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Proceedings ArticleDOI

Assimilating Remote Sensing-Based ET into SWAP Model for Improved Estimation of Hydrological Predictions

07 Jul 2008-Vol. 3, pp 1036-1039

TL;DR: The agro-hydrological model driven by the observed ET produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating ET measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilates regional ET estimated from satellite-based measurements.
Abstract: An agro-hydrological simulation model is useful for agriculture monitoring and Remote Sensing provides useful information over large area. Combining both information by data assimilation is used in agro-hydrological modeling and predictions, where multiple remotely sensed data, ground measurement data and model forecast routinely combined in operational mapping procedures. Remote sensing cannot observe input parameters of agro-hydrological models directly. A method to estimate input parameters of such model from Remote Sensing using data assimilation has been proposed by Ines [2002] using the SWAP (Soil, Water, Atmosphere and Plant) model. A Genetic Algorithm (GA) loaded stochastic physically based soil-water-atmosphere-plant model (SWAP) was extended for the discussed problem and used in the study. The objective of this study was to implement a data assimilation scheme to estimate hydrological parameters (e.g soil moisture) of SWAP model. For this study six Landsat TM/ETM satellite images were obtained for part of the Great Plains (Path 29, Row 32) in the states of Nebraska (NE) for the 2006 growing season (May-October). Then a land surface energy balance model (METRIC) was used to map spatiotemporal distribution of evapotranspiration. The ability of METRIC accuracy was compared with the measurements at several flux sites with Bowen Ratio Energy Balance System units. Remotely sensed ET data and ground measurement data from experiment fields were then combined in a data assimilation to estimate parameters of the SWAP model. The system is initialized with a population of random solutions and searches for optima by updating generations. The result shows that the reasonable parameters (sowing date and harvesting date, Ground water level) were successfully estimated. On the basis of estimated parameters, soil moisture is predicted by SWAP model. The agro-hydrological model driven by the observed ET produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating ET measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating regional ET estimated from satellite-based measurements.
Topics: Data assimilation (54%), Data modeling (52%), Population (52%), Atmospheric model (50%)

Summary (2 min read)

I. INTRODUCTION

  • Ccurate estimation of evapotranspiration (ET) plays an important role in quantification of the water balance at the pixel, watershed, basin and regional scale for better planning and managing water resources (Irmak et al., 2008a) .
  • Furthermore, quantification of ET at multiple scales is spatially restricted because in-situ observations provide only point measurements.
  • ET estimated from remote sensing observations can be used to calibrate hydrology model or estimate uncertain model parameters in the model via data assimilation.
  • Similar works by Ines [2003] , Srinuandee [2005] , Chemin [2005] , Kamble[2006] , Kulkarni [2006] , Thapa [2006] used remotely sensed information combined with a binary GA and SWAP model for optimizing soil hydraulic parameters.
  • Additionally, the authors show here that such ET estimates may be used together with on-farm measurements of applied irrigation water to provide reliable estimates of soil moisture.

II. STUDY AREA

  • The study was conducted at the South-central Nebraska.
  • The long-term average rainfall in south central part of the state is about 680 mm although the annual total rainfall shows significant variations from year to year (e.g., 420 mm in 1988 to 1,040 mm in 1993).
  • The dominant cropping system in south central Nebraska is corn-soybean rotation with increasing continuous corn production as the demand for ethanol production has been increasing.
  • Most of the croplands in the region are irrigated with center pivots with the ground water pumped from the Ogallala aquifer being the dominant water supply for irrigation.

B. SWAP-GA Model Framework

  • Genetic algorithm used in this research comprises of three components that are Remote sensing, SWAP model and Genetic algorithm.
  • This process is termed as SWAP-GA method.
  • The authors used SWAP-GA to estimate starting date of cropping, irrigation scheduling start time, time extent of cropping and the groundwater depths.
  • The newly proposed parameters were fed to SWAP by GA according to the evaluation of the difference processes between SWAP output ETa values and the target ETa values.

III -1037

  • It is evident from the numerical figures in table that some crops are still developing on May and others are transpiring at higher rates.
  • On June23, all the crops in the area are established.
  • This indicates the variability of sowing dates and water management practices as influenced by water availability.

B. Remotely sensed Evapotranspiration Data Assimilation

  • In simulations, hydraulic properties were based on measured values where possible; some values were altered slightly by optimizing the model to the local conditions until good agreement with measured ET was attained.
  • Given these constraints, it was not possible to achieve perfect agreement with measurements for the wide range of ET conditions that occurred during the study.
  • ET data from METRIC model were used as the "observed" RS data for the investigated pixel.
  • Above results showed good fitness between the observed and simulated ET.
  • Overall, the ET data assimilation results indicated that SWAP-GA performs well for the advective conditions of the study area with prediction errors of 10-20%.

C. Optimization of crop growth parameters from Data Assimilation

  • The goal of the calibration process is to find optimal sets of configuration parameters for SWAP models.
  • Optimal configuration parameters are determined by comparing the RMSE of the derived parameters, the convergence, the amount of a priori information used.
  • The RMSE and Square of correlation coefficient then calculated from the observed and optimized values (Table 2 ).
  • Good agreement was found between the optimized and observed ET.
  • RMSE and r2 improvements occurred with the observed data when generation and population increased from 10 Generation 10 Population to 100 Generation and 100 Population.

D. Soil Moisture Estimation from Data Assimilation

  • Soil-based measurements may be a far more practical and easy method for corn growers to use to schedule irrigations and assess current irrigation practices.
  • At the start of the season the soil is moist from winter and spring rains; the readings are less than 0.3 cm 3 /cm 3 .
  • Gradually the soil dries and the readings increase, beginning with the simulation at 0.5, 9.5, 27.5, 52.5 cm.
  • The drying cycle resumed until a partial irrigation occurred in early May.
  • The reason for the partial irrigation was that it was needed to replenish enough soil moisture to sow the crop through the germination process without excessive soil moisture depletion and crop stress.

III -1038

  • This study used remote sensing data to characterize system via a GA based hydrological data assimilation approach in Great Plain environment, and then the derived data were used as inputs to their water management optimization model.
  • Reasonable parameters were successfully estimated and the ETa output from SWAP model matched with the actual ETa reasonably well.
  • Soil moisture in the unsaturated zone in study area responded strongly to rainfall events because of the shallow water table in the great plain and additional net inputs from lateral saturated subsurface flows.
  • If this evaluation is computationally expensive, the forward modeling approach can become impractical.
  • These conclusions suggest that it is indeed necessary to couple a remotely sensed ET with a pixel-based hydrological model in order to study and explore the water management options.

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University of Nebraska - Lincoln University of Nebraska - Lincoln
DigitalCommons@University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln
Civil and Environmental Engineering Faculty
Publications
Civil and Environmental Engineering
2008
Assimilating Remote Sensing-Based ET into SWAP Model for Assimilating Remote Sensing-Based ET into SWAP Model for
Improved Estimation of Hydrological Predictions Improved Estimation of Hydrological Predictions
Baburao Kamble
University of Nebraska-Lincoln
, bkamble3@unl.edu
Ayse Kilic
University of Nebraska-Lincoln
, akilic@unl.edu
Follow this and additional works at: https://digitalcommons.unl.edu/civilengfacpub
Part of the Civil Engineering Commons
Kamble, Baburao and Kilic, Ayse, "Assimilating Remote Sensing-Based ET into SWAP Model for Improved
Estimation of Hydrological Predictions" (2008).
Civil and Environmental Engineering Faculty Publications
.
38.
https://digitalcommons.unl.edu/civilengfacpub/38
This Article is brought to you for free and open access by the Civil and Environmental Engineering at
DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Civil and Environmental
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Lincoln.

Assimilating Remote Sensing-Based ET into SWAP Model for
Improved Estimation of Hydrological Predictions
Baburao Kamble
1, 2
, Ayse Irmak
1
1
Department of Civil Engineering, University of Nebraska-Lincoln, Lincoln, NE 68503, USA
2
Member IEEE
An agro-hydrological simulation model is useful for agriculture monitoring and Remote Sensing provides useful information over
large area. Combining both information by data assimilation is used in agro-hydrological modeling and predictions, where multiple
remotely sensed data, ground measurement data and model forecast routinely combined in operational mapping procedures. Remote
sensing cannot observe input parameters of agro-hydrological models directly. A method to estimate input parameters of such model
from Remote Sensing using data assimilation has been proposed by Ines
[2002] using the SWAP (Soil, Water, Atmosphere and Plant)
model. A Genetic Algorithm (GA) loaded stochastic physically based soil-water-atmosphere-plant model (SWAP) was extended for the
discussed problem and used in the study. The objective of this study was to implement a data assimilation scheme to estimate
hydrological parameters (e.g soil moisture) of SWAP model. For this study six Landsat TM/ETM satellite images were obtained for
part of the Great Plains (Path 29, Row 32) in the states of Nebraska (NE) for the 2006 growing season (May -October). Then a land
surface energy balance model (METRIC) was used to map spatiotemporal distribution of evapotranspiration. The ability of METRIC
accuracy was compared with the measurements at several flux sites with Bowen Ratio Energy Balance System units. Remotely sensed
ET data and ground measurement data from experiment fields were then combined in a data assimilation to estimate parameters of
the SWAP model. The system is initialized with a population of random solutions and searches for optima by updating generations.
The result shows that the reasonable parameters (sowing date and harvesting date, Ground water level) were successfully estimated.
On the basis of estimated parameters, soil moisture is predicted by SWAP model. The agro-hydrological model driven by the observed
ET produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating ET measurements
that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating
regional ET estimated from satellite-based measurements.
Keywords: Genetic algorithm, Data Assimilation, Remote Sensing, METRIC, Evapotranspiration, Hydrological Modeling, SWAP
I. INTRODUCTION
ccurate estimation of evapotranspiration (ET) plays an
important role in quantification of the water balance at the
pixel, watershed, basin and regional scale for better
planning and managing water resources (Irmak et al., 2008a).
Unfortunately, ET estimation under actual field conditions is
still a very challenging task for scientists and water managers
(Kamble et al, 2007). Furthermore, quantification of ET at
multiple scales is spatially restricted because in-situ
observations provide only point measurements. Techniques
such as bowen ratio energy balance system and eddy
correlation measure ET on a field scale. These systems may
not be practical when quantifying water use at watershed or
larger scale. Remote sensing techniques have emerged as a
very useful tool to provide such information at various
temporal and spatial scales (Courault et al. 2003). ET
estimated from remote sensing observations can be used to
calibrate hydrology model or estimate uncertain model
parameters in the model via data assimilation.
With advances in remote sensing in recent years, there has
been an increasing attention on estimating uncertain model
parameters from remote sensing observations via data
assimilation. Benard et al. [1981] demonstrated that
evaporation could be modeled very accurately with the
contribution of surface moisture measurements every 3 days.
Prevot et al. [1984] continued this work and showed that the
soil water balance could be determined with equal accuracy
using remotely sensed surface soil moisture estimates
substituted for in situ observations. Ines and Honda [2002]
developed an assimilation methodology of the SWAP (Soil,
Water, Atmosphere, Plant) crop model with RS data using
Genetic Algorithm (GA). Similar works by Ines [2003],
Srinuandee [2005], Chemin [2005], Kamble[2006], Kulkarni
[2006], Thapa [2006] used remotely sensed information
combined with a binary GA and SWAP model for
optimizing soil hydraulic parameters. Furthermore, Kamble
[2006] implemented SWAP-GA model (Modified SWAP-GA)
with a new methodology to assimilate RS evapotranspiration
(ETa) data for satellite images by MODIS for Sirsa Irrigation
Circle-Haryana India.
In this study, METRIC model was first used to map
spatiotemporal distribution of ET in Nebraska. We have then
combined METRIC-derived ET with a SWAP model Genetic
Algorithm to (1) update and correct SWAP ET estimations at
the field level and (2) assess its impact on scheme water use.
Additionally, we show here that such ET estimates may be
used together with on-farm measurements of applied irrigation
water to provide reliable estimates of soil moisture.
II. S
TUDY AREA
The study was conducted at the South-central Nebraska.
The long-term average rainfall in south central part of the state
is about 680 mm although the annual total rainfall shows
significant variations from year to year (e.g., 420 mm in 1988
to 1,040 mm in 1993). The dominant cropping system in south
central Nebraska is corn-soybean rotation with increasing
continuous corn production as the demand for ethanol
production has been increasing. Most of the croplands in the
region are irrigated with center pivots with the ground water
pumped from the Ogallala aquifer being the dominant water
supply for irrigation. The weather data used in this study were
A
III - 1036978-1-4244-2808-3/08/$25.00 ©2008 IEEE IGARSS 2008
Published in IGARSS 2008. IEEE International Geoscience and Remote Sensing Symposium, 2008. Vol. 3, pp. 1036–1039;
doi: 10.1109/IGARSS.2008.4779530 Copyright 2008 IEEE. Used by permission.

measured with an automated weather station operated by the
High Plains Regional Climate Center
(http://www.hprcc.unl.edu).
III.
METHODOLOGY
A. Evapotranspiration monitoring with METRIC
The landsat TM/ETM satellite images were obtained for
part of the Great Plains in the states of Nebraska (Path 29,
Row 32) for the 2006 growing season (May -October). The
hourly in situ meteorological observational data were acquired
from South Central Agricultural Laboratory (SCAL) of the
University of Nebraska-Lincoln located near Clay Center, NE.
A total of 6 cloud free images from May through October
were processed to calculate ET. The hourly in situ energy flux
observational data were acquired from South Central
Agricultural Laboratory (SCAL) research farm of University
of Nebraska located at Clay Center. The energy flux data were
measured using Bowen Ratio Energy Balance System (Irmak
et al., 2006) and were used for METRIC model.
B. SWAP-GA Model Framework
Genetic algorithm used in this research comprises of three
components that are Remote sensing, SWAP model and
Genetic algorithm. Ines [2002] has proposed a data
assimilation scheme using GA as an optimizer. This process is
termed as SWAP-GA method. We used SWAP-GA to
estimate starting date of cropping, irrigation scheduling start
time, time extent of cropping and the groundwater depths. The
newly proposed parameters were fed to SWAP by GA
according to the evaluation of the difference processes
between SWAP output ETa values and the target ETa values.
Consider C the cost function, having (x,y,d) parameters,
x the longitude [0-180/E-W], y the latitude [0-90/N-S], d the
date [yyyymmdd] .With d = [i,...,j], with i to j being the
different satellite overpass dates, n is the sum of i to j.
|ETaETa|
n
=C
j
i
xyd
SWAPxydxy
¦
1
mm (1)
Where
xyd
ETa
is the measured ET from RS at time t; n the
time domain; C
xy
is the objective function. The environment
pressure is the SWAP model ETa output that has to match the
remote sensing ETa target. When a minimum-difference
defined threshold will be reached, SWAP parameters will be
stored for reconstruction of high spatial ETa for any required
day in the cropping season.
The fitness of an individual having xy pixel location
characteristics is the inverse of the cost function times the
constraints aiming at minimizing the differences between
SWAP simulation and target ETa.

)Constraint0.1(*
1
xy
xy
C
F
(2)
The constraint is function of date of emergence of first crop
and the date of emergence of second crop.
40121365Constraint DECdoyi
(3)
Subject to: Possible range of sowing dates:
j
j
bsdb
maxmin
dd
(j=1,…,6) (4)
IV. R
ESULTS AND DISCUSSION
A. Evapotranspiration monitoring with METRIC
Figure 2 shows ET map corresponding to the 2006 season
for the entire Clay, York, Hamilton Adams and Fillmore
counties in NE. The study site SCAL is located in Clay county
at latitude 40
o
34’, longitude 98
o
08’. ET map resolution is
30X30m and the range is 400 mm/season (bare soil) to
950mm/season (irrigated crops). Seasonal ET varied from
950 mm for well-irrigated fields to 400 mm for non-
agricultural areas. Rain fed areas surrounding the Fillmore (in
the south east) had ET values around 400 mm which depicted
the bare fields and fallow lands, the ET over Adams county
shows the mixed ET in between 400mm to 650mm, while ET
values are for the SCAL fields located towards the south in
York and Hamilton county due to shallow water table, lateral
seepage from the SCAL fields and a open network of
irrigation canals. The ET map further shows a spatial gradient
of increasing ET from the Southern parts towards the Northern
parts of the irrigation system except low ET in the Howard
due to settlements. All of these ET values are important for the
agro-hydrological balance of the area as well as ground water
modeling.
Fig. 1. Study area: South Central Agricultural Laboratory near Clay Center,
N
ebraska
Fig. 2. Spatial distribution of remotely sensed Evapotranspiration fro
m
Landsat ETM+ for the 2006
III - 1037

According to table 1, average daily ET (ET24) was
0.426 c
m
d
-1
with a mode and maximum values of 0.75 and 0.71 cm d
-1
,
respectively, for the study field. It is evident from the
numerical figures in table that some crops are still developing
on May and others are transpiring at higher rates. On June23,
all the crops in the area are established. This indicates the
variability of sowing dates and water management practices as
influenced by water availability.
B. Remotely sensed Evapotranspiration Data Assimilation
In simulations, hydraulic properties were based on
measured values where possible; some values were altered
slightly by optimizing the model to the local conditions until
good agreement with measured ET was attained. Given these
constraints, it was not possible to achieve perfect agreement
with measurements for the wide range of ET conditions that
occurred during the study. The objective of assimilation is to
obtain the best estimate of the state of the system by
combining observations with the forecast model first guess.
ET data from METRIC model were used as the “observed” RS
data for the investigated pixel. Above results showed good
fitness between the observed and simulated ET. As expected,
there is bias due to the comparison of point observation with
model. Some of this bias could be attributed to uncertainty in
SWAP model parameters.
Overall, the ET data assimilation results (figure 3) indicated
that SWAP-GA performs well for the advective conditions of
the study area with prediction errors of 10-20%. Some errors
in the evaluation may have been introduced by the hydraulic
parameters. According to Wright and Jensen (1978), a
common standard error for ET prediction equations based on
weather data using Penman or Penman-Monteith type
equations is as much as 10% of daily estimates.
C. Optimization of crop growth parameters from Data
Assimilation
The goal of the calibration process is to find optimal sets of
configuration parameters for SWAP models. Optimal
configuration parameters are determined by comparing the
RMSE of the derived parameters, the convergence, the amount
of a priori information used. The RMSE and Square of
correlation coefficient then calculated from the observed and
optimized values (Table 2). Good agreement was found
between the optimized and observed ET. RMSE and r2
improvements occurred with the observed data when
generation and population increased from 10 Generation 10
Population to 100 Generation and 100 Population.
D. Soil Moisture Estimation from Data Assimilation
Soil-based measurements may be a far more practical and easy
method for corn growers to use to schedule irrigations and
assess current irrigation practices. Figure 6 shows soil
moisture content in cm
3
/cm
3
from January-2006 to December
2006 obtained from SWAP-GA data assimilation for effective
irrigation management and illustrates how readings typically
fluctuate from spring through corn harvesting. At the start of
the season the soil is moist from winter and spring rains; the
readings are less than 0.3 cm
3
/cm
3
. Gradually the soil dries
and the readings increase, beginning with the simulation at
0.5, 9.5, 27.5, 52.5 cm. The uppermost 0.5 and 9.5 reading
normally climbed first, as there was greater root activity in the
upper portion of the soil profile than at deeper depths.
Furthermore, moisture readings at the 27.5 and 52.5 cm depth
were typically lower (more soil moisture) and fluctuated far
less than the shallow depths. When the soil moisture content
dropped to near 80 in late March, rainfall started and the soil
moisture readings at all four depths went to above 0.3,
indicating the soil profile had been refilled. The drying cycle
resumed until a partial irrigation occurred in early May. The
reason for the partial irrigation was that it was needed to
replenish enough soil moisture to sow the crop through the
germination process without excessive soil moisture depletion
and crop stress.
Fig. 3. Results of Actual Evapotranspiration Simulated (ETa from SWAP)
and Observed (ETa from Remote Sensing) in SWAPGA model
Date ET
22/05/2006 0.095
23/06/2006 0.58
17/07/2006 0.75
25/07/2006 0.71
19/09/2006 0.24
13/10/2006 0.18
Table 1: Remotely sensed Evapotranspiration to satellite overpass dates
(r
2
) RMSE (mm)
10Gen10Pop 0.86 7.28
50Gen50Pop 0.22 10.808
100Gen100Pop 0.962 3.94
500Gen500Pop 0.96 5.38
Avg Gen Avg Pop 0.97 3.21
Table 2: Remotely sensed Evapotranspiration to satellite overpass dates
Fig. 3. Results of Actual Evapotranspiration Simulated (ETa from SWAP)
and Observed (ETa from Remote Sensing) in SWAPGA model
III - 1038

V. C
ONCLUSION
This study used remote sensing data to characterize system
via a GA based hydrological data assimilation approach in
Great Plain environment, and then the derived data were used
as inputs to our water management optimization model.
Reasonable parameters were successfully estimated and the
ETa output from SWAP model matched with the actual ETa
reasonably well. Although the analyses were limited to the
conditions imposed in the water management optimization
model, some basic but useful findings have been drawn on
how to make use to the best possible way the limited soil
moisture estimation and best possible utilization of maximum
irrigation water. Soil moisture in the unsaturated zone in study
area responded strongly to rainfall events because of the
shallow water table in the great plain and additional net inputs
from lateral saturated subsurface flows. Short drought
episodes also occurred in rainfall events January to march,
even causing short-term water stress in the relatively dry low
areas. In most real applications, the model needs to be
evaluated (i.e., given a parameter set, compute a synthetic
dataset and its associated goodness of fit) a great many times.
If this evaluation is computationally expensive, the forward
modeling approach can become impractical. GA-based
optimization retains the advantageous features of forward
modeling, while reducing the number of required function
evaluations to a level that is often much more computationally
manageable. These conclusions suggest that it is indeed
necessary to couple a remotely sensed ET with a pixel-based
hydrological model in order to study and explore the water
management options.
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Journal ArticleDOI
TL;DR: The procedure for quantifying crop coefficients from NDVI data presented in this paper should be useful in other regions of the globe to understand regional irrigation water consumption.
Abstract: Crop coefficient (Kc)-based estimation of crop evapotranspiration is one of the most commonly used methods for irrigation water management. However, uncertainties of the generalized dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper No. 56 can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. Similarities between the crop coefficient curve and a satellite-derived vegetation index showed potential for modeling a crop coefficient as a function of the vegetation index. Therefore, the possibility of directly estimating the crop coefficient from satellite reflectance of a crop was investigated. The Kc data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop coefficients procedure using field data obtained during 2007 from representative US cropping systems in the High Plains from AmeriFlux sites. A simple linear regression model ( ) is developed to establish a general relationship between a normalized difference vegetation index (NDVI) from a moderate resolution satellite data (MODIS) and the crop coefficient (Kc) calculated from the flux data measured for different crops and cropping practices using AmeriFlux towers. There was a strong linear correlation between the NDVI-estimated Kc and the measured Kc with an r2 of 0.91 and 0.90, while the root-mean-square error (RMSE) for Kc in 2006 and 2007 were 0.16 and 0.19, respectively. The procedure for quantifying crop coefficients from NDVI data presented in this paper should be useful in other regions of the globe to understand regional irrigation water consumption.

149 citations


Cites background or methods from "Assimilating Remote Sensing-Based E..."

  • ...The eight-day composite images are treated as cloud-free products to monitor the Earth’s terrestrial activity [7,20]....

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  • ...Rather, we quantified the , NDVI, their interactions and the actual evapotranspiration values for the natural conditions of the fields, as reflected in the MODIS satellite images [7,20]....

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  • ...The declination extent primarily depends on the particular crop growth characteristics [6,7] and the irrigation management during the late season [1]....

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Journal ArticleDOI
Xiaoyong Xu1, Jonathan Li1, Bryan A. Tolson1Institutions (1)
Abstract: Remote sensing and hydrologic modeling are two key approaches to evaluate and predict hydrology and water resources. Remote sensing technologies, due to their ability to offer large-scale spatially distributed observations, have opened up new opportunities for the development of fully distributed hydrologic and land-surface models. In general, remote sensing data can be applied to land-surface and hydrologic modeling through three strategies: model inputs (basin information, boundary conditions, etc.), parameter estimation (model calibration), and state estimation (data assimilation). There has been an intensive global research effort to integrate remote sensing and land/hydrologic modeling over the past few decades. In particular, in recent years significant progress has been made in land/hydrologic remote sensing data assimilation. Hence there is a demand for an up-to-date review on these efforts. This paper presents an overview of research efforts to combine hydrologic/land models and remote sensing pr...

74 citations


Cites methods from "Assimilating Remote Sensing-Based E..."

  • ...Kamble and Irmak (2008) attempted to assimilate ET, which was derived from Landsat TM/ETM data with a surface energy balance model, into a SWAP model....

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Journal ArticleDOI
21 Nov 2017
Abstract: Monitoring and assessment of hydrological parameters are the key elements for the sustainable development of water resources of any country. The various components of the hydrological cycle also known as hydrological parameters are highly dynamic in space and time. Quantification of hydrological parameters using traditional methods provides limited, point based, information which is not sufficient for assessing spatio-temporal variations in these parameters. Satellite based remote sensing has proven its usefulness in effective mapping/retrieval and monitoring of hydrological parameters such as precipitation, interception, soil moisture, surface runoff, water level and river flow, evapotranspiration, change in terrestrial water storage, etc. This review paper highlights the major work done in India for estimation of hydrological parameters using remote sensing. The basics of retrieval techniques, their applications in India, their validation and limitations are discussed in this paper. The progress of each technique from conventional optical remote sensing based to advance microwave remote sensing based hydrological parameters estimation has been presented. The integration of remote sensing derived hydrological parameters in water balance and land surface model is also presented.

14 citations


DissertationDOI
01 Jan 2014
Abstract: The goal of this study is a contribution to the water resources management through the detailed study of evapotranspiration estimated by calculating the surface energy balance. The surface energy balance is examined in a new way with the use of satellite remote sensing techniques deriving, finally, crop coefficients values (Kc) giving much emphasis to the prevailing local conditions. During this thesis satellite-based energy balance for Mapping EvapoTRanspiration with Internalized Calibration (METRIC) methodology is successfully modified for the region of Thessaly, Greece. At the same time, a new land use map is produced, necessary for the next steps of the described water balance methodologies. Furthermore, field measurements (spectral measurements) for the year 2012 for selected crops at the Lake Karla basin were recorded. Hundreds of surface reflectivity values are filtered and then converted according to the specific wavelengths of Landsat 7 bands. This is achieved by applying the method of interpolation in order to yield values in steps of 1 nm. These values are then filtered according to the relative spectral profiles provided by the satellite manufacturer. Finally, using the Relative Spectral Response (RSR) of Landsat TM/ETM+, and applying interpolation, surface reflectivity values equivalent to channels 1, 2, 3 and 4 of Landsat TM/ETM+ for selected crops of Lake Karla basin, namely cotton, wheat, maize, alfalfa and sugar beet are generated. As a result, vegetation indices can be estimated with high accuracy without the need for atmospheric correction. It is therefore possible now to incorporate accurate values of vegetation indices into METRIC methodology to Lake Karla watershed and finally get separate Kc values for each crop with a spatial resolution of 30 m x 30 m. Kc values can be finally introduced into FAO CROPWAT model computing water needs for each crop. The next step is the application of specialized downscaling techniques, where fine spatial resolution from Landsat imagery, is combined with fine temporal resolution from AQUA/MODIS sensors, resulting to an "artificial" ETa and/or Kc image map with improved spatiotemporal characteristics. This methodology can be applied again for every crop as previously. The reason for doing this procedure is the need for Institutional Repository Library & Information Centre University of Thessaly 17/09/2020 03:20:00 EEST 54.70.40.11 xxiv improvement of Landsat’s temporal resolution which is sixteen (16) days. The processing results show that it is possible to predict a 30 m x 30 m ETa Landsat image map for Lake Karla watershed simply applying a linear model (linear regression) derived from MODIS images with a 250 m x 250 m spatial resolution. The basic assumption of this methodology is that the fine-scale variability inside a MODIS pixel (250 m x 250 m) is assumed to be fixed between the times of the first and the second Landsat image acquisition. After that, an estimation of the required amount of irrigation water requirements using CROPWAT (FAO) model is generated, using specific land use. At last, a new model incorporating all the previous applied methodologies is established. The new model uses field measurements, downscaling techniques between Landsat and MODIS derived NDVI values and linear regression in order to finally produce daily crop coefficient values with 30 m x 30 m spatial resolution. Validation of the proposed methodologies which have been developed and applied at this study is not possible through reliable measurements. This happens both to evapotranspiration and crop water requirement measurements. The reason is the absence of lysimeters and/or a water consumption measurement system respectively. For this reason, an internal valiation is performed instead. Initially, values of actual evapotranspiration produced by METRIC methodology are checked against the theoretical Penman-Monteith methodology. The procedure is performed at the regions close to the available meteorological stations of the study area. Then, the proposed methodology is validated independently utilizing new Landsat 7 and Landsat 8 images not used initially at the proposed methodology, by comparing the predicted values when applying the proposed methodology with the computed values of evapotranspiration based on METRIC methodology. Validation is satisfactory for both cases. Finally, a sensitivity analysis of the assessment of crop irrigation requirements is performed. CROPWAT outputs are checked against the possible error of crop coefficients derived from the proposed methodology. The results are again ecouraging. Institutional Repository Library & Information Centre University of Thessaly 17/09/2020 03:20:00 EEST 54.70.40.11 xxv ABSTRACT IN GREEK ΕΚΤΕΤΑΜΕΝΗ ΠΕΡΙΛΗΨΗ

7 citations


Book ChapterDOI
30 Apr 2013
Abstract: Previous studies across the High Plains and the Arid West of the United States have pro‐ duced widely varying impacts of riparian evapotranspiration (ET) on surface and ground water. Many producers as well as various state agencies have advocated removing all trees along the river basins as a method of riparian control for water reclamation. Although eradi‐ cation of trees might be an effective method for water reclamation in the short-term, it has not been yet proven whether such water savings are possible on a stream level. Mean water use of riparian trees has been reported in relatively few studies, and most of the previous studies have been of short duration. The water use for saltcedar (Tamarix spp.) was estimat‐ ed at 15.9 L d-1 for 10 cm2 sap wood area (swa) (Smith et al. 1998), 56.8 L d-1 for 33 cm2 swa (Nagler et al. 2003), and 29.9 L d-1 for 100 cm2 swa (Owens and Moore, 2007). The water use for Fremont cottonwood (Populus fremontii S. Wats.) varied from 57.6 L d-1 for 33 cm2 swa (Nagler et al. 2003) to as high as 499.7 L d-1 for 833 cm2 swa (Schaeffer et al. 2000). Riparian plant communities are complex ecosystems that, through an intimate relationship with the fluvial dynamics of river systems, are as much described by their continual cycle of disturb‐ ance and succession as by the vegetation that makes up their multi-storied habitats. Current‐ ly, there is uncertainty in the water use of riparian systems due to the narrow and sparse vegetation commonly associated with them. Local, state and federal water management reg‐ ulatory agencies need good quality water use estimates on unmanaged riparian systems. High frequency micrometeorological flux measurements such as Eddy Correlation System (ECS) have been used to estimate water use by balancing fluxes of sensible and latent heat with total energy incident on a riparian area. However, the technique is most effective when

5 citations


Cites methods from "Assimilating Remote Sensing-Based E..."

  • ...An Overview80 able method (Irmak and Kamble, 2009;, Kamble and Irmak, 2011;2008; Irmak et al., 2011)....

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Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

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TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
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Abstract: (First edition: 1998, this reprint: 2004). This publication presents an updated procedure for calculating reference and crop evapotranspiration from meteorological data and crop coefficients. The procedure, first presented in FAO Irrigation and Drainage Paper No. 24, Crop water requirements, in 1977, allows estimation of the amount of water used by a crop, taking into account the effect of the climate and the crop characteristics. The publication incorporates advances in research and more accurate procedures for determining crop water use as recommended by a panel of high-level experts organised by FAO in May 1990. The first part of the guidelines includes procedures for determining reference crop evapotranspiration according to the FAO Penman-Monteith method. These are followed by updated procedures for estimating the evapotranspiration of different crops for different growth stages and ecological conditions.

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Abstract: The major bottlenecks of existing algorithms to estimate the spatially distributed surface energy balance in composite terrain by means of remote sensing data are briefly summarised. The relationship between visible and thermal infrared spectral radiances of areas with a sufficiently large hydrological contrast (dry and wet land surface types, vegetative cover is not essential) constitute the basis for the formulation of the new Surface Energy Balance Algorithm for Land (SEBAL). The new algorithm (i) estimates the spatial variation of most essential hydro-meteorological parameters empirically, (ii) requires only field information on short wave atmospheric transmittance, surface temperature and vegetation height, (iii) does not involve numerical simulation models, (iv) calculates the fluxes independently from land cover and (v) can handle thermal infrared images at resolutions between a few meters to a few kilometers. The empirical relationships are adjusted to different geographical regions and time of image acquisition. Actual satellite data is inserted in the derivation of the regression coefficients. Part 2 deals with the validation of SEBAL. q 1998 Elsevier Science BV. All rights reserved.

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