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LARGE-SCALE ROOT ZONE SOIL MOISTURE ESTIMATION USING DATA-DRIVEN METHODSPan, Xiaojun 11 1900 (has links)
Soil moisture is an important variable in many environmental researches and application areas as it affects the interactions between atmosphere and land surface by controlling the energy and water exchange. The current measurement techniques are insufficient to acquire accurate large-scale root zone soil moisture (RZSM) data at the spatial resolution of interest. Though assorted models have been successfully applied in relatively small areas to estimate RZSM, the large-scale estimation is still facing challenges as it requires the flexibility and practicality of the models for the applications under various conditions. Though physically based soil moisture models are widely used, the errors in model physics affect the flexibility of these models meanwhile their large demand of data and computational resources reduces the practicality. On the contrary, the statistical and data-driven methods have high potential but their applications for large-scale RZSM estimation have not been fully explored. To develop feasible models for large-scale RZSM estimation using the surface observations, artificial neural networks, specifically multilayer perceptrons (MLPs), were applied in this study to estimate RZSM at the depths of 20cm and 50cm, using the data of 557 stations in the United States. Two experiments including four models were developed and the input variables of the models were carefully selected. The sensitivity analysis found that surface soil moisture and the cumulative rainfall, snowfall, air temperature and surface soil temperature were important inputs. If given soil texture data as inputs, the models achieved better performance and were extremely sensitive to them. The results showed that the MLPs were effective and flexible for the estimation of soil moisture at 20cm under various climate types and were insensitive to the potential errors in soil moisture datasets. However, the results of the estimation at 50cm are not as good as that of the 20cm. / Thesis / Master of Science (MSc)
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Implementation of UAS-based P-band signals of opportunity receiver for root-zone soil moisture retrievalPeranich, Preston 30 April 2021 (has links)
Root-zone soil moisture (RZSM) is an important variable when forecasting plant growth, determining water availability during drought, and understanding evapotranspiration as a flux. However, current methods indirectly estimate RZSM using data assimilation, which requires time-series data to make model-based predictions. This is because direct measurement requires a lower frequency signal, typically P-band and below (<500MHz), to reach root zone depths and, in turn, necessitates a larger antenna to be deployed in space, which is often unfeasible. A new remote sensing technique known as Signals of Opportunity (SoOp) reutilizes transmitted communication signals to perform microwave remote sensing. This means that SoOp platforms need not include a transmitter, but rather rely on passive radar technology to make measurements. This thesis details the development of a UAS-based P-band SoOp receiver instrument. This platform will be used to progress the state-of-art in techniques for direct measurement of RZSM.
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IRRIGATION, ADAPTATION, AND WATER SCARCITYIman Haqiqi (7481798) 17 October 2019 (has links)
<p>Economics is about the management
of scare resources. In agricultural production, water stress and excess heat
are the main constraints. The three essays of this dissertation try to improve
our understandings of how climate and water resources interact with agricultural
markets, and how global changes in agricultural markets may affect water
resources. I construct empirical and simulation models to explain the interplay
between agriculture and water. These models integrate economic theories with environmental
sciences to analyze the hydroclimatic and economic information at different
geospatial scales in a changing climate. </p>
<p>In the first essay, I illustrate
how irrigation, as a potential adaptation channel, can reduce the volatility of
crop yields and year-on-year variations caused by the projected heat stress.
This work includes estimation of yield response to climate variation for
irrigated and rainfed crops; and global projections of change in the mean and
the variation of crop yields. I use my estimated response function to project
future yield variations using NASA NEX-GDDP climate data. I show that the
impact of heat stress on rainfed corn is around twice as big as irrigated
practices. </p>
<p>In the second essay, I establish
a framework for estimating the value of soil moisture for rainfed production. This
framework is an extension of Schlenker and Roberts (2009) model enabled by the
detailed soil moisture information available from the Water Balance Model (WBM).
An important contribution is the introduction of a cumulative yield production
function considering the daily interaction of heat and soil moisture. I use
this framework to investigate the impacts of soil moisture on corn yields in
the United States. However, this framework can be used for the valuation of
other ecosystem services at daily basis.</p>
<p>In the third essay, I have
constructed a model that explains how the global market economy interacts with
local land and water resources. This helps us to broaden the scope of global to
local analysis of systems sustainability. I have employed SIMPLE-G-W (a
Simplified International Model of agricultural Prices, Land use, and the
Environment- Gridded Water version) to explain the reallocation across regions.
The model is based on a cost minimization behavior for irrigation technology
choice for around 75,000 grid cells in the United States constrained by water
rights, water availability, and quasi-irreversibility of groundwater supply. This
model is used to examine the vulnerability of US land and water resources from
global changes.</p>
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Estimation of Root Zone Soil Hydraulic Properties by Inversion of a Crop Model using Ground or Microwave Remote Sensing ObservationsSreelash, K January 2014 (has links) (PDF)
Good estimates of soil hydraulic parameters and their distribution in a catchment is essential for crop and hydrological models. Measurements of soil properties by experimental methods are expensive and often time consuming, and in order to account for spatial variability of these parameters in the catchment, it becomes necessary to conduct large number of measurements.
Estimation of soil parameters by inverse modelling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise for heterogeneous layered soils. Although extensive soil data is becoming more and more available at various scales in the form of digital soil maps there is still a large gap between this available information and the input parameters needed for hydrological models.
Inverse modeling has been extensively used but the spatial variability of the parameters and insufficient data sets restrict its applicability at the catchment scale. Use of remote sensed soil moisture data to estimate soil properties using the inverse modeling approach received attention
in recent years but yielded only an estimate of the surface soil properties. However, in
multilayered and heterogeneous soil systems the estimation of soil properties of different layers yielded poor results due to uncertainties in simulating root zone soil moisture from remote sensed surface soil moisture. Surface soil properties can be estimated by inverse approach using
surface soil moisture data retrieved from remote sensing data. Since soil moisture retrieved from remote sensing is representative of the top 5 cm only, inversion of models using surface soil
moisture cannot give good estimates of soil properties of deeper layers. Crop variables like biomass and leaf area index are sensitive to the deeper layer soil properties. The main focus of this study is to develop a methodology of estimation of root zone soil hydraulic properties in
heterogeneous soils by crop model based inversion techniques. Further the usefulness of the radar soil moisture and leaf area index in retrieving soil hydraulic properties using the develop approach is be tested in different soil and crop combinations.
A brief introduction about the soil hydraulic properties and their importance in agro-hydrological model is discussed in Chapter 1. Soil water retention parameters are explained in detail in this chapter. A detailed review of the literature is presented in chapter 2 to establish the state of art on the following: (i) estimation of soil hydraulic properties, (ii) role of crop models in estimating
soil hydraulic properties, (iii) retrieval of surface soil moisture using water cloud model from SAR data, (iv) retrieval of leaf area index from SAR (synthetic aperture radar) data and (v) modeling of root zone soil moisture and potential recharge.
The thesis proposes a methodology for estimating the root zone soil hydraulic properties viz. field capacity, wilting point and soil thickness. To test the methodology developed in this thesis
for estimating the soil hydraulic properties and their uncertainty, three synthetic experiments were conducted by inversion of STICS (Simulateur mulTIdiscplinaire pour les Cultures Standard) model for maize crop using the GLUE (Generalized Likelihood Uncertainty Estimation) approach. The estimability of soil hydraulic properties in a layer-wise heterogeneous soil was examined with several sets of likelihood combinations, using leaf area index, surface
soil moisture and above ground biomass. The robustness of the approach is tested with parameter estimation (model inversion) in two different meteorological conditions. The details of the numerical experiments and the several likelihood and meteorological cases examined are given in Chapter 3. The likelihood combination of leaf area index and surface soil moisture provided
consistently good estimates of soil hydraulic properties for all soil types and different meteorological cases. Relatively wet year provided better estimates of soil hydraulic properties as compared with a dry year.
To validate the approach of estimating root zone soil properties and to test the applicability of the approach in several crops and soil types, field measurements were carried out in the Berambadi
experimental watershed located in the Kabini river basin in south India. The profile soil
measurements were made for every 10 cm upto 1 m depth. Maize, Marigold, Sunflower,
Sorghum and Turmeric crops were monitored during the four year period from 2010 to 2013.
Crop growth parameters viz. leaf area index, above ground biomass, yield, phenological stages and crop management activities were measured/monitored at 10 day frequency for all the five crops in the study area. The details of the field experiments performed, the data collected and the results of the model inversion using the ground measured data are given in Chapter 4. The likelihood combination of leaf area index and surface soil moisture provided consistently lower
root mean square error (1.45 to 2.63 g/g) and uncertainty in the estimation of soil hydraulic properties for all soil crop and meteorological cases. The uncertainty in the estimation of soil hydraulic properties was lower in the likelihood combination of leaf area index and soil moisture. Estimability of depth of root zone showed sensitivity to the rooting depth.
Estimating root zone soil properties at field plot scale using SAR data (incidence angle 24o, wave length 5.3 GHz) of RADARSAT-2 is presented in the Chapter 5. In the first step, an approach of estimating leaf area index from radar vegetation index using the parametric growth curve of leaf
area index and the retrieval of soil moisture using water cloud model are given in Chapter 5. The parameters of the growth curve and the leaf area index are generated using a time series of RADARSAT-2 for two years 2010-2011 and 2011-12 for the crops (maize, marigold, sunflower, sorghum and turmeric) considered in this study. The surface soil moisture is retrieved using the
water cloud model, which is calibrated using the ground measured values of leaf area index and surface soil moisture for different soils and crops in the study area. The calibration and validation of LAI and water cloud models are discussed in this Chapter. Eventually, the retrieved leaf area
index and surface soil moisture from RADARSAT-2 data were used to estimate the soil hydraulic properties and their uncertainty in a similar manner as discussed in Chapter 4 for various crop and soil plots and the results are presented in Chapter 5. The mean and uncertainty in the estimation of soil hydraulic properties using inversion of remote sensing data provided results similar to the estimates from inversion of ground data. The estimates of soil hydraulic
properties compared well (R2 of 0.7 to 0.80 and RMSE of 2.1 to 3.16 g/g) with the physically measured vales of the parameters.
In Chapter 6, root zone soil moisture and potential recharge are modelled using the STICS model and the soil hydraulic parameters estimated using the RADARSAT-2 data. The potential recharge is highly sensitive to the water holding capacity of rooting zone. Variability in the root
zone soil moisture for wet and dry years for different soil types on irrigated and non-irrigated crops were investigated. Potential recharge from different crop and soil types were compared.
The uncertainty in the estimation of potential recharge due to uncertainty in the estimation of field capacity is quantified. The root zone soil moisture modeled by STICS showed good agreement with the measured root zone soil moisture in all crop and soil cases. This was tested for both dry and wet year and provides similar results. The temporal variability of root zone soil
moisture was also modeled well by the STICS model; the model also predicted well the intra-soil variability of soil moisture of root zone. The results of the modeling of root zone soil moisture and potential recharge are presented in Chapter 6. At the end, in Chapter 7, the major conclusions drawn from the various chapters are summarized.
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