TM, Envisat-AATSR/MERIS and MSG-SEVIRI images. Some aspects affecting the model performance such as spatial resolution, terrain conditions, vegetation index applied and method for deriving the triangle edges have been assessed. The derived EF estimations have been validated against ground measurements obtained with scintillometer on a winter crop field during 2010–2011. When working with large spatial windows, removing areas with different topographic characteristics (altitude and slope) improved the performance of the methods. In addition, replacing the typically used NDVI with Leaf Area Index enhances the performance of the triangle method allowing a better characterization of the wet edge. Finally, results showed a relatively good performance for the EF estimates, with an RMSE of 0.11, 0.15 and 0.23 and R2 of 0.77, 0.41, and 0.24 for Landsat, Envisat and MSG satellites respectively, showing a scale dependency on the accuracy.

A method for the estimation of daily evapotranspiration is tested for the North China Plain. The method is designed to be simple to implement and with very limited requirements for ground data (air temperature and humidity). The method uses MODIS NDVI and Land Surface Temperature (LST) data to derive evaporative fraction, using an adaptation of the “triangle method”. The energy available for evapotranspiration is estimated using a combination of satellite data from MODIS and the (geostationary) Fengyun 2-series of sensors and station-based air temperature data. A gapfilling routine is applied to the time series of evaporative fraction to create complete daily maps for the region, allowing for use of the ET-estimates for applications requiring complete daily coverage (e.g. hydrological models). Results show that ET estimation on a daily scale is feasible with the proposed method, and that seasonal patterns are in accordance with other independent ET-estimates. There are some indications that our ET-estimates are somewhat overestimated when comparing to other RS-based methods and model simulations. It is demonstrated that the proposed method provides a relatively simple way of obtaining spatially distributed daily estimates of ET, making the method suitable for applications in studies where ground data availability is limited.

A modified version of the MODerate resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) algorithm is presented for use in the angular normalization of surface reflectance data gathered by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the geostationary Meteosat Second Generation (MSG) satellites. We present early and provisional daily nadir BRDF-adjusted reflectance (NBAR) data in the visible and near-infrared MSG channels. These utilize the high temporal resolution of MSG to produce BRDF retrievals with a greatly reduced acquisition period than the comparable MODIS products while, at the same time, removing many of the angular perturbations present within the original MSG data. The NBAR data are validated against reflectance data from the MODIS instrument and in situ data gathered at a field location in Africa throughout 2008. It is found that the MSG retrievals are stable and are of high-quality across much of the SEVIRI disk while maintaining a higher temporal resolution than the MODIS BRDF products. However, a number of circumstances are discovered whereby the BRDF model is unable to function correctly with the SEVIRI observations-primarily because of an insufficient spread of angular data due to the fixed sensor location or localized cloud contamination.

Water deficit indices based on the spatial relationship between surface temperature (Ts) and NDVI, known as triangle approaches, are widely used for drought monitoring. However, their application has been recently questioned when the main factor limiting evapotranspiration is energy. Even though water is the main control in dryland ecosystems, these can also undergo periods of energy and temperature limitation. In this paper we aimed to: (i) evaluate the TVDI (Temperature–Vegetation Dryness Index) to estimate water deficits (e.g. ratio between actual and potential evapotranspiration), and heat surface fluxes using MODIS data; and (ii) provide insights about the factors most affecting the accuracy of results. Factors considered included the type of climatic control on evapotranspiration, λE, (i.e. water-limited vs. energy-limited), the quality of Tair estimates, the heterogeneity of land cover types and climatic variables in the region, or the algorithm to extract hydrological boundaries from the images.The TVDI was compared with eddy covariance (EC) data from two shrublands with different climatic controls for λE in South Spain. Evaluations showed that it could be used to estimate the water deficit when water was the main limiting factor (R=0.81–0.88; Mean Average Error, MAE=0.16–0.17) but not in energy-limited situations (R<0.2; MAE=0.10–0.2). Spatial heterogeneity in climatic variables also had a different impact on accuracy depending on limiting factors. Relative humidity was significant at the water-limited site while solar irradiance and air temperature were more important at the energy-limited site. The skill of the TVDI to estimate surface fluxes at the water-limited site was confirmed for the dominant sensible heat flux, H (R2=0.93; Mean Absolute Percentage Error, MAPE=12.85%) but not for λE (R2=0.01, MAPE=115.22%) as λE fluxes at this site are just slightly above the error of the eddy covariance system. At the energy-limited site, λE (R2=0.74; MAPE=31.83%) and H estimates (R2=0.80; MAPE=26.85%) were better than those from the SEBAL (Surface Energy Balance Algorithm for Land) or the PML (Penman–Monteith–Leuning) models. However, the skill to predict surface fluxes in this case was due to the net radiation inputs and not by the TVDI input.Our analyses also suggest: (1) to apply the TVDI excluding energy-limited sites/periods based on climatic knowledge of limiting factors on λE; (2) the best conditions for TVDI performance correspond to the situation when the controlling factors are less limiting, e.g. during the growing season (higher SWC and lower VPD); and (3) to account better for the role of vegetation controls on transpiration.

The Dahra field site in Senegal, West Africa, was established in 2002 to monitor ecosystem properties of semiarid savanna grassland and their responses to climatic and environmental change. This article describes the environment and the ecosystem properties of the site using a unique set of in situ data. The studied variables include hydroclimatic variables, species composition, albedo, normalized difference vegetation index ( NDVI ), hyperspectral characteristics (350–1800 nm), surface reflectance anisotropy, brightness temperature, fraction of absorbed photosynthetic active radiation ( FAPAR ), biomass, vegetation water content, and land‐atmosphere exchanges of carbon ( NEE ) and energy. The Dahra field site experiences a typical Sahelian climate and is covered by coexisting trees (~3% canopy cover) and grass species, characterizing large parts of the Sahel. This makes the site suitable for investigating relationships between ecosystem properties and hydroclimatic variables for semiarid savanna ecosystems of the region. There were strong interannual, seasonal and diurnal dynamics in NEE , with high values of ~−7.5 g C m −2 day −1 during the peak of the growing season. We found neither browning nor greening NDVI trends from 2002 to 2012. Interannual variation in species composition was strongly related to rainfall distribution. NDVI and FAPAR were strongly related to species composition, especially for years dominated by the species Zornia glochidiata . This influence was not observed in interannual variation in biomass and vegetation productivity, thus challenging dryland productivity models based on remote sensing. Surface reflectance anisotropy (350–1800 nm) at the peak of the growing season varied strongly depending on wavelength and viewing angle thereby having implications for the design of remotely sensed spectral vegetation indices covering different wavelength regions. The presented time series of in situ data have great potential for dryland dynamics studies, global climate change related research and evaluation and parameterization of remote sensing products and dynamic vegetation models.

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.

In the Sudano-Sahelian areas of Africa droughts can have serious impacts on natural resources, and therefore land surface moisture is an important factor. Insufficient conventional sites for monitoring land surface moisture make the use of Earth Observation data for this purpose a key issue. In this study we explored the potential of using reflectance data in the Red, Near Infrared (NIR), and Shortwave Infrared (SWIR) spectral regions for detecting short term variations in land surface moisture in the Sahel, by analyzing data from three test sites and observations from the geostationary Meteosat Second Generation (MSG) satellite. We focused on responses in surface reflectance to soil- and surface moisture for bare soil and early to mid- growing season. A method for implementing detrended time series of the Shortwave Infrared Water Stress Index (SIWSI) is examined for detecting variations in vegetation moisture status, and is compared to detrended time series of the Normalized Difference Vegetation Index (NDVI). It was found that when plant available water is low, the SIWSI anomalies increase over time, while the NDVI anomalies decrease over time, but less systematically. Therefore SIWSI may carry important complementary information to NDVI in terms of vegetation water status, and can provide this information with the unique combination of temporal and spatial resolution from optical geostationary observations over Sahel. However, the relation between SIWSI anomalies and periods of water stress were not found to be sufficiently robust to be used for water stress detection.

Improving regional estimates of actual evapotranspiration (λΕ) in water-limited regions located at climatic transition zones is critical. This study assesses an λΕ model (PT-JPL model) based on downscaling potential evapotranspiration according to multiple stresses at daily time-scale in two of these regions using MSG–SEVIRI (surface temperature and albedo) and MODIS products (NDVI, LAI and fPAR). An open woody savanna in the Sahel (Mali) and a Mediterranean grassland (Spain) were selected as test sites with Eddy Covariance data used for evaluation. The PT-JPL model was modified to run at a daily time step and the outputs from eight algorithms differing in the input variables and also in the formulation of the biophysical constraints (stresses) were compared with the λΕ from the Eddy Covariance. Model outputs were also compared with other modeling studies at similar global dryland ecosystems. The novelty of this paper is the computation of a key model parameter, the soil moisture constraint, relying on the concept of apparent thermal inertia (fSM-ATI) computed with surface temperature and albedo observations. Our results showed that fSM-ATI from both in-situ and satellite data produced satisfactory results for λΕ at the Sahelian savanna, comparable to parameterizations using field-measured Soil Water Content (SWC) with r2 greater than 0.80. In the Mediterranean grasslands however, with much lower daily λE values, model results were not as good as in the Sahel (r2 = 0.57–0.31) but still better than reported values from more complex models applied at the site such as the Two Source Model (TSM) or the Penman–Monteith Leuning model (PML). PT-JPL-daily model with a soil moisture constraint based on apparent thermal inertia, fSM-ATI offers great potential for regionalization as no field-calibrations are required and water vapor deficit estimates, required in the original version, are not necessary, being air temperature and the available energy (Rn-G) the only input variables required, apart from routinely available satellite products.