The current intensive use of agricultural land is affecting the land quality
and contributes to climate change. Feeding the world's growing population
under changing climatic conditions demands a global transition to more
sustainable agricultural systems. This requires efficient models and data to
monitor land cultivation practices at the field to global scale.
This study outlines a spatially distributed version of the field-scale crop
model AquaCrop version 6.1 to simulate agricultural biomass production and
soil moisture variability over Europe at a relatively fine resolution of 30 arcsec (∼1km). A highly efficient parallel processing
system is implemented to run the model regionally with global meteorological
input data from the Modern-Era Retrospective analysis for Research and
Applications version 2 (MERRA-2), soil textural information from the
Harmonized World Soil Database version 1.2 (HWSDv1.2), and generic crop
information. The setup with a generic crop is chosen as a baseline for a
future satellite-based data assimilation system. The relative temporal
variability in daily crop biomass production is evaluated with the Copernicus
Global Land Service dry matter productivity (CGLS-DMP) data. Surface soil
moisture is compared against NASA Soil Moisture Active–Passive surface soil
moisture (SMAP-SSM) retrievals, the Copernicus Global Land Service surface
soil moisture (CGLS-SSM) product derived from Sentinel-1, and in situ data
from the International Soil Moisture Network (ISMN). Over central Europe, the
regional AquaCrop model is able to capture the temporal variability in both
biomass production and soil moisture, with a spatial mean temporal correlation
of 0.8 (CGLS-DMP), 0.74 (SMAP-SSM), and 0.52 (CGLS-SSM). The
higher performance when evaluating with SMAP-SSM compared to Sentinel-1
CGLS-SSM is largely due to the lower quality of CGLS-SSM satellite retrievals
under growing vegetation. The regional model further captures the short-term
and inter-annual variability, with a mean anomaly correlation of 0.46 for daily
biomass and mean anomaly correlations of 0.65 (SMAP-SSM) and 0.50 (CGLS-SSM)
for soil moisture. It is shown that soil textural characteristics and
irrigated areas influence the model performance. Overall, the regional
AquaCrop model adequately simulates crop production and soil moisture and
provides a suitable setup for subsequent satellite-based data assimilation.
Introduction
Over the past 60 years, global agricultural production has more than tripled
(FAO, 2017). This became possible through productivity-enhanced technologies,
industrialization, and expansion of agricultural land. However, the current
intensive use of cropland is resulting in reduced land quality and increased
greenhouse gas emissions, which in turn impact agricultural systems (Foley
et al., 2011; Kopittke et al., 2019). To meet the future crop demand of a
vastly growing population, while minimizing the ecological footprint and
increasing the crop resilience for changing climatic conditions, the need to
adapt to more effective and sustainable land cultivation practices is urgent
(Aznar-Sánchez et al., 2019; Pingali, 2012; Raes and Vanuytrecht, 2017).
To evaluate the effect of environmental conditions and different management
practices on crop production, there are a variety of models that simulate
the biophysiological growth of crops at the field scale. An overview of 70 of
such crop models is given by Di Paola et al. (2016). Some of these point-based
crop models have more recently been upscaled and assessed at a regional to
global level (Balkovic et al., 2013; Boogaard et al., 2013; Folberth et al.,
2019; Liu et al., 2007; Müller et al., 2017; Nichols et al., 2011; Resop
et al., 2012; Roerink et al., 2012; Stöckle et al., 2014). Large-scale
crop models are a valuable asset in providing information to policy makers and
for applications in climate scenario analyses (Asseng et al., 2013; Iizumi
et al., 2018). A downside of large-scale crop models, especially at a global
level, is that they often suffer from the generalization of input data and
loss of information that is typically available at smaller scales, resulting
in larger errors at the local scale. The AgMIP Global Gridded Model
Intercomparisons (GGCMI) is a framework initiated to overcome this issue. It
is built on a large group of crop modelling researchers that combine and
intercompare a set of upscaled point models or global gridded crop models to
assess and reduce the bias and uncertainties at a global level (Elliot et al.,
2015). Another possibility to improve the simulations at either the local or
larger scale is the updating of the model simulations with remote sensing
observations via data assimilation. There are several studies that have
already used data assimilation in regional crop modelling systems (De wit and
van Diepen, 2007; Mladenova et al., 2019; Zhuo et al., 2019) either for
parameter or state updating. Parameter updating or calibration allows for matching
the absolute values of the simulations with (most often historical)
observations. State updating allows for correction of the relative temporal evolution
and for obtaining better initial conditions for subsequent model predictions. To
get the most optimal results with data assimilation, it is important to start
with a reliable model that is able capture the seasonal and inter-annual
temporal variabilities.
This study presents a methodology to apply the original field-scale AquaCrop
model version 6.1 efficiently over a large region and for any spatial
resolution. The flexible model setup will allow for many different
applications, but in this study the focus is on the preparation of a
satellite-based data assimilation system. AquaCrop was developed by the FAO to
estimate responses of herbaceous crops to water (Raes et al., 2009; Steduto,
et al., 2009). It differs from most other crop models by its low requirement
of detailed input data, as it aims for a balance between simplicity, accuracy,
and robustness (Steduto, et al., 2009). The model has been applied in numerous
studies for various crop types and environmental conditions and shows good
results in simulating crop biomass and yield, especially when calibrated for
local field conditions (Abedinpour et al., 2012; Geerts et al., 2009; Hsiao
et al., 2009; Maniruzzaman et al., 2015; Razzaghi et al., 2017; Sandhu and
Irmak, 2019). Earlier spatially distributed versions of AquaCrop were
developed by e.g. Lorite et al. (2013), Sallah et al. (2019), and Huang
et al. (2019) using a Geographic Information System or batch processing with
remote sensing data input. Some challenges of existing distributed AquaCrop
systems are related to the limited scalability and high computational cost
when they are applied to any large domain at any resolution, the limitations
in the upscaling of crop parameters from the plant or field to the grid scale
(Han et al., 2020), and the availability of other suitable spatially
distributed parameters or input information. Applications of the AquaCrop
model at a continental scale exist but are very limited (Dale et al., 2017)
and so far are only used in combination with coarse spatial resolutions. To
our best knowledge, no study has yet reported on high-resolution and
large-scale (beyond country level) applications of AquaCrop.
The continental setup of our regional AquaCrop simulations uses spatially
distributed input data about soil texture and meteorology, while assuming a
homogenous generic crop. To evaluate, or later update, select variables within
such a regional modelling system, in situ data only provide sparse
information. However, a range of spatially distributed optical and
microwave-based satellite data are available at various temporal and spatial
resolutions. A confrontation between model simulations and satellite data to
evaluate or update the model simulations is not always trivial. Most
importantly, the magnitude of model simulations and satellite retrievals of
soil moisture or biomass are often not directly comparable. Biases between
models and observations are inevitable because they represent different
quantities (Koster et al., 2009; Reichle et al., 2004) or are simply based on
different assumed parameterizations. The assumption of a generic crop will, for
example, lead to inevitable biases. Via parameter estimation, soil and
vegetation parameters can be spatially tuned to reduce such biases, but this
is often not feasible for satellite retrievals or is difficult with more detailed
models at the regional to global scale. For this same reason, state-of-the-art
data assimilation systems for state updating are designed to correct for
random error and not for systematic bias. Therefore, satellite products of
relative soil water indices or anomaly total water storage are often
distributed (Wagner et al., 1999; Albergel et al., 2008; De Lannoy et al.,
2016; Li et al., 2019), and the performance of large-scale model simulations
is often evaluated using bias-free temporal skill metrics (De Lannoy et al.,
2015; Gruber et al., 2020).
The objective of this research is to assess whether a high-resolution regional
gridded AquaCrop model can capture the seasonal, inter-annual, and short-term
temporal variability, as well as the spatial variability, of biomass and
surface soil moisture when using global spatially distributed input data
about soil texture and meteorology and assuming a generic crop. The model
performance will be evaluated over Europe at a spatial resolution of 30 arcsec (1/120∘; ∼1km at the Equator) using
satellite products derived from both optical and microwave sensors as well as in situ
measurements.
The structure of the paper will be as follows: Sects. 2 and 3 will cover the
methodology, with a description of the regional AquaCrop model setup, the
evaluation datasets, and performance metrics. In Sect. 4 the results will be
presented and discussed, followed by a conclusion in Sect. 5.
The regional gridded AquaCrop modelAquaCrop equations
AquaCrop is a daily crop-water productivity model that translates, on a daily
basis, the simulated amount of crop transpiration into a proportional amount
of biomass for a single field, which is assumed to be homogeneous (Raes
et al., 2009; Steduto et al., 2009). The relation between transpiration and
biomass production is defined by a water productivity (WP) factor.
B=WP*⋅∑TrET0B (tha-1) is the cumulative biomass produced, WP* is the WP
(gm-2) factor normalized for atmospheric CO2
(369.41 ppm for the year 2000) and for climate, and Tr
(mmd-1) is the transpiration, also normalized for climate after
division by the reference evapotranspiration, ET0
(mmd-1). Because of this normalization, the WP* factor only
significantly differs between C3 and C4 crops, with C4 crops having a higher
WP* due to a more efficient carbon assimilation process. The calculation of
Tr is dependent on ET0, the adjusted green canopy cover (CC*; –),
the crop transpiration coefficient (Kc,tr; –), the cold stress
coefficient (KsTr; –), and the soil water stress coefficient
(Ks; –).
Tr=Ks⋅KsTr⋅Kc,tr⋅CC*⋅ET0
To calculate the soil water balance, AquaCrop divides the soil profile into
multiple compartments (default 12) with depth increments Δz (default
0.1 m). For deeper soils, Δz increases exponentially with
increasing soil depth so that the processes of the near-surface layers can
still be resolved with sufficient detail. The number of compartments is
independent of the number of soil horizons, and the hydraulic properties for
each compartment will be used depending on the soil layer in which they
reside. The simulation of the water content in each compartment is done with a
set of finite-difference equations (subroutines) that are defined in terms of
the dependent variable Θ, as represented in Eq. (3) (Raes et al.,
2018). First, the drainage of the soil profile is calculated. Then, the water
infiltration is computed (after subtraction of surface runoff) and upward
movement of water by capillary rise is estimated. Finally, the amount of water
lost by evaporation and crop transpiration is subtracted:
θi,j=θi,j-1+ΔθDFi,Δt+ΔθIi,Δt+ΔθCRi,Δt3+ΔθEi,Δt+ΔθTi,Δt,
where θi,j is the soil water content of compartment i at
time step j, θi,j-1 is the water content of compartment
i at the previous time step, and ΔθXi,Δt
indicates the change in moisture due to various processes X, with
X=DF. DF is downward flow, I is infiltration, CR is
capillary rise, E is soil evaporation, and
T is crop transpiration.
Downward flow over the compartments is described by an exponential drainage
function (Eq. 4) based on the volumetric water content in the compartment i
(Θi) within the soil layer and drainage characteristics of the
soil layer (Raes et al., 2006, 2009).
ΔθDFi,Δt=τiθsat-θFCeθi-θFC-1eθsat-θFC-1ΔθDFi,Δt is the decrease in water content over
time (m3m-3d-1), θFC and θsat are the volumetric moisture content at field capacity and at
saturation (i.e. the porosity) of the soil layer, and τi is the
drainage coefficient derived from the saturated hydraulic conductivity
(Ksat). Infiltration (I) is the sum of water that
enters the soil, which is rainfall minus surface runoff and possibly
irrigation. Internal drainage between compartments is defined by the drainage
ability, which depends on θsat and θFC
(Eq. 4). The cumulative drainage amount from any compartment will percolate
through as long as its drainage ability is greater than or equal to the
drainage ability of the overlying compartment. If the drainage ability is
lower than the overlying compartment, the cumulative drainage amount will be
stored in that compartment, increasing the water content and thereby its
drainage ability. If the drainage ability then reaches the equal amount of
that of the overlying compartment, excess drainage will percolate through to
the lower compartment. For the bottom soil compartment, the drainage is lost
as deep percolation. The runoff is estimated based on the curve number (CN)
method developed by the US Soil Conservation Service (USDA, 1964). The CN
values are dependent on Ksat of the topsoil
layer. Upward flow by capillary rise is estimated based on the depth of the
groundwater table and hydraulic characteristics of the soil layers. Since no
groundwater table is implemented in the regional version of the model in this
paper, capillary rise is not included in the simulations. Soil evaporation is
based on the soil wetness and crop cover (Ritchie, 1972), and water extraction
by roots is described with the sink term from Feddes (1982). Because the root
density for most crops is highest near the soil surface and decreases with
increasing soil depth, the water extraction pattern by roots is simulated as
follows: 40 %, 30 %, 20 %, 10 % for the upper
quarter to the lowest quarter of the root zone (Raes et al., 2009). The
estimated water retained in the root zone that will be available to the plants
(Wr) at each daily time step is described by the fraction of total available
water (TAW) after subtraction of depleted water (Dr). TAW is the difference of
volumetric moisture content between field capacity (θFC)
and wilting point (θWP) over the root zone and is
therefore dependent on soil texture and depth.
Plant stresses, such as water excess or water limitation, cold–heat air
temperature stress, soil fertility, and salinity stresses, can affect biomass
production during different steps of the calculation procedure, depending on
the process that is affected (i.e. canopy expansion, crop transpiration,
pollination). The inclusion of stress factors is done by assigning unique
thresholds to each of these biological processes (Raes et al., 2018). Further
details on the AquaCrop equations can be found in the calculation procedure
manual by Raes et al. (2018).
Regional model setup
The model domain of this study covers the agricultural land in the central
part of Europe (35–55∘ N, 10–20∘ E) and 45 pixels across
all of mainland Europe where in situ soil moisture data are available for
evaluation (three in situ points are also included in the central European
domain). The model was run for the years 2011 through 2018, starting on 1 January 2011. The initial soil moisture content for the first year
was set at θFC, since the runs were initiated midwinter,
and for the subsequent years the initial soil moisture content was based on
the moisture content of the last day from the previous year. Because the
evaluation for soil moisture was done with microwave-based satellite products
that pertain to the surface layer, the AquaCrop volumetric moisture content of
the topsoil compartment (WC01) at a depth of 0.05 m (centre of top
10 cm) was chosen for evaluation in this study. For the biomass, the
daily productivity (tha-1) was derived from the cumulative
biomass. In the regional version of AquaCrop, a single homogeneous field is
represented by a 30 arcsec (∼1km) pixel, and input and
output were defined independently for each pixel. The system can easily be set
up for any given resolution over any domain. In this study, the model was run
exclusively for dominantly rainfed agricultural areas based on the land use
map of the CORINE land cover inventory (Büttner, 2014) for the year
2012. This dataset is available at 100 m resolution and was aggregated
to 30 arcsec. To best represent the pixels as agricultural fields, only
pixels were included for which at least 50 CORINE pixels (∼50%
of one AquaCrop pixel) contained non-irrigated agriculture.
The AquaCrop input data are categorized into several components, e.g. climate,
soil, vegetation, and management. For each component, parameters are described
in a text file with a specific file extension that is recognized by the
model. A project management (PRM) file oversees all the information for a
single field (or pixel) and contains paths and names of these input
files. This PRM file is read and executed by AquaCrop, after which an output
file is created.
The original Pascal source code of AquaCrop v6.1 was minimally adjusted and
compiled on the Linux-based high-performance computer (HPC) of the Vlaams
Supercomputer Centrum (VSC), and the resulting executable was plugged into a
Python wrapper to allow massively parallel simulations to optimize the model
efficiency over larger spatial domains. The current system allows for easy
implementations of later versions of AquaCrop. The regional input files have
to be prepared before model execution. The Python wrapper then creates the
PRM file for a pixel as a first step of the model run, after which the
AquaCrop model is executed and time series output is stored into a new folder
for each pixel. The reason for creating the PRM files right before the model
execution is so that changes to a project can be made quickly. With this
setup, AquaCrop simulations over 1000 pixels for 8 years can be completed in a
wall time of 2.2 min when using 36 processors. The runs over the
domain and period used in this study were completed in approximately
20 h on 36 processors.
Model input
The meteorological forcings were extracted from the global Modern-Era
Retrospective analysis for Research and Applications version 2 (MERRA-2;
Gelaro et al., 2017). The MERRA-2 meteorological variables have a 3-hourly
temporal resolution and a spatial resolution of 0.5∘lat×0.625∘ long, and they are readily available at a latency of about a
month. A nearest-neighbour function was used to identify the 30 arcsec
pixels situated within one MERRA-2 grid to assign meteorological
input. Minimum and maximum temperature and precipitation were converted into
daily data needed for the AquaCrop model. The reference evapotranspiration
ET0 was derived from the FAO Penman–Monteith equation using radiation,
wind speed, average temperature, and dew temperature from MERRA-2 (Allen
et al., 1998). For the FAO Penman–Monteith equation, a psychrometric constant
of 0.067 was assumed for the entire domain, and variations in topographic
elevation were not taken into account. At high elevations
(>1kma.s.l.) this could result in deviations of ET0 of max
0.2 mmd-1. However, since most agricultural areas are located at
much lower elevations, the effect of the psychrometric constant was assumed to
be very small. The long record of mean annual CO2 concentration
observed at Mauna Loa (Hawaii, USA) was used as CO2 input (default
file in the database of AquaCrop).
The soil texture and organic matter were taken from the Harmonized World Soil
Database version 1.2 (HWSDv1.2). The HWSDv1.2 has a spatial resolution of 30 arcsec. The hydraulic soil properties for 253 different soil classes were
linked to the information on mineral soil texture and organic matter from the
HWSDv1.2 via pedo-transfer functions as in De Lannoy et al. (2014). More
specifically, AquaCrop uses the soil water content at various matrix
potentials, i.e. θWP, θFC,
θsat, and Ksat. These parameters are available
for a top layer (0–30 cm) and a deeper layer
(30–100 cm). Stoniness and soil salinity were not considered. No
restrictions on the root zone development by impermeable layers were included
in the simulations. According to the 1∘ global dataset of soil depth
to bedrock used by the Second Global Soil Wetness Project (Dirmeyer and Oki,
2002; Mahanama et al., 2015) and the 250 m resolution map developed by
Shangguan et al. (2017), the bedrock is generally deeper than 1 m over
the study area, which allows for reaching the maximum effective rooting depth.
A soil fertility stress parameter in the field management file provides an
indication of the overall soil quality. The default of this parameter is
0 %, referencing to unlimited soil fertility with the perfect
concentrations of plant nutrients. Since this situation is very rare in real
fields, even for well-maintained land, the value was manually tuned to
30 % after initial model evaluation of daily biomass production with
the CGLS-DMP (see Sect. 3.1) product for several pixels. With this reduction
in soil fertility, a good to moderate crop production over the entire domain
can be simulated in the absence of water stress, which is a setting recommended by
expert knowledge of the AquaCrop source code developers.
Main crop parameters of the generic crop to simulate biomass over Europe.
Generic crop main parametersInputCrop typeleafy vegetable cropCrop is sown/crop is transplantedcrop is transplantedDetermination of crop cyclecalendar daysCoefficient for maximum crop transpiration (Kc,tr,x; –)1.10Base temperature (∘C) below which crop development does not progress8.0Upper temperature (∘C) above which crop development no longer increases with an increase in temperature30.0Minimum effective rooting depth (m)0.3Maximum effective rooting depth (m)1.0Normalized water productivity factor (WP*; gm-2)17.0Calendar days from transplanting to recovered transplant0Calendar days from transplanting to maximum rooting depth80Calendar days from transplanting to start senescence232Calendar days from transplanting to maturity365Calendar days from transplanting to flowering0Minimum growing degrees required for full crop transpiration (∘C per day)10.0
Flowchart of the regional model setup with gridded meteorological and
soil input data as well as generic crop and management input data indicated on the
left side. The parallel computing system with a maximum of N cores can
execute N pixels at the same time. The composited output files can then be
visualized as maps or time series.
A single crop file was created to simulate crop production over
Europe. Spatial and temporal gaps of information at the ∼1km
resolution prevent the inclusion of a more detailed crop
parameterization. Furthermore, this research is focused on capturing relative
temporal variation in biomass (not yield) for future use in a data
assimilation system, so a generic crop was developed and used for the entire
domain. It is expected that regional differences of crop productivity from
different crops will be corrected for via future data assimilation. After
visual model evaluation and quantitative comparisons against satellite-based
dry matter productivity (DMP, see below; Smets et al., 2019), the date of
senescence was tuned manually to optimally capture the length of the growing
season. A generic reference crop was developed to simulate annual biomass
development of C3 crops. C3 crops are predominantly found in temperate
climates, as opposed to C4 crops that are more common in hot and dry
climatological zones (Monfreda et al., 2008; Still et al., 2003). The crop was
simulated as a transplant, assuming a small presence of vegetation from the
start of the season, and with an annual cycle of 365 d starting on
1 January. Because of this fixed annual cycle, the canopy
development had to be simulated in calendar days instead of the more commonly
used growing degree days. This results in an error in the simulation of canopy
expansion during cold periods, but due to the consideration of growing degrees
in the simulation of crop transpiration with the cold stress factor
(KsTr; Eq. 2), the reduced biomass production in these
periods is still captured. As can been seen from Eqs. (1) and (2), the factors
that affect the crop development, simulated by canopy cover, are soil water
stress and cold temperature stress. This generic crop file is mostly suitable
to simulate canopy development during the spring and summer season. The main
crop parameters are presented in Table 1, and a flowchart of the model setup
with input datasets is shown in Fig. 1.
Evaluation datasets and metricsCGLS-DMP
To evaluate simulations of daily biomass production, the ∼1km
dry matter productivity product from the Copernicus Global Land Service
(CGLS-DMP; kgha-1d-1) was used (Smets et al., 2019). The
CGLS-DMP is based on a simplified Monteith (1972) approach that makes use of
the fraction of absorbed photosynthetically active radiation (fAPAR), which is
derived from the optical satellite missions Satellite Pour l'Observation de la
Terre (SPOT; 1999–2014) and Project for On-Board Autonomy – Vegetation
(PROBA-V; 2014–June 2020), ECMWF re-analysis estimates of atmospheric
variables such as radiation and temperature, and land cover information from
the ESA CCA Land Cover Map. The retrieval algorithm is thus driven by
atmospheric water availabilities, without explicitly accounting for water
storage in the soil. The CGLS-DMP product is provided in 10 d
time steps;
each value is representative of the past 10 d for the years 1999
up to present date. To compare the data with the AquaCrop biomass, the
nearest-neighbour function was used to spatially match the gridded simulations
to the grid of CGLS-DMP, and the median of the modelled daily biomass
production was computed for the corresponding 10 d intervals of the
CGLS-DMP products. Since the crop parameterization in AquaCrop is suited to
simulate the main growing season, the months November to February were not
included for the biomass evaluation.
CGLS-SSM
AquaCrop surface moisture content, i.e. the output of soil moisture in the top
compartment of the soil profile, was evaluated with the CGLS relative surface
soil moisture product CGLS-SSM. CGLS-SSM provides data for the top few
centimetres of the soil, which are available at the same ∼1km resolution
as CGLS-DMP. This product is derived from the C-band radar on board Sentinel-1,
processed by the TU Wien (Bauer-Marschallinger et al., 2018), and available
from October 2014 onwards. Processing steps included geo-correction,
radiometric calibration, and normalization of the incidence angle. No
correction was included for dynamics in vegetation or surface roughness. The
data are provided as relative soil moisture estimates (%) that have to be
multiplied by the porosity (θsat) to convert
to absolute volumetric soil moisture contents (m3m-3). The
Sentinel-1 satellite has varying overpass densities, resulting in a slightly
different number of data points in various areas, but the temporal resolution
is generally between 3 and 8 d. To exclude potential data points for
days on which the soil was nearly frozen, the soil temperature variable from
MERRA-2 was used to identify and remove all data for which the soil temperature
was below 4 ∘C, following the recommended data masking by e.g.
Gruber et al. (2020). The CGLS-SSM product contains masks for areas where it
cannot be applied, i.e. a water mask for pixels containing only water, a
sensitivity mask for pixels with a low sensitivity (urban, rivers, dense
forests), and a slope mask screening out pixels with a topographic slope
higher than 17∘.
SMAP-SSM
Surface soil moisture simulations were further evaluated with retrievals from
the NASA Soil Moisture Active–Passive (SMAP) mission from April 2015
onwards. More specifically, the enhanced level-2 radiometer half-orbit
version 4 was used at 9 km resolution (Chan et al., 2018; Chaubell
et al., 2020). The SMAP radiometer measures L-band brightness temperatures in
vertical and horizontal polarization at an incidence angle of 40∘. It
scans the Earth's surface in a sun-synchronous orbit, which is
06:00 for descending and 18:00 for ascending mode (both local time),
with a temporal resolution of 2–3 d. The SMAP product provides
three estimates of surface (∼5cm) soil moisture
(m3m-3) derived from different retrieval algorithms (O'Neill
et al., 2020). The single-channel algorithm using vertical polarization is
the current baseline for SMAP soil moisture and was also used for AquaCrop
evaluations.
SMAP data are projected onto the 9 km EASE-Grid version 2.0 (EASE2;
Brodzik et al., 2012), and the AquaCrop soil moisture output was aggregated to
this grid by simply averaging all ∼1km pixels belonging to the
same EASE2 grid cell. Only cells that were at least 50 % filled with
AquaCrop output were included for evaluation. The number of AquaCrop pixels
per 9 km grid cell varies depending on the location on the EASE2
grid. For SMAP-SSM, the recommended conservative quality control was applied,
and a temperature threshold of 4 ∘C, derived from the GMAO
GEOS land surface model, was applied to exclude nearly frozen soils (O'Neill
et al., 2018).
In situ SSM
In situ soil moisture measurements up to a depth of 5 cm were taken from
the International Soil Moisture Network (ISMN; Dorigo et al., 2011) to
evaluate AquaCrop simulations and satellite soil moisture products across all
of mainland Europe. The corresponding soil temperature data were used to
exclude the dates with temperatures below 4 ∘C. Whenever
multiple in situ observation points were available within one AquaCrop pixel,
the mean of those points was taken. AquaCrop simulations were cross-masked with
both in situ data and the respective satellite product (CGLS-SSM, SMAP-SSM) to
perform an in situ (and satellite-based) evaluation at each point. In situ
data from the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen,
Austria (∼49∘57′ N, 14∘52′ E),
were made available by partners of the SHui consortium with data contributed to
three extra clustered pixels for CGLS-SSM, resulting in a total of 45
evaluation points for CGLS-SSM and 32 for SMAP-SSM in non-irrigated
agricultural areas.
Metrics
To assess the temporal performance of the AquaCrop model, the bias, root mean
square difference (RMSD), unbiased RMSD (ubRMSD), temporal Pearson correlation
(R), and anomaly correlation (anomR) were calculated with satellite and in situ
products as follows:
5Bias=1N∑n=1N(xn-yn),6RMSD=1N∑n=1N(xn-yn)2,7ubRMSD=RMSD2-bias2,8R=∑n=1N(xn-x‾)(yn-y‾)∑n=1Nxn-x‾2∑n=1Nyn-y‾2,
where x represents the simulated output data from AquaCrop, y represents the
observations from the satellite products, and N is the number of
observations. x‾ and y‾ are the time mean values. For the anomR,
x and y are anomaly time series.
Comparing products with different spatial resolutions will always result in
representativeness bias, which is especially acute when using in situ
observations to evaluate pixel-scale estimates. Therefore, the focus of the
evaluation was on temporal variability using the R, anomR, and ubRMSD
metrics. The time period used for validation depended on the evaluation
product. When using satellite-based soil moisture, only grid cells were
included when at least 150 CGLS-SSM or 200 SMAP-SSM retrievals (after quality
control) were available during the overlapping period of satellite data
(starting in 2014 for CGLS-SSM and in 2015 for SMAP-SSM) and simulations. When
further comparing the satellite products to in situ data, a relaxed minimal
threshold of 100 data pairs was set for the period of available data for each
satellite product. For CGLS-DMP, the 10 d data are complete between
2011 and 2018 and only March through November are included in the evaluation
metrics.
The anomR was computed to assess both the short-term and inter-annual
variability of biomass and soil moisture compared to the satellite products
only, for lack of sufficiently long records of in situ data. A multi-year
climatology (8 for CGLS-DMP, 4 for CGLS-SSM, and 3.5 for SMAP-SSM) was computed
and subtracted from the datasets to obtain anomalies as described by Gruber
et al. (2020). The climatology is built on 31 d moving window
averages, requiring either a minimum of three 10 d CGLS-DMP estimates or a
minimum of 10 instantaneous CGLS-SSM and SMAP-SSM observations within a
31 d window. The climatology of AquaCrop was computed using the same
moving window and time period as the evaluation product. For surface soil
moisture, only daily model output that matched the days of observations of the
evaluation product was used, whereas for biomass evaluations, the data
consisted of the median of the matching 10 d period.
In this study, only rainfed agriculture is considered. However, it is very
likely that irrigation will occasionally take place on rainfed fields, where
the timing and volume are based on local decisions made by farmers. Irrigation
practices were not included in the model simulations. To analyse how this
human-driven process could influence the model performance, the FAO map of area
equipped for irrigation (AEI; Siebert et al., 2015) was used to identify
areas that are occasionally irrigated and which were not necessarily captured
by the irrigation class from the CORINE land cover inventory. The latter only
considers regularly irrigated areas to distinguish rainfed from irrigated
land. The available 1 and 10 km AEI map versions were used to stratify
correlation values with CGLS-DMP and with SMAP-SSM, respectively.
Biomass production of CGLS-DMP and AquaCrop during different days of
the year 2017. Light grey areas represent no data.
Results and discussionBiomass
A visual comparison of simulated and satellite-based biomass on different days
in the year 2017 is presented in Fig. 2 and gives an indication of the
spatial performance of the regional AquaCrop model against the CGLS-DMP
product. The figure shows that the model is able to capture large regional and
temporal differences in biomass production, but the absolute values can differ
between CGLS-DMP and the model. The coarser-resolution MERRA-2 climate input
is visible in the blocky pattern of the AquaCrop biomass maps. For the days in
June and July, simulations over most of Italy ceased to produce biomass,
whereas the CGLS-DMP product shows spatial variability in productivity. Water
stress in the simulations brings crop production to a halt, which is not
in agreement with the CGLS-DMP. This can be caused by an overestimation
of water stress by the model, unmodelled irrigation, or the CGLS-DMP
product not accounting for drought stresses well. Drought stress is
indirectly included in the CGLS-DMP via the observed fAPAR but could still
lead to overestimations of DMP in drier periods (Smets et al., 2019).
Temporal metrics of AquaCrop biomass evaluated against CGLS-DMP, with
metrics R (–), anomR (–), bias (tha-1d-1), and ubRMSD (thad-1). The spatial mean and standard deviation of the metrics are indicated with MEAN and SD. Also shown is the TAW (m3m-3) computed as the field capacity minus wilting point, without taking rooting depth into account. Light grey areas represent no data.
Box plots showing the distribution of biomass anomaly correlations for
the northern half of the study domain (46–55∘ N), grouped by
different TAW ranges.
Time series of biomass productivity, anomaly daily productivity (and
precipitation) for (HL) the HOAL catchment in Austria, and (AP) a pixel in the
Apulia region of southern Italy; both are marked in Fig. 3. Precipitation is only
shown for AP because it has a marked effect there on short-term anomaly
productivity. Periods between October and March are masked out in grey for
precipitation.
Figure 3 summarizes the performance metrics of AquaCrop biomass simulations
against CGLS-DMP. Differences in absolute values of biomass estimates are
inevitable because of representativeness errors in both the model and
satellite retrievals. For example, the model uses a generic crop, for which
the parameters could be locally optimized. Nevertheless, the long-term biases
are limited and cancel out over the entire domain. When focusing on the
temporal variability, the temporal correlations indicate a high performance,
with an overall mean of R=0.8. Higher correlations are mostly found in the
northern part of Europe. Lower correlations are specifically found in the
upper north and in the south (Italy). Similarly, the ubRMSD is highest in the
southern half of the study domain. The spatial variability in ubRMSD can be
attributed to different factors that limit crop growth, which will be mostly
cold temperatures in the north and low soil water contents in the
south. Across the domain the ubRMSD is 0.03 tha-1d-1 and
typically less than 20 % of the amplitude in biomass production. The
anomaly correlation is lower than the correlation but still significant, with
a mean of anomR = 0.46. The raw correlation includes the trivial agreement in
the seasonal variability and is thus inevitably higher, whereas the anomaly
correlation only evaluates short-term and inter-annual variability, as
illustrated in Fig. 5 for the HOAL catchment in Austria. Both the model and
satellite data show anomalously high biomass production in June 2017, whereas
anomalously low values are found in both datasets in June 2013. The short-term
anomaly biomass productivity increments also correspond well to the
evaluation data, but for AquaCrop they are often more pronounced. For regions
in the south of Europe (Italy), simulated productivity anomalies are much more
pronounced, clearly showing the modelled response to stronger rainfall events
after a relatively dry period. When comparing this to the CGLS-DMP product, it
shows anomalies that are either less extreme or do not match the anomalies of
the model simulations, resulting in lower anomaly correlations (Fig. 5). This
emphasizes the importance of high-resolution precipitation information for
climatic regions in which precipitation is the main limiting factor for crop
production. Across the northern region, the lower anomaly correlation values
can be partly associated with soil texture (TAW), as can be seen from Figs. 3
and 4. In areas where there is a sufficient amount of rainfall but soils are
typically sandy and have a low TAW and high Ksat, soil
water easily drains through the profile, which prevents optimal crop
production. The effect of such stresses may not be observed in the CGLS-DMP
and will result in deviating inter-annual variabilities.
Temporal performance metrics of AquaCrop SSM evaluated against
(a) SMAP-SSM and (b) CGLS-SSM, i.e. R (–), anomR (–),
bias (m3m-3), and ubRMSD (m3m-3), with indication of
the spatial mean and standard deviation of the metrics (MEAN, SD). Light
grey areas represent no data.
Surface moisture content
Surface soil moisture content was evaluated using three products at different
scales: point measurements from ISMN and some additional sites in the HOAL
catchment, 1 km CGLS-SSM, and 9 km SMAP-SSM. Figure 6 shows the
AquaCrop performance metrics against the satellite data. The spatial mean R and anomR values with SMAP retrievals are 0.74 and 0.65, respectively. The
anomR is especially high in the central part of Europe and decreases towards
the north. Overall, AquaCrop is much better correlated with SMAP-SSM than with
CGLS-SSM. The mean R and anomR values of AquaCrop SSM with CGLS-SSM are 0.52
and 0.50, respectively. Figure 7 illustrates that the lower agreement between
AquaCrop and CGLS-SSM data is not solely due to the inevitably higher noise in
the finer-scale CGLS-SSM data. When aggregating CGLS-SSM to the EASE2
9 km grid using the same spatial mask of SMAP-SSM, the temporal
correlations with AquaCrop increase slightly, with a mean R of 0.57 (spatial
standard deviation SD: 0.08) and a mean anomR of 0.56 (SD: 0.06)
(Fig. 7), and they remain well below the correlation values between SMAP-SSM and
AquaCrop SSM.
Same as Fig. 6b, but after aggregation of the CGLS-SSM data to the
9 km EASEv2 grid and spatial cross-masking with SMAP-SSM data.
Several areas with higher elevations have lower correlation values (central
Italy, eastern Alps). The spatial correlations of AquaCrop SSM on the
9 km EASE2 grid with 9 km CGLS-SSM and 9 km SMAP-SSM
reveal a large variability in time, with a temporal mean spatial R of 0.38 and
temporal standard deviation of 0.21 for CGLS-SSM as well as a mean R of 0.32 and
temporal standard deviation of 0.22 with SMAP-SSM.
When looking at the absolute values of the bias and ubRMSD, the evaluation of
AquaCrop against CGLS-SSM (1 or 9 km) is also far worse than that
against SMAP-SSM, but the spatial pattern of the errors is similar for
SMAP-SSM and CGLS-SSM. The spatial mean ubRMSD against SMAP-SSM is
0.05 m3m-3, which is close to the global target product uncertainty of
0.04 m3m-3 (Entekhabi et al., 2014), and the spatial mean ubRMSD
against 1 km CGLS-SSM is 0.10 m3m-3. Also here, the
effect of soil texture on model performance was found. The ubRMSD values of
0.14 m3m-3 and higher for 1 km CGLS-SSM exactly correspond to
outliers in a specific soil class in the HWSDv1.2 classification that
contains 93 % sand. This soil class is characterized by very high
Ksat and very low values for θWP and
θFC, resulting in extremely low simulated available
moisture content in the top layers. Because the low θWP is
very close to the soil evaporation demand, the model is not able to simulate
soil moisture correctly for the top layers for daily time steps. AquaCrop is a
crop simulation model, and this soil class is unrealistic for agricultural
land. In future applications when multiple datasets from different sources are
combined, it is recommended to limit the simulations to possibilities that are
actually suitable for the specific simulation purpose. Nonetheless, the poorer
performance against the 1 km Sentinel-1-based CGLS-SSM is in general
not due to model shortcomings, but dominated by poor satellite retrievals, as
will be discussed below.
(a) Pearson correlation R values between in situ
measurements from the ISMN and AquaCrop surface soil moisture at 45 locations
over Europe, with grey pixels containing at least 50 % rainfed
agriculture according to the CORINE land cover map for 2012. Correlations shown
are from cross-masked data with CGLS-SSM. The circles indicate the locations
used for evaluations with both CGLS-SSM and SMAP-SSM, whereas triangles show
locations that were only used for CGLS-SSM. (b) Histogram of R
values between AquaCrop surface soil moisture and the two satellite products
CGLS-SSM (45 points) in grey and SMAP-SSM (32 points) in orange at the
locations of the in situ sites. (c) Histogram of the R values
between the in situ measurements and the two satellite products CGLS-SSM
(45 points) in grey and SMAP-SSM (32 points) in orange.
Time series of daily surface soil moisture at three locations marked
in Fig. 6a: 1 (∼55∘54′ N, 8∘52′ E), 2 (∼43∘39′ N, 0∘13′ E), and 3 (∼41∘17′ N, 5∘18′ W). AquaCrop (light blue) in situ measurements (dark grey), CGLS-SSM (light grey), and SMAP-SSM
(orange). Pearson correlations R of in situ data with the different products are given for each location.
A comparison between in situ data, 1 km CGLS-SSM, 9 km
SMAP-SSM, and 1 km AquaCrop surface soil moisture at ISMN sites and three
sites in the HOAL catchment is shown in Fig. 8. Across the in situ sites, the
mean R value between AquaCrop and in situ soil moisture is 0.61 (Fig. 8a) and
higher than the mean R value of 0.52 with CGLS-SSM (Fig. 8b). The mean ubRMSD
between AquaCrop and in situ measurements is 0.06 m3m-3, which is
significantly lower than the mean between AquaCrop and CGLS-SSM
(0.10 m3m-3). The mean R between Sentinel-1 CGLS-SSM and in situ
data is even lower, with a value of 0.42 and a mean ubRMSD of
0.11 m3m-3 (Fig. 8c). The comparison with the satellite products
over in situ sites shows that SMAP-SSM mean temporal correlations are
significantly better with both AquaCrop simulations (Fig. 8b; R=0.81,
ubRMSD = 0.05 m3m-3) and in situ measurements (Fig. 8c;
R=0.69, ubRMSD = 0.05 m3m-3) than CGLS-SSM, even though
SMAP-SSM has a lower spatial resolution. This is further illustrated in the
time series at three locations presented in Fig. 9, where SMAP-SSM follows the
pattern of in situ data well and slightly better than AquaCrop, whereas the
pattern of the CGLS-SSM values is more erratic. The high correlations between
SMAP-SSM and in situ measurements show that SMAP-SSM is better at capturing
variations at smaller scales than the current system of AquaCrop due the
coarse resolution of meteorological input data. Additionally, SMAP-SSM
retrievals probably benefit from a more accurate background representation of
the vegetation, whereas AquaCrop uses a generic crop description. For
CGLS-SSM, lower observed soil moisture was often found for the months April,
May, and June, as can be seen in Fig. 9b and c. The poor correlation of
CGLS-SSM during these months is most likely due to the fact that the
Sentinel-1 backscatter signals are dynamically affected by changing vegetation
during the growing season, but the soil moisture retrievals are only corrected
for with a static vegetation value for every day of the year. Furthermore,
changes in surface soil roughness are not accounted for in the retrievals and
could play an important role in the lower quality of the CGLS-SSM retrievals
(Bauer-Marschallinger et al., 2018).
Effect of irrigation
Figure 10 shows the spatial distribution of the R values of AquaCrop biomass
and soil moisture with CGLS-DMP and SMAP-SSM, respectively, grouped into two
percentage classes of AEI. In terms of biomass, higher R values between
AquaCrop and CGLS-DMP (mean R=0.81) are found for pixels wherein
AEI < 10 % than for areas where AEI ≥ 10 % (mean
R=0.72). For soil moisture, the correlation with SMAP-SSM barely shows any
difference between the AEI groups (AEI < 10 %: mean R=0.74;
AEI ≥ 10 %: mean R=0.73). It should be noted that SMAP-SSM
has much smaller coverage than the CGLS-DMP because SMAP-SSM is screened
conservatively based on its quality flags. The results of this comparison
suggest that, even if the simulations were limited to dominantly rainfed
agricultural areas according to the CORINE land use map and therefore did not
include irrigation, it is possible that in reality irrigation is occasionally
applied in rainfed fields and seen by the satellite data, resulting in lower
correlation metrics.
(a) Box plots with a violin curve of temporal R values
grouped by the FAO percentage of area equipped for irrigation (AEI). Group 1:
0 %–10 %, group 2: 10 %–100 %. The left side is for
CGLS-DMP and the right side for SMAP-SSM. The percentage of the total
number of pixels for each group and the spatial mean R value are noted at
the top of the figure. (b) AEI map over the study domain.
Discussion of the regional AquaCrop model
The current gridded AquaCrop model has several conveniences, such as the
efficient parallel processing structure, the ability to run at any resolution
and domain, and the modular setup in which a compiled executable can be easily
replaced by newer AquaCrop versions. The model setup is chosen to facilitate
subsequent embedding within a future satellite-based data assimilation system.
The regional modelling system was designed to capture the seasonal and
inter-annual variability, with some important simplifications. A general C3
crop was assumed, and management data were considered to be homogeneous over the
entire study area, whereas meteorology and soil information were spatially
variant. Therefore, the evaluation of this regional crop model setup against
satellite products was mainly done in terms of unbiased temporal
metrics. AquaCrop accurately simulates the temporal variability in biomass and
surface soil moisture, especially in the northern regions and if the soil's
TAW is not limiting. Limitations in the accuracy of the input precipitation
(MERRA-2) causes slightly worse simulations in the water-limited southern
regions, where biomass shows a fast response to limited (but sometimes
inaccurately timed) rainfall events. The use of high-resolution meteorological
forcing is likely to be the most important next step to further improve fine-scale
AquaCrop simulations. The evaluation was limited to surface soil moisture and
biomass but could be further expanded to other variables such as root zone
soil moisture and transpiration in the future. Reference data for the latter
variables are always informed by strong (often modelled) background
information (Martens et al., 2017; Reichle et al., 2019) and not directly
observed over large regions. Furthermore, applying crop-specific parameters to
the crop file would most likely result in better biomass and yield
simulations, which would mainly improve the temporal bias and spatial
performance metrics.
The suitability of this modelling system to estimate the spatial variability
in soil moisture and yield production for specific crop types would require
further analysis and more detailed input information. For example, by
combining input datasets from different sources, some unsuitable cropland
areas were identified (e.g. TAW that is too low in combination with high
Ksat) that were not filtered out from this
analysis. Furthermore, unmodelled irrigation could influence the regional model
performance. Most importantly, the relative spatial variability in biomass is
likely not dominated by meteorology and soil texture, but by the various types
of crops. The parameters associated with each of these crops could be
spatially optimized (calibration, data assimilation for parameter estimation)
in future work using historical time series of spatially covering reference
data, e.g. optical Sentinel-2 data.
The regional model evaluation could only be performed with satellite
retrievals, but such an evaluation is limited to the days of overpass and to
times and locations for which retrievals are of sufficient quality. For example,
SMAP-SSM retrievals are filtered out under vegetation that is too dense or frozen
conditions. Furthermore, the satellite signal may represent a slightly
different quantity than what is modelled. Additionally, microwave signals only
pertain to the upper 5 cm of the soil, but the model's surface layer
is 10 cm. The provided quality flags on CGLS-SSM are less strict,
providing better spatial coverage of fine-scale data. However, the C-band
soil moisture measurements pertain to an even shallower soil depth and are
likely more affected by vegetation. In any case, both the satellite retrievals
and model simulations have their own systematic and random errors. The
influence of the former is suppressed in this study by focusing on relative
temporal variability. To further dynamically improve model simulations or to
add value to the available satellite data (e.g. dynamically interpolate) via
AquaCrop modelling, random errors in both sources can be limited via data
assimilation for state updating.
Conclusions
In this paper, a spatially distributed version of the field-scale AquaCrop
model v6.1 is presented and evaluated against various satellite data products
and in situ data. The new regional AquaCrop infrastructure allows for simulation of
biomass and soil moisture over large domains in an efficient way due to the
massive parallelization of the gridded simulations. In this case study, the
regional AquaCrop model is forced with meteorological input based on MERRA-2
re-analysis data, the soil information is extracted from the HWSDv1.2, and a
generic crop is parameterized. Even when using coarse meteorological input
data, the AquaCrop model can capture seasonal, inter-annual, and short-term
temporal dynamics of biomass over Europe at a fine ∼1km
resolution. For the years 2011 through 2018, the temporal R between the
AquaCrop biomass production and CGLS-DMP is 0.8, and the anomR is 0.46, across
central Europe. The R values are higher in the northern half of the study
domain, where crop growth is generally temperature-limited, whereas in the
southern half of the domain, water stress becomes more important and the R
values are lower. Likely factors that can influence this difference in
correlation are an underrepresentation of drought stress by the CGLS-DMP
product, the effect of occasionally applied irrigation which is not included
in the model, or possibly overestimations of simulated drought stress by the
model. Additionally, the impact of soil parameters is apparent in the anomR
values; lower TAW values in the northern part result in differing
anomalies for modelled biomass and CGLS-DMP.
The AquaCrop simulations for surface moisture content show that seasonal,
inter-annual, and short-term temporal dynamics correspond well to the
9 km SMAP-SSM data, with a mean R value of 0.75 and an anomR value
of 0.65 across the study domain. Lower R values are found for Sentinel-1
CGLS-SSM, with a mean temporal R of 0.52 (aggregated to 9 km EASE2
grid: 0.57) and a similar anomR of 0.50 (aggregated to 9 km EASE2
grid: 0.56). The comparison between AquaCrop, CGLS-SSM, SMAP-SSM, and in situ
data for 45 (32 for SMAP-SSM) locations in Europe shows that both AquaCrop and
SMAP-SSM agree better with in situ data (mean R=0.61, 0.69, respectively)
than Sentinel-1 CGLS-SSM (mean R=0.52). The lower performance of Sentinel-1
CGLS-SSM can be attributed to the static correction for vegetation, which
causes soil moisture retrieval errors during the growing season, and the fact
that there is no correction for surface roughness (Bauer-Marschallinger
et al., 2018). For both the evaluations with SMAP and Sentinel-1 retrievals,
the effect of soil characteristics influences the evaluation performance of
the AquaCrop model. When certain soil characteristics are unsuitable for crop
cultivation, such as a high Ksat, a very low θWP, and low TAW, soil moisture becomes inaccurately represented by
the AquaCrop model, increasing the model error. At the same time,
satellite-based soil moisture retrievals also contain errors related to a
priori defined soil hydraulic parameters.
Improvements to the regional AquaCrop model can be made in terms of higher-resolution meteorological input data to better capture small-scale spatial
differences by revising the soil hydraulic parameters to better represent
soil types used for agricultural land and by introducing spatio-temporally
varying crop parameters when such information becomes available. Overall, the
current model is able to represent temporal and spatial differences well at
the field and regional scale in both biomass production and surface soil
moisture, requiring only easily accessible input data. The computationally
efficient modelling system is ideal to foster future improvements in the
spatial patterns in both soil moisture and biomass production via local
parameter optimization based on historical records of satellite data, as well as
improvements in the short-term and inter-annual temporal variations via
sequential satellite data assimilation.
Code and data availability
The code and data needed to run the regional version of AquaCrop v6.1 on a Linux-based system are available on Zenodo at https://doi.org/10.5281/zenodo.4770738 (de Roos et al., 2021). Apart from the code, this repository includes the generic crop file, the management file, and ancillary soil data from De Lannoy et al. (2014) at https://doi.org/10.1002/2014MS000330. All other input data and evaluation datasets are freely available, except for the in situ measurements of the HOAL experiment site. Please visit the following links for data access. MERRA-2 variables: https://disc.gsfc.nasa.gov/datasets?project=MERRA-2 (last access: 24 May 2019, Global Modeling and Assimilation office, 2015a, 10.5067/VJAFPLI1CSIV, 2015b, 10.5067/RKPHT8KC1Y1T); the soil mineral classification and organic matter from HWSDv1.2: http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last access: 30 August 2019, FAO/IIASA/ISRIC/ISSCAS/JRC, 2012); the CORINE land cover map: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=mapview (last access: 5 September 2019, Copernicus Global Land Service, 2018);
evaluation datasets CGLS-DMP: https://land.copernicus.eu/global/products/dmp (last access: 2 February 2020, Copernicus Global Land Service, 2019a) and CGLS-SSM: https://land.copernicus.eu/global/products/ssm (last access: 2 June 2020, Copernicus Global Land Service, 2019b);
SMAP enhanced L2 radiometer half-orbit 9 km EASE-Grid soil moisture version 4: https://nsidc.org/data/SPL2SMP_E/versions/4 (last access: 14 November 2020, O’Neill et al., 2020, 10.5067/Q8J8E3A89923);
ISMN soil moisture from various observatories at a depth of 5 cm: https://ismn.geo.tuwien.ac.at/en/ (last access: 29 June 2020, GEWEX/CEOS/GCOS-TOPC/GEO/GTN-H, 2011;
Gonzalez-Zamora et al., 2018, 10.1016/j.rse.2018.02.010;
Calvet et al., 2007, 10.1109/IGARSS.2007.4423019;
Zacharias et al., 2011, 10.2136/vzj2010.0139;
Jensen and Refsgaard, 2018, 10.2136/vzj2018.03.0059); FAO irrigation maps: https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/ (last access: 8 June 2020, Siebert et al., 2013).
Author contributions
SDR created the code to execute the regional version of the model, prepared the input data, and conducted the model evaluation. GDL prioritized the main steps taken in the paper, provided supervision and scientific guidance throughout all research, and managed HPC usage. DR provided scientific guidance regarding the use and interpretation of the AquaCrop model, developed the generic crop file, and provided the source code of AquaCropv6.1. SDR wrote the paper, and all authors contributed.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors would like to thank the HPC VSC team, in particular Geert-Jan Bex
and Martijn Oldenhof, for their help during the AquaCrop compilation on the
VSC HPC. We would also like to thank Peter Strauss and Gerhard Rab from the Vienna
University of Technology (TU Wien) for sharing their data from the HOAL
experiment site and Stefan Siebert for providing a 1 km map with area
equipped for irrigation. Finally, we greatly appreciate the review comments
from Christoph Müller, an anonymous reviewer, and the editors.
Financial support
This research has been supported by the European Commission, Horizon 2020 Framework Programme (SHui (grant no. 773903)). SHui is co-funded by the European Union Project GA 773903 and the Chinese MOST.
Review statement
This paper was edited by David Lawrence and reviewed by Christoph Müller and one anonymous referee.
ReferencesAbedinpour, M., Sarangi, A., Rajput, T. B. S., Singh, M., Pathak, H., and Ahmad, T.: Performance evaluation of AquaCrop model for maize crop in a semi-arid environment, Agr. Water Manage., 110, 55–66, 10.1016/j.agwat.2012.04.001, 2012.Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, 10.5194/hess-12-1323-2008, 2008.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements, FAO Irrigation and drainage paper 56, FAO, Rome, Italy, ISBN 92-5-104219-5, 1998.Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P. J., Rötter, R. P., Cammarano, D., Brisson, N., Basso, B., Martre, P., Aggarwal, P. K., Angulo, C., Bertuzzi, P., Biernath, C., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R., Grant, R., Heng, L., Hooker, J., Hunt, L. A., Ingwersen, J., Izaurralde, R. C., Kersebaum, K. C., Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G., Olesen, J. E., Osborne, T. M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M. A., Shcherbak, I., Steduto, P., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., Wallach, D., White, J. W., Williams, J. R., and Wolf, J.: Uncertainty in simulating wheat yields under climate change, Nat. Clim. Change, 3, 827–832, 10.1038/nclimate1916, 2013.Aznar-Sánchez, J. A., Piquer-Rodríguez, M., Velasco-Muñoz, J. F., and Manzano-Agugliaro, F.: Worldwide research trends on sustainable land use in agriculture, Land Use Policy, 87, 104069, 10.1016/j.landusepol.2019.104069, 2019.Balkovic, J., van der Velde, M., Schmid, E., Skalský, R., Khabarov, N., Obersteiner, M., Stürmer, B., and Xiong, W.: Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation, Agr. Syst., 120, 61–75, 10.1016/j.agsy.2013.05.008, 2013.Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S., Stachl, T., Modanesi, S., Massari, C., Ciabatta L., Brocca L., and Wagner, W.: Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles, IEEE T. Geosci. Remote, 57, 520–539, 10.1109/TGRS.2018.2858004, 2018.Boogaard, H., Wolf, J., Supit, I., Niemeyer, S., and van Ittersum, M.: A
regional implementation of WOFOST for calculating yield gaps of autumn-sown
wheat across the European Union, Field Crop. Res., 143, 130–142,
10.1016/j.fcr.2012.11.005, 2013.Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: EASE-Grid 2.0: Incremental but significant improvements for Earth-gridded data sets, ISPRS Int. Geo-Inf., 1, 32–45, 10.3390/ijgi1010032, 2012.Büttner, G.: CORINE land cover and land cover change products, Land use and land cover mapping in Europe, Springer, Dordrecht, the Netherlands, 55–74, 10.1007/978-94-007-7969-3, 2014.Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., and Piguet, B.: In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network, International Geoscience and Remote Sensing Symposium, IGARSS, Barcelona, Spain, 23–28 July 2007, 1196–1199, 10.1109/IGARSS.2007.4423019, 2007.Chan, S., Bindlish, R., O’Neill, P. E., Jackson, T., Njoku, E.G., Dunbar, S., Chaubell,J., Piepmeier, J. R., Yueh, S., Entekhabi, D., Colliander, A., Chen, F., Cosh, M., Caldwell, T., Walker, J., Berg, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Uldall, F., Seyfried, M., Bosch, D., Starks, P., Holifield -Collins, C., Prueger, J., Van der Velde, R., Asanuma, J., Palecki, M., Small, E. E., Zreda, M., Calvet, J., Crow, W. T., and Kerr, Y. Development and assessment of the SMAP enhanced passive soil moisture product, Remote Sens. Environ., 204. 931–941, 10.1016/j.rse.2017.08.025, 2018.Chaubell, M. J., Yueh, S. H., Dunbar, R. S., Colliander, A., Chen, F., Chan, S. K., Entekhabi, D., Bindlish, R., O'Neill, P. E., Asanuma, J., Berg, A. A., Bosch, D. D., Caldwell, T., Cosh, M. H., Collins, C. H., Martinez-Fernandez, J., Seyfried, M., Starks, P. J., Su, Z., Thibeault, M., and Walker, J.: Improved SMAP Dual-Channel Algorithm for the Retrieval of Soil Moisture, IEEE T. Geosci. Remote, 58, 3894–3905, 10.1109/TGRS.2019.2959239, 2020.Copernicus Land Monitoring Service: CORINE land cover (CLC) 2012, European Environment Agency (EEA) [data set], available at: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=mapview (last access: 5 September 2019), 2018.Copernicus Global Land Service: Dry matter productivity 1km product version 2, European Environment Agency (EEA) [data set], available at: https://land.copernicus.eu/global/products/dmp (last access: 2 February 2020), 2019a.Copernicus Global Land Service: Surface soil moisture, European Environment Agency (EEA) [data set], available at: https://land.copernicus.eu/global/products/ssm (last access: 2 June 2020), 2019b.Dale, A., Fant, C., Strzepek, K., Lickley, M., and Solomon, S.: Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa, Earths Future, 5, 337–353, 10.1002/2017EF000539, 2017.De Lannoy, G. J. M. and Reichle, R. H.: Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model, Hydrol. Earth Syst. Sci., 20, 4895–4911, 10.5194/hess-20-4895-2016, 2016.De Lannoy, G. J., Koster, R. D., Reichle, R. H., Mahanama, S. P., and Liu, Q.: An updated treatment of soil texture and associated hydraulic properties in a global land modeling system, J. Adv. Model. Earth Sy., 6, 957–979, 10.1002/2014MS000330, 2014.De Lannoy, G. J. M., de Rosnay, P., and Reichle, R. H.: Soil Moisture Data
Assimilation, in: Handbook of Hydrometeorological Ensemble Forecasting, edited
by: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., and Schaake, J. C., Springer Verlag, New York, USA, 43 pp., 10.1007/978-3-642-40457-3_32-1, 2015.de Roos, S., De Lannoy, G., and Raes, D.: source code and datasets for gmd-2021-98, Version 1, Zenodo [data set], 10.5281/zenodo.4770738, 2021.De Wit, A. D. and Van Diepen, C. A.: Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts, Agr. Forest Meteorol., 146, 38–56, 10.1016/j.agrformet.2007.05.004, 2007.Di Paola, A., Valentini, R., and Santini, M.: An overview of available crop growth and yield models for studies and assessments in agriculture, J. Sci. Food Agr., 96, 709–714, 10.1002/jsfa.7359, 2016.
Dirmeyer, P. and Oki, T.: The Second Global Soil Wetness project (GSWP-2)
Science 2 and Implementation Plan, International GEWEX Project Office Publication (IGPO), Columbia, Md, IGPO Publication Series No. 37, 64 pp.,
2002.Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, 10.5194/hess-15-1675-2011, 2011.Elliott, J., Müller, C., Deryng, D., Chryssanthacopoulos, J., Boote, K. J., Büchner, M., Foster, I., Glotter, M., Heinke, J., Iizumi, T., Izaurralde, R. C., Mueller, N. D., Ray, D. K., Rosenzweig, C., Ruane, A. C., and Sheffield, J.: The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0), Geosci. Model Dev., 8, 261–277, 10.5194/gmd-8-261-2015, 2015.
Entekhabi, D., Yueh, S., O'Neill, P., Kellogg, K. H., Allen, A., Bindlish, R., Brown, M., Chan, S., Colliander, A., Crow, W. T, Das, N., De Lannoy, G., Dunbar, R. S., Edelstein, W. N., Entin, J. K., Escobar, V., Goodman, S. D., Jackson, T. J., Jai, B., Johnson, J., Kim, E., Kim, S., Kimball, J., Koster, R. D., Leon, A., McDonald, K. C., Moghaddam, M., Mohammed, P., Moran, S., Njoku, E. G., Piepmeier, J. R., Reichle, R., Rogez, F., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., Van Zyl, J., Weiss, B., and West, R.: SMAP Handbook–soil moisture active passive: Mapping soil moisture and freeze/thaw from space, JPL publication, Pasadena, California USA, 192 pp., JPL 400-1567, 2014.
FAO: The future of food and agriculture–Trends and challenges, Annual Report, FAO, Rome, Italy, ISBN 978-92-5-109551-5, 2017.FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database v 1.2, Food and Agricultural Organization [data set], Rome, available at: http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last accessed: 30 August 2019), 2012.
Feddes, R. A.: Simulation of field water use and crop yield, in: Simulation
of plant growth and crop production, edited by: Penning de Vries, F. W. T. and van Laar H. H., Pudoc, Wageningen, the Netherlands, 194–209, ISBN 9789022008096, 1982.Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O'Connell, C., Ray, D. K., West, P. C., Balzer, C., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D., and Zaks, D. P. M.: Solutions for a cultivated planet, Nature, 478, 337–342, 10.1038/nature10452, 2011.Folberth, C., Elliott, J., Müller, C., Balkovič, J., Chryssanthacopoulos,
J., Izaurralde, R. C., Jones, C. D., Khabarov, N., Liu, W., Reddy, A., Schmid, E., Skalský, R., Yang, H., Arneth, A., Ciais, P., Deryng, D., Lawrence P. J., Olin, S., Pugh, T. A. M., Ruance, A.C., and Wang, X.: Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble, PLoS ONE, 14, e0221862, 10.1371/journal.pone.0221862, 2019.Geerts, S., Raes, D., Garcia, M., Miranda, R., Cusicanqui, J. A., Taboada, C., Mendoza, J., Huanca, R., Mamani, A., Condori, O., and Mamani, J.: Simulating yield response of quinoa to water availability with AquaCrop, Agron. J., 101, 499–508, 10.2134/agronj2008.0137s, 2009.Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, 10.1175/JCLI-D-16-0758.1, 2017.Gonzalez-Zamora, A., Sanchez, N., Pablos, M., and Martinez-Fernandez, J.: CCI soil moisture assessment with smos soil moisture and in situ data under different environmental conditions and spatial scales in Spain, Remote Sens. Environ., 225, 469–482, 10.1016/j.rse.2018.02.010, 2018.GEWEX/CEOS/GCOS-TOPC/GEO/GTN-H: International Soil Moisture Network, GEWEX/CEOS/GCOS-TOPC/GEO/GTN-H [data sets], available at: https://ismn.geo.tuwien.ac.at/en/ (last access: 29 June 2020), 2011.Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, 10.5067/VJAFPLI1CSIV (data available at: https://disc.gsfc.nasa.gov/datasets?project=MERRA-2, last access: 24 May 2019), 2015a.Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_lnd_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, 10.5067/RKPHT8KC1Y1T, 2015b (data available at: https://disc.gsfc.nasa.gov/datasets?project=MERRA-2, last access: 24 May 2019).Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet, J.-C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi, M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C., Muñoz-Sabater, J., Peng, J., Reichle, R. M., Richaume, P., Rüdiger C., Scanlon T., van der Schalie R., Wigneron J.-P., and Wagner, W.: Validation practices for satellite soil moisture retrievals: What are (the) errors?, Remote Sens. Environ., 244, 111806, 10.1016/j.rse.2020.111806, 2020.Han, C., Zhang, B., Chen, H., Liu, Y., and Wei, Z.: Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data, Agr. Water Manage., 240, 106288, 10.1016/j.agwat.2020.106288, 2020.Hsiao, T. C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., and Fereres, E.: AquaCrop – The FAO Crop Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize, Agron. J., 101, 448–59, 10.2134/agronj2008.0218s, 2009.Huang, J., Scherer, L., Lan, K., Chen, F., and Thorp, K. R.: Advancing the application of a model-independent open-source geospatial tool for national-scale spatiotemporal simulations, Environ. Modell. Softw., 119, 374–378, 10.1016/j.envsoft.2019.07.003, 2019.Iizumi, T., Shin, Y., Kim, W., Kim, M., and Choi, J.: Global crop yield forecasting using seasonal climate information from a multi-model ensemble, Climate Services, 11, 13–23, 10.1016/j.cliser.2018.06.003, 2018.Jensen, K. H. and Refsgaard, J. C.: HOBE: The Danish hydrological observatory. Vadose Zone J., 17, 1–24, 10.2136/vzj2018.03.0059, 2018.Kopittke, P. M., Menzies, N. W., Wang, P., McKenna, B. A., and Lombi, E.: Soil and the intensification of agriculture for global food security, Environ. Int., 132, 105078, 10.1016/j.envint.2019.105078, 2019.Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., and Puma, M. J.: On the nature of soil moisture in land surface models, J. Climate, 22, 4322–4335, 10.1175/2009JCLI2832.1, 2009.Li, B., Rodell, M., Kumar, S., Beaudoing, H. K., Getirana, A., Zaitchik, B. F., de Goncalves, L.G., Cossetin, C., Bhanja, S., Mukherjee, A. and Tian, S., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I. B., Daira, D., Bila, M., De Lannoy, G., Mocko, D., Steele-Dunne, S. C., Save, H., and Bettadpur, S.: Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges, Water Resour. Res., 55, 7564–7586, 10.1029/2018WR024618, 2019.Liu, J., Williams, J. R., Zehnder, A. J., and Yang, H.: GEPIC–modelling wheat yield and crop water productivity with high resolution on a global scale, Agr. Syst., 94, 478–493, 10.1016/j.agsy.2006.11.019, 2007.Lorite, I. J., García-Vila, M., Santos, C., Ruiz-Ramos, M., and Fereres, E.: AquaData and AquaGIS: two computer utilities for temporal and spatial simulations of water-limited yield with AquaCrop, Comput. Electron. Agr., 96, 227–237, 10.1016/j.compag.2013.05.010, 2013.
Mahanama, S. P., Koster, R. D., Walker, G. K., Tackacs, L., Reichle, R. H., De
Lannoy, G., Liu, Q., Zhao, B., and Suarez, M.: Land Boundary Conditions for
the Goddard Earth Observing System Model Version 5 (GEOS-5) Climate Modeling
System – Recent Updates and Data File Descriptions, NASA Technical Report
Series on Global Modeling and Data Assimilation 104606, Vol. 39, NASA Goddard Space Flight Center, MD, USA, 51 pp., 2015.Maniruzzaman, M., Talukder, M. S. U., Khan, M. H., Biswas, J. C., and Nemes, A.: Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh, Agr. Water Manage., 159, 331–340, 10.1016/j.agwat.2015.06.022, 2015.Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, 10.5194/gmd-10-1903-2017, 2017.Mladenova, I. E., Bolten, J. D., Crow, W. T., Sazib, N., Cosh, M. H.,
Tucker, C. J., and Reynolds, C.: Evaluating the operational application of
SMAP for global agricultural drought monitoring. IEEE J. Sel. Top. Appl., 12, 3387–3397, 10.1109/JSTARS.2019.2923555, 2019.Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cy., 22, 10.1029/2007GB002947, 2008.Monteith, J. L.: Solar radiation and productivity in tropical ecosystems, J. Appl. Ecol., 9, 747–766, 10.2307/2401901, 1972.Müller, C., Elliott, J., Chryssanthacopoulos, J., Arneth, A., Balkovic, J., Ciais, P., Deryng, D., Folberth, C., Glotter, M., Hoek, S., Iizumi, T., Izaurralde, R. C., Jones, C., Khabarov, N., Lawrence, P., Liu, W., Olin, S., Pugh, T. A. M., Ray, D. K., Reddy, A., Rosenzweig, C., Ruane, A. C., Sakurai, G., Schmid, E., Skalsky, R., Song, C. X., Wang, X., de Wit, A., and Yang, H.: Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403–1422, 10.5194/gmd-10-1403-2017, 2017.Nichols, J., Kang, S., Post, W., Wang, D., Bandaru, V., Manowitz, D., Zhang, X., and Izaurralde, R.: HPC-EPIC for high resolution simulations of environmental and sustainability assessment, Comput. Electron. Agr., 79, 112–115, 10.1016/j.compag.2011.08.012, 2011.
O'Neill, P., Bindlish, R., Chan, S., Njoku, E., and Jackson, T.: Algorithm Theoretical Basis Document. Level 2 & 3 Soil Moisture (Passive) Data Products, NASA Jet Propulsion Laboratory, California, USA, 2018.O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and Chaubell, J.: SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture, Version 4, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], Boulder, Colorado, USA, 10.5067/Q8J8E3A89923, 2020 (data available at: https://nsidc.org/data/SPL2SMP_E/versions/4, last access: 14 November 2020).Pingali, P. L.: Green revolution: impacts, limits, and the path ahead, P. Natl. Acad. Sci. USA, 109, 12302–12308, 10.1073/pnas.0912953109, 2012.Raes, D. and Vanuytrecht, E.: Food production and water: constraints and solutions for the future, Meded. Zitt. K. Acad. Overzeese Wet., 63, 265–288, 10.5281/zenodo.3894493, 2017.Raes, D., Geerts, S., Kipkorir, E., Wellens, J., and Sahli, A: Simulation of yield decline as a result of water stress with a robust soil water balance model, Agr. Water Manage., 81, 335–357, 10.1016/j.agwat.2005.04.006, 2006.Raes, D., Steduto, P., Hsiao, T. C., and Fereres, E.: AquaCrop –
the FAO crop model to simulate yield response to water: II. Main algorithms and software description, Agron. J., 101, 438–447, 10.2134/agronj2008.0140s, 2009.
Raes, D., Steduto, P., Hsiao, T. C., and Fereres, E.: AquaCrop version 6.0-6.1 – Chapt. 3: calculation procedures, FAO of the UN, Rome, Italy, 25 pp., 2018.Razzaghi, F., Zhou, Z., Andersen, M. N., and Plauborg, F.: Simulation of potato yield in temperate condition by the AquaCrop model, Agr. Water Manage., 191, 113–12, 10.1016/j.agwat.2017.06.008, 2017.Reichle, R. H. and Koster, R. D.: Bias reduction in short records of satellite
soil moisture, Geophys. Res. Lett., 31, L19501, 10.1029/2004GL020938,
2004.Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J.,
Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M.,
Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., and Walker, J. P.: Version 4 of the SMAP Level-4 Soil Moisture algorithm and data product, J. Adv. Model. Earth Sy., 11, 3106–3130, 10.1029/2019MS001729, 2019.Resop, J. P., Fleisher, D. H., Wang, Q., Timlin, D. J., and Reddy, V. R.: Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units, Comput. Electron. Agr., 89, 51–61, 10.1016/j.compag.2012.08.001, 2012.Ritchie, J. T.: Model for predicting evaporation from a row crop with incomplete cover, Water Resour. Res., 8, 1204–1213, 10.1029/WR008i005p01204, 1972.Roerink, G. J., Bojanowski, J. S., De Wit, A. J. W., Eerens, H., Supit, I., Leo, O., and Boogaard, H. L.: Evaluation of MSG-derived global radiation estimates for application in a regional crop model, Agr. Forest Meteorol., 160, 36–47, 10.1016/j.agrformet.2012.02.006, 2012.Sallah, A. H. M., Tychon, B., Piccard, I., Gobin, A., Van Hoolst, R., Djaby, B., and Wellens, J.: Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data, Agr. Water Manage., 217, 346–355, 10.1016/j.agwat.2019.03.016, 2019.Sandhu, R. and Irmak, S.: Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation, Agr. Water Manage., 223, 105687, 10.1016/j.agwat.2019.105687, 2019.Shangguan, W., Hengl, T., de Jesus, J. M., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling. J. Adv. Model. Earth Sy., 9, 65–88, 10.1002/2016MS000686, 2017.Siebert, S., Henrich, V., Frenken, K., and Burke, J.: Global Map of Irrigation Areas version 5, Rheinische Friedrich-Wilhelms-University, Bonn, Germany/Food and Agriculture Organization of the United Nations, Rome, Italy, [data set], available at https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/, (last access: 8 June 2020), 2013.
Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N., and Scanlon, B. R.: A global data set of the extent of irrigated land from 1900 to 2005, Hydrol. Earth Syst. Sci., 19, 1521–1545, 10.5194/hess-19-1521-2015, 2015.
Smets B., Swinnen E. and Van Hoolst R.: Copernicus Global Land Operations “Vegetation and Energy” “CGLOPS-1” – product user manual: Dry Matter Productivity(DMP) – Gross Dry Matter Productivity (GDMP) – Collection 1 km – Version 2, CGLOPS-1 consortium, Brussels, Belgium, 47 pp., I322, 2019.Steduto, P., Hsiao, T. C., Raes, D., and Fereres, E.: AquaCrop – The FAO crop model to simulate yield response to water: I. Concepts and underlying principles, Agron. J., 101, 426–437, 10.2134/agronj2008.0139s, 2009.Still, C. J., Berry, J. A., Collatz, G. J., and DeFries, R. S.: Global distribution of C3 and C4 vegetation: carbon cycle implications, Global Biogeochem. Cy., 17, 6–1, 10.1029/2001GB001807, 2003.Stöckle, C. O., Kemanian, A. R., Nelson, R. L., Adam, J. C., Sommer, R., and Carlson, B.: CropSyst model evolution: From field to regional to global scales and from research to decision support systems, Environ. Modell. Softw., 62, 361–369, 10.1016/j.envsoft.2014.09.006, 2014.
USDA: Estimation of direct runoff from storm rainfall, Section 4 Hydrology,
Chapter 4, in: National Engineering Handbook, USDA, Washington DC, USA, 1-241964, 1964.Wagner, W., Lemoine, G., and Rott, H.: A method for estimating soil moisture
from ERS scatterometer and soil data, P. SPIE, 70, 191–207,
10.1016/S0034-4257(99)00036-X, 1999.Zacharias, S., Bogena, H., Samaniego, L., Mauder, M., Fuß, R., Pütz, T., Frenzel, M., Schwank, M., Baessler, C., Butterbach-Bahl, K., Bens, O., Borg, E., Brauer, A., Dietrich, P., Hajnsek, I., Helle, G., Kiese, R., Kunstmann, H., Klotz, S., Munch, J. C., Papen, H., Priesack, E., Schmid, H. P., Steinbrecher, R., Rosenbaum, U., Teutsch, G., and Vereecken, H.: A Network of Terrestrial Environmental Observatories in Germany, Vadose Zone J., 10, 955–973, 10.2136/vzj2010.0139, 2011.Zhuo, W., Huang, J., Li, L., Zhang, X., Ma, H., Gao, X., Huang H, Xu, B., and Xiao, X.: Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation, Remote Sens.-Basel, 11, 1618, 10.3390/rs11131618, 2019.