Evaluation of a simple approach for crop evapotranspiration partitioning and analysis of the water budget distribution for several crop species

International audienceClimate variability and climate change induce important intra- and inter-annual variability of precipitation that significantly alters the hydrologic cycle. The surface water budgets and the plant or ecosystem water use efficiency (WUE) are in turn modified. Obtaining greater insight into how climatic variability and agricultural practices affect water budgets and regarding their components in croplands is, thus, important for adapting crop management and limiting water losses. Therefore, the principal objectives of this study are: (1) to assess the contribution of different components to the agro-ecosystem water budget and (2) to evaluate how agricultural practices and climate modify the components of the surface water budget. To achieve these goals, we tested a new method for partitioning evapotranspiration (ETR), measured by means of an eddy-covariance method, into soil evaporation (E) and plant transpiration (TR) based on marginal distribution sampling (MDS). The partitioning method proposed requires continuous flux recording and measurements of soil temperature and humidity close to the surface, global radiation above the canopy and assessment of leaf area index dynamics. This method is well suited for crops because it requires a dataset including long bare-soil periods alternating with vegetated periods for accurate partitioning estimation. We compared these estimations with calibrated simulations of the ICARE-SVAT double source mechanistic model. The results showed good agreement between the two partitioning methods, demonstrating that MDS is a convenient, simple and robust tool for estimating E with reasonable associated uncertainties. During the growing season, the proportion of E in ETR was approximately one-third and varied mainly with crop leaf area. When calculated on an annual time scale, the proportion of E in ETR reached more than 50%, depending on the crop leaf area and on the duration and distribution of bare soil within the year

Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model

International audienceIn semi-arid areas, a strongly variable climate represents a major risk for food safety. An operational grain yield forecasting system, which could help decision-makers to make early assessments and plan annual imports, is thus needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyse the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. First, statistical analysis is used to characterize the vegetation's dynamics and grain yield, based on remotely sensed (satellite) normalized difference vegetation index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced for the studied site. The second approach is based on the application of a Simple Algorithm for Yield Estimation (SAFY) growth model, developed to simulate the dynamics of the LAI and the grain yield. An inter-comparison between ground yield measurements and SAFY model simulations reveals that yields are underestimated by this model. Finally, the combination of multi-temporal satellite measurements with the SAFY model estimations is also proposed for the purposes of yield mapping. Although the results produced by the SAFY model are found to be reasonably well correlated with those determined by satellite measurements (NDVI), the grain yields are nevertheless underestimated

Evapotranspiration partition using the multiple energy balance version of the ISBA-A-gs land surface model over two irrigated crops in a semi-arid Mediterranean region (Marrakech, Morocco)

The main objective of this work is to question the representation of the energy budget in soil–vegetation–atmosphere transfer (SVAT) models for the prediction of the turbulent fluxes in the case of irrigated crops with a complex structure (row) and under strong transient hydric regimes due to irrigation. To this end, the Interaction between Soil, Biosphere, and Atmosphere (ISBA-A-gs) is evaluated at a complex open olive orchard and, for the purposes of comparison, on a winter wheat field taken as an example of a homogeneous canopy. The initial version of ISBA-A-gs, based on a composite energy budget (hereafter ISBA-1P for one patch), is compared to the new multiple energy balance (MEB) version of ISBA that represents a double source arising from the vegetation located above the soil layer. In addition, a patch representation corresponding to two adjacent, uncoupled source schemes (hereafter ISBA-2P for two patches) is also considered for the olive orchard. Continuous observations of evapotranspiration (ET), with an eddy covariance system and plant transpiration (Tr) with sap flow and isotopic methods were used to evaluate the three representations. A preliminary sensitivity analyses showed a strong sensitivity to the parameters related to turbulence in the canopy introduced in the new ISBA–MEB version. For wheat, the ability of the single- and dual-source configuration to reproduce the composite soil–vegetation heat fluxes was very similar; the root mean square error (RMSE) differences between ISBA-1P, ISBA-2P and ISBA–MEB did not exceed 10 W m−2 for the latent heat flux. These results showed that a composite energy balance in homogeneous covers is sufficient to reproduce the total convective fluxes. The two configurations are also fairly close to the isotopic observations of transpiration in spite of a light underestimation (overestimation) of ISBA-1P (ISBA–MEB). At the olive orchard, contrasting results are obtained. The dual-source configurations, including both the uncoupled (ISBA-2P) and the coupled (ISBA–MEB) representations, outperformed the single-source version (ISBA-1P), with slightly better results for ISBA–MEB in predicting both total heat fluxes and evapotranspiration partition. Concerning plant transpiration in particular, the coupled approach ISBA–MEB provides better results than ISBA-1P and, to a lesser extent, ISBA-2P with RMSEs of 1.60, 0.90, and 0.70 mm d−1 and R2 of 0.43, 0.69, and 0.70 for ISBA-1P, ISBA-2P and ISBA–MEB, respectively. In addition, it is shown that the acceptable predictions of composite convective fluxes by ISBA-2P for the olive orchard are obtained for the wrong reasons as neither of the two patches is in agreement with the observations because of a bad spatial distribution of the roots and a lack of incoming radiation screening for the bare soil patch. This work shows that composite convection fluxes predicted by the SURFace EXternalisée (SURFEX) platform and the partition of evapotranspiration in a highly transient regime due to irrigation is improved for moderately open tree canopies by the new coupled dual-source ISBA–MEB model. It also points out the need for further local-scale evaluations on different crops of various geometry (more open rainfed agriculture or a denser, intensive olive orchard) to provide adequate parameterisation to global database, such as ECOCLIMAP-II, in the view of a global application of the ISBA–MEB model

EVASPA (EVapotranspiration Assessment from SPAce) Tool: An overview

AbstractEvapotranspiration (ET) is a fundamental variable of the hydrological cycle and its estimation is required for irrigation management, water resources planning and environmental studies. Remote sensing provides spatially distributed cost-effective information for ET maps production at regional scale. We have developed EVASPA too for mapping ET from remote sensing data at spatial and temporal scales relevant to hydrological or agronomica studies.EVASPA includes several algorithms for estimating evapotranspiration and various equations for estimating the required input information (net radiation, ground heat flux, evaporative fraction…), which provides a way to assess uncertainties in the derivation of ET. The tool integrates data from various remote sensing sensors and it can be easily adapted to new sensors. To test the tool, evapotranspiration maps have been produced for the Crau-Camargue pilot site (south-eastern France), where several energy balance stations deployed in contrasted areas provide ground measurements. An overall description of the tool and first results of performance asse sment (comparison to ground data) are presented here

The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models

Aerodynamic resistance (hereafter ra) is a preeminent variable in evapotranspiration (ET) modelling. The accurate quantification of ra plays a pivotal role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links ra with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates. The present study investigates the influence of ra and its relation to LST uncertainties on the performance of three structurally different SEB models. It used data from nine Australian OzFlux eddy covariance sites of contrasting aridity in conjunction with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the sensible heat flux (H) and the latent heat flux (LE, the energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated against flux observations in water-limited (arid and semi-arid) and energy-limited (mesic) ecosystems from 2011 to 2019. Our results revealed an overestimation tendency of instantaneous LE by all three models in the water-limited shrubland, woodland and grassland ecosystems by up to 50% on average, which was caused by an underestimation of H. Overestimation of LE was associated with discrepancies in ra retrievals under conditions of high atmospheric instability, during which uncertainties in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive difference in LST coincided with low ra (high wind speeds) and caused a slight underestimation of LE at the water-limited sites. The impact of ra on the LE residual error was found to be of the same magnitude as the influence of LST uncertainties in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for the mesic forest ecosystems indicated minor dependency on ra for modelling LE (VIP \u3c 0.4), which was due to a higher roughness length and lower LST resulting in the dominance of mechanically generated turbulence, thereby diminishing the importance of buoyancy production for the determination of ra

Modeling actual water use under different irrigation regimes at district scale: Application to the FAO-56 dual crop coefficient method

The modeling of irrigation in land surface models are generally based on two soil moisture parameters SMthreshold and SMtarget at which irrigation automatically starts and stops, respectively. Typically, both parameters are usually set to optimal values allowing to fill the soil water reservoir with just the estimated right amount and to avoid crop water excess at all times. The point is that agricultural practices greatly vary according to many factors (climatological, crop, soil, technical, human, etc.). To fill the gap, we propose a new calibration method of SMthreshold and SMtarget to represent the irrigation water use in any (optimal, deficit or even over) irrigation regime. The approach is tested using the dual-crop coefficient FAO-56 model implemented at the field scale over an 8100 ha irrigation district in northeastern Spain where the irrigation water use is precisely monitored at the district scale. Both irrigation parameters are first retrieved at monthly scale from the irrigation observations of year 2019. The irrigation simulated by the FAO-56 model is then evaluated against observations at district and weekly scale over 5 years (2017–2021) separately. The performance of the newly calibrated irrigation module is also assessed by comparing it against three other modules with varying configurations including default estimates for SMthreshold and SMtarget. The proposed irrigation module obtains systematically the best performance for each of the 5 years with an overall correlation coefficient of 0.95 ± 0.02 and root-mean square error of 0.27 ± 0.07 hm3/week (0.64 ± 0.17 mm/day). Unlike the three irrigation modules used as benchmark, the new irrigation module is able to reproduce the farmers’ practices throughout the year, and especially, to simulate the actual water use in the deficit and excess irrigation regimes occurring in the study area in spring and summer, respectively.This study was supported by the IDEWA project ( ANR-19-P026-003 ) of the Partnership for research and innovation in the Mediterranean area ( PRIMA ) program and by the Horizon 2020 ACCWA project (grant agreement # 823965 ) in the context of Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) program. The authors wish to acknowledge the "Comunitat de Regants Canal Algerri Balaguer" and the Ebro Hydrographic Confederation (SAIH Ebro) for providing the observation irrigation data used in this study

The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat

Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil–vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water stress and moisture-limited soil evaporation) against in situ data over contrasted test sites (irrigated and rainfed wheat). We demonstrated with those two data sets that the SPARSE series model is more robust to component stress retrieval for this cover type, that its performance increases by using bounding relationships based on potential conditions (root mean square error lowered by up to 11 W m−2 from values of the order of 50–80 W m−2), and that soil evaporation retrieval is generally consistent with an independent estimate from observed soil moisture evolution

Agrometerological study of semi-arid areas : an experiment for analysing the potential of time series of FORMOSAT-2 images (Tensift-Marrakech plain)

Earth Observing Systems designed to provide both high spatial resolution (10m) and high capacity of time revisit (a few days) offer strong opportunities for the management of agricultural water resources. The FORMOSAT-2 satellite is the first and only satellite with the ability to provide daily high-resolution images over a particular area with constant viewing angles. As part of the SudMed project, one of the first time series of FORMOSAT-2 images has been acquired over the semi-arid Tensift-Marrakech plain. Along with these acquisitions, an experimental data set has been collected to monitor land-cover/land-use, soil characteristics, vegetation dynamics and surface fluxes. This paper presents a first analysis of the potential of these data for agrometerological study of semi-arid areas

Uncertainty assessment of surface net radiation derived from Landsat images

The net radiation flux available at the Earth's surface drives evapotranspiration, photosynthesis and other physical and biological processes. The only cost-effective way to capture its spatial and temporal variability at regional and global scales is remote sensing. However, the accuracy of net radiation derived from remote sensing data has been evaluated up to now over a limited number of in situ measurements and ecosystems. This study aims at evaluating estimates and uncertainties on net radiation derived from Landsat-7 images depending on reliability of the input surface variables albedo, emissivity and surface temperature. The later includes the reliability of remote sensing information (spectral reflectances and top of canopy brightness temperature) and shortwave and longwave incoming radiations. Primary information describing the surface is derived from remote sensing observations. Surface albedo is estimated from spectral reflectances using a narrow-to-broadband conversion method. Land surface temperature is retrieved from top of canopy brightness temperature by accounting for land surface emissivity and reflection of atmospheric radiation; and emissivity is estimated using a relationship with a vegetation index and a spectral database of soil and plant canopy properties in the study area. The net radiation uncertainty is assessed using comparison with ground measurements over the Crau–Camargue and lower Rhone valley regions in France. We found Root Mean Square Errors between retrievals and field measurements of 0.25–0.33 (14–19%) for albedo, ~ 1.7 K for surface temperature and ~ 20 W·m− 2 (5%) for net radiation. Results show a substantial underestimation of Landsat-7 albedo (up to 0.024), particularly for estimates retrieved using the middle infrared, which could be due to different sources: the calibration of field sensors, the correction of radiometric signals from Landsat-7 or the differences in spectral bands with the sensors for which the models where originally derived, or the atmospheric corrections. We report a global uncertainty in net radiation of 40–100 W·m− 2 equally distributed over the shortwave and longwave radiation, which varies spatially and temporally depending on the land use and the time of year. In situ measurements of incoming shortwave and longwave radiation contribute the most to uncertainty in net radiation (10–40 W·m− 2 and 20–30 W·m− 2, respectively), followed by uncertainties in albedo (< 25 W·m− 2) and surface temperature (~ 8 W·m− 2). For the latter, the main factors were the uncertainties in top of canopy reflectances (< 10 W·m− 2) and brightness temperature (5–7 W·m− 2). The generalization of these results to other sensors and study regions could be considered, except for the emissivity if prior knowledge on its characterization is not available

Insights Into the Aerodynamic Versus Radiometric Surface Temperature Debate in Thermal-Based Evaporation Modeling

Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research