PREPLANTING SOIL MOISTURE USING PASSIVE MICROWAVE SENSORS1

ABSTRACT: Accurate assessment of preplanting soil moisture conditions is necessary for good agricultural management, and can have a significant influence on crop yield in the Texas Panhandle region. The Texas High Plains Underground Water Conservation District invests considerable time and money in developing a soil moisture deficit map each year in the hopes of achieving optimal use of irrigation water. Microwave sensors are responsive to surface soil moisture and, if used in this application, can provide timely and detailed information on root zone soil moisture. For this reason, an experiment was conducted in 1984 to evaluate the potential of aircraft-mounted passive microwave sensors. Microwave radiometer data were collected over a 2700 km² area near Lubbock, Texas, with a processed resolution of 0.32 km². These data were ground registered and converted to estimates of soil moisture using an appropriate model and land cover and soil texture information. Analyses indicate that the system provides an efficient means for mapping variations in soil moisture over large areas.     

RESPONSE FUNCTIONS OF CROPS YIELD TO SOIL MOISTURE STRESS1

: A methodology for determining an optimal irrigation policy to obtain maximum economic yield of crops is presented. The method is based on an empirical response function, relating crop yield to soil moisture stress in different stages of plant growth. The function was developed from data obtained in field experiments on several crops and may be useful in determining when and how much to irrigate. Additionally, a mathematical expression is derived from that function which estimates the economic loss if irrigation is not applied in the opportune moment.     

SOIL MOISTURE SENSORS FOR CONTINUOUS MONITORING1

ABSTRACT: Certain physical and chemical properties of soil vary with soil water content. The relationship between these properties and water content is complex and involves both the pore structure and constituents of the soil solution. One of the most economical techniques to quantify soil water content involves the measurement of electrical resistance of a dielectric medium that is in equilibrium with the soil water content. The objective of this research was to test the reliability and accuracy of fiberglass soil-moisture electrical resistance sensors (ERS) as compared to gravimetric sampling and Time Domain Reflectometry (TDR). The response of the ERS was compared to gravimetric measurements at eight locations on the USDA-ABS Walnut Gulch Experimental Watershed. The comparisons with TDR sensors were made at three additional locations on the same watershed. The high soil rock content (≥45 percent) at seven locations resulted in consistent overestimation of soil water content by the ERS method. Where rock content was less than 10 percent, estimation of soil water was within 5 percent of the gravimetric soil water content. New methodology to calibrate the ERS sensors for rocky soils will need to be developed before soil water content values can be determined with these sensors.     

SPATIAL DISTRIBUTION OF POINT SOIL MOISTURE ESTIMATES USING LANDSAT TM DATA AND FUZZY-C CLASSIFICATION1

ABSTRACT: Many hydrologic models have input data requirements that are difficult to satisfy for all but a few well-instrumented, experimental watersheds. In this study, point soil moisture in a mountain watershed with various types of vegetative cover was modeled using a generalized regression model. Information on sur-ficial characteristics of the watershed was obtained by applying fuzzy set theory to a database consisting of only satellite and a digital elevation model (DEM). The fuzzy-c algorithm separated the watershed into distinguishable classes and provided regression coefficients for each ground pixel. The regression model used the coefficients to estimate distributed soil moisture over the entire watershed. A soil moisture accounting model was used to resolve temporal differences between measurements at prototypical measurement sites and validation sites. The results were reasonably accurate for all classes in the watershed. The spatial distribution of soil moisture estimates corresponded accurately with soil moisture measurements at validation sites on the watershed. It was concluded that use of the regression model to distribute soil moisture from a specified number of points can be combined with satellite and DEM information to provide a reasonable estimation of the spatial distribution of soil moisture for a watershed.     

SOIL MOISTURE ACCOUNTING COMPONENT OF THE USDAHL-74 MODEL OF WATERSHED HYDROLOGY1

ABSTRACT: Soil moisture in two layers of a soil near Chickasha, Oklahoma, was simulated, using USDAHL-74 Model of Watershed Hydrology. Weekly values computed for both layers compared well with those observed during the 15-month period. Certain key parameters required adjustments in the model which illustrate the need for accurate input information. The experiment demonstrates that the model, which has previously given good results in continuous streamflow prediction on watersheds up to 100 square miles, can also compute soil moisture continuously at a site. This capability suggests other model uses, for example, in monitoring the disposition of applied chemicals.     

SATELLITE MICROWAVE OBSERVATIONS OF SOIL MOISTURE VARIATIONS1

ABSTRACT: Results from studies in the Illinois-Indiana and Texas-Oklahoma areas indicate that satellite microwave observations at the 1.55 cm wavelength are responsive to relative moisture variations in the near surface layer of the soil. Because significant vegetation cover absorbs the 1.55 cm microwave emission from the soil, the target area must be predominately bare soil or low density vegetation cover for meaningful measurements to result. The 25 km resolution of the satellite sensor limits application of the microwave techniques to large areas such as watersheds or agricultural districts rather than individual fields. In general, at 1.55 cm. there is an inverse relationship between microwave brightness temperature and changes in soil moisture levels (as indicated by antecedent rainfall) in agricultural regions before the planting of crops or during the early growing season when vegetation cover is sparse. Even early season observations should be of great value in deciding on the time and type of crop planting and for initial irrigation scheduling when the root zone is still in close proximity to the surface.     

EVAPOTRANSPIRATION AND SOIL MOISTURE DYNAMICS ON A SEMIARID PONDEROSA PINE HILLSLOPE1

ABSTRACT: Detailed measurements of soil moisture and ET in semiarid forest environments have not been widely reported in the literature. In this study, soil moisture and water balance components were measured over a four‐year period on a semiarid ponderosa pine hillslope, with evapotranspiration (ET) determined as the residual of measured precipitation, runoff, and change in soil moisture storage. ET accounts for approximately 95 percent of the water budget and has a distinctly bimodal annual pattern, with peaks occurring after spring snowmelt and during the late summer monsoon season, periods that coincide with high soil moisture. Weekly growing season ET rates determined by the hillslope water balance are found to be invariably below calculated potential rates. Normalized ET rates are linearly correlated (r²= 0.62) with soil moisture; therefore, a simple linear relation is proposed. Growing season soil moisture dynamics were modeled based on this relation. Results are in fair agreement (r²= 0.63) with the observed soil moisture data over the four growing seasons; however, for two dry summers with little surface runoff, much better results (r² > 0.90) were obtained.     

SOIL MOISTURE SENSORS FOR URBAN LANDSCAPE IRRIGATION: EFFECTIVENESS AND RELIABILITY1

ABSTRACT: Granular matrix soil moisture sensors were used to control urban landscape irrigation in Boulder, Colorado, during 1997. The purpose of the study was to evaluate the effectiveness and reliability of the technology for water conservation. The 23 test sites included a traffic median, a small city park, and 21 residential sites. The results were very good. The system limited actual applications to an average of 73 percent of the theoretical requirement. This resulted in an average saving of $331 per installed sensor. The sensors were highly reliable. All 23 sensors were placed in service at least three years prior to the 1997 study during earlier studies. Of these, only two had failed by the beginning of the 1997 study, both due to external factors. Including replacement of these failed sensors, the total repair cost for the 1997 irrigation season was less than $270. The effort required to maintain each system was small, only about 6–7 minutes per visit. Each site was visited weekly for this study, but less frequent visits could be made in practice. The sensors observed in this study performed well, significantly reduced water consumption, and were easy to monitor and maintain. Soil moisture sensors appear to be a useful and economical tool for urban water conservation.     

METHODOLOGY AND EMPIRICAL ESTIMATES OF THE RESPONSE FUNCTION OF SORGHUM TO IRRIGATION AND SOIL MOISTURE1

ABSTRACT: This paper presents a methodology for the estimation of response functions of crops to irrigation and soil moisture. A system analysis framework is applied to describe the relationships involved. Two subsystems are distinguished, with the first one involving the relationship between irrigation decision variables and soil state variables, and the second involving the relationship between soil state variables and crop yield. A method for tracing and predicting soil moisture profile variations over time and depth is presented, and empirical estimates of the response function of grain sorghum to soil moisture are derived. In the specification of the response function the concept of “critical days” is applied with a “critical day” being defined as one where the soil moisture is depleted below a certain critical level. The paper provides empirical evidence for the usefulness of the approach     

SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES1

ABSTRACT: Herein, a recently developed methodology, Support Vector Machines (SVMs), is presented and applied to the challenge of soil moisture prediction. Support Vector Machines are derived from statistical learning theory and can be used to predict a quantity forward in time based on training that uses past data, hence providing a statistically sound approach to solving inverse problems. The principal strength of SVMs lies in the fact that they employ Structural Risk Minimization (SRM) instead of Empirical Risk Minimization (ERM). The SVMs formulate a quadratic optimization problem that ensures a global optimum, which makes them superior to traditional learning algorithms such as Artificial Neural Networks (ANNs). The resulting model is sparse and not characterized by the “curse of dimensionality.” Soil moisture distribution and variation is helpful in predicting and understanding various hydrologic processes, including weather changes, energy and moisture fluxes, drought, irrigation scheduling, and rainfall/runoff generation. Soil moisture and meteorological data are used to generate SVM predictions for four and seven days ahead. Predictions show good agreement with actual soil moisture measurements. Results from the SVM modeling are compared with predictions obtained from ANN models and show that SVM models performed better for soil moisture forecasting than ANN models.     

ESTIMATION OF SOIL MOISTURE FROM SEASAT SAR DATA1

ABSTRACT A study was made to determine if Seasat Synthetic Aperture Radar (SAR) data could be used to make practical estimates of soil moisture. Extensive ground measurements were collected at two primary sites near Guymon, Oklahoma, and Sublette, Kansas. The relative sensitivity of the SAR to differences in soil moisture, tillage roughness, and vegetation was determined. To validate the effects detected in the SAR data, an airborne scatterometer with a similar wavelength was flown repeatedly over the Guymon site. Soil moisture variations in the surface 2 cm and surface 15 cm of fields with bare soil, milo and alfalfa produce similar responses in the scattering coefficient from both systems. Roughness due to tillage in row crops produced as much as 12–15 dB increase in the scatterometer return. Most agricultural vegetation was effectively penetrated by the L-band frequencies; however, corn produced an exceptionally high radar return either standing or after combine harvesting. When corn had ripened, there was some evidence that tillage roughness could be detected through the canopy. Moderate tillage roughness produced by grain drill furrows caused over 12 dB increase in return when row directions changed from parallel to perpendicular with respect to the SAR look direction. Dramatic increases in return occurred when vegetation surfaces were wet. Increased radar returns from tillage roughness, some vegetation and wet vegetation surfaces, all dyanmic in nature, were significant and may limit the practical estimation of soil moisture from the radar data.     

ESTIMATION OF SOIL MOISTURE WITH API ALGORITHMS AND MICROWAVE EMISSION1

Large area soil moisture estimations are required to describe input to cloud prediction models, rainfall distribution models, and global crop yield models. Satellite mounted microwave sensor systems that as yet can only detect moisture at the surface have been suggested as a means of acquiring large area estimates. Relations previously discovered between microwave emission at the 1.55 cm wavelength and surface moisture as represented by an antecedent precipitation index were used to provide a pseudo infiltration estimation. Infiltration estimates based on surface wetness on a daily basis were then used to calculate the soil moisture in the surface 0–23 cm of the soil by use of a modified antecedent precipitation index. Reasonably good results were obtained (R²= 0.7162) when predicted soil moisture for the surface 23 cm was compared to measured moisture. Where the technique was modified to use only an estimate of surface moisture each three days an R² value of 0.7116 resulted for the same data set. Correlations between predicted and actual soil moisture fall off rapidly for repeat observations more than three days apart. The algorithms developed in this study may be used over relatively flat agricultural lands to provide improved estimates of soil moisture to a depth greater than the depth of penetration for the sensor.     

IMPROVED RAINFALL/RUNOFF ESTIMATES USING REMOTELY SENSED SOIL MOISTURE1

ABSTRACT: Remotely sensed soil moisture data measured during the Southern Great Plains 1997 (SGP97) experiment in Oklahoma were used to characterize antecedent soil moisture conditions for the Soil Conservation Service (SCS) curve number method. The precipitation‐adjusted curve number and the soil moisture were strongly related (r²= 0.70). Remotely sensed soil moisture fields were used to adjust the curve numbers and the runoff estimates for five watersheds, in the Little Washita watershed; the results ranged from 2.8 km² to 601.6 km². The soil moisture data were applied at two spatial scales, a finer one (800 m) measuring spatial resolution and a coarser one (28 km). The root mean square error (RMSE) and the mean absolute error (MAE) of the runoff estimated by the standard SCS method was reduced by nearly 50 percent when the 800 m soil moisture data were used to adjust the curve number. The coarser scale soil moisture data also significantly reduced the error in the runoff predictions with 41 percent and 28 percent reductions in MAE and RMSE, respectively. The results suggest that remote sensing of soil moisture, when combined with the SCS method, can improve rainfall runoff predictions at a range of spatial scales.     

ASSESSMENT OF SAMPLING ERROR ASSOCIATED WITH SOIL MOISTURE ESTIMATION DESIGNS1

A spectral formalism was developed and applied to quantify the sampling errors due to spatial and/or temporal gaps in soil moisture measurements. A design filter was developed to compute the sampling errors for discrete measurements in space and time. This filter has as its advantage a general form applicable to various types of sampling design. The lack of temporal measurements of the two‐dimensional soil moisture field made it difficult to compute the spectra directly from observed records. Therefore, the wave number frequency spectra of soil moisture data derived from stochastic models of rainfall and soil moisture were used. Parameters for both models were estimated using data from the Southern Great Plains Hydrology Experiment (SGP97) and the Oklahoma Mesonet. The estimated sampling error of the spatial average soil moisture measurement by airborne L‐band microwave remote sensing during the SGP97 hydrology experiment is estimated to be 2.4 percent. Under the same climate conditions and soil properties as the SGP97 experiment, equally spaced ground probe networks at intervals of 25 and 50 km are expected to have about 16 percent and 27 percent sampling error, respectively. Satellite designs with temporal gaps of two and three days are expected to have about 6 percent and 9 percent sampling errors, respectively.     

SOIL MOISTURE SENSING WITH AIRCRAFT OBSERVATIONS OF THE DIURNAL RANGE OF SURFACE TEMPERATURE1

ABSTRACT: Aircraft Observations of the surface temperature were made by measurements of the thermal emission in the 8-14 μm band over agricultural fields around Phoenix, Arizona. The diuranal range of these surface temperature measurnments were well correlated with the ground measurment of soil moisture in the 0-2 cm layer. The surface temperature indicating no moisture stress. These results indicate that for clear atmospheric conditions remoteley sensed sufrace temperatures can be a reliable indicator of soil moisture conditions and crop status.     

EFFECTS OF CLIMATIC CHANGE ON THE THORNTHWAITE MOISTURE INDEX1

ABSTRACT: The Thornthwaite moisture index is a useful indicator of the supply of water (precipitation) in an area relative to the demand for water under prevailing climatic conditions (potential evapotranspiration). This study examines the effects of changes in climate (temperature and precipitation) on the Thornthwaite moisture index in the conterminous United States. Estimates of changes in mean annual temperature and precipitation for doubled-atmospheric CO₂ conditions derived from three general circulation models (GCMs) are used to study the response of the moisture index under steady-state doubled-CO₂ conditions. Results indicate that temperature and precipitation changes under doubled-CO₂ conditions generally will cause the Thornthwaite moisture index to decrease, implying a drier climate for most of the United States. The pattern of expected decrease is consistent among the three GCMs, although the amount of decrease depends on which GCM climatic-change scenario is used. Results also suggest that changes in the moisture index are related mainly to changes in the mean annual potential evapotranspiration as a result of changes in the mean annual temperature, rather than to changes in the mean annual precipitation.     

A FIELD EVALUATION OF THE EFFECTS OF HEATED DISCHARGES ON FISH DISTRIBUTION1

ABSTRACT. Studies were conducted to determine the distribution of fish in the New and East Rivers in relation to thermal discharges from Appalachian Power Company's fossil fuel plant at Glen Lyn, Virginia. Over 15,300 specimens representing 41 species were collected with seines, electrogear and rotenone at six sampling locations from February, 1973 to October 1973. Sampling frequency was designed to evaluate the effects of ambient temperature upon preferred temperature. Diversity indices were calculated for each location. There was a slight decrease in the diversity indices for those stations located in the thermal discharge. Condition coefficients calculated for Notropis albeolus Jordan, Notropis rubellus Aqasaiz, Notropis spilopterus Cope, Ictalurus punctatus Rafinesque, and Etheostoma blennioides Rafuesque were found to be significantly (p =.05) lower in the thermal discharge for all species tested except E. blennioides. Temperatures were plotted against frequency of capture to determine a particular species temperature selection from field data and indicated that: (1) Some species avoided high temperatures (i.e., Curnpostoma anomalum Rafuesque). (2) Some species were attracted to high temperatures (i.e., Ictalurus punctatus). (3) Some species distribution was not effected by temperatures (i.e., Notropis spilopterus).     

USE OF SOIL MOISTURE PROBES TO ESTIMATE GROUND WATER RECHARGE AT AN OIL SPILL SITE1

Soil moisture data collected using an automated data logging system were used to estimate ground water recharge at a crude oil spill research site near Bemidji, Minnesota. Three different soil moisture probes were tested in the laboratory as well as the field conditions of limited power supply and extreme weather typical of northern Minnesota: a self‐contained reflectometer probe, and two time domain reflectometry (TDR) probes, 30 and 50 cm long. Recharge was estimated using an unsaturated zone water balance method. Recharge estimates for 1999 using the laboratory calibrations were 13 to 30 percent greater than estimates based on the factory calibrations. Recharge indicated by the self‐contained probes was 170 percent to 210 percent greater than the estimates for the TDR probes regardless of calibration method. Results indicate that the anomalously large recharge estimates for the self‐contained probes are not the result of inaccurate measurements of volumetric moisture content, but result from the presence of crude oil, or borehole leakage. Of the probes tested, the 50 cm long TDR probe yielded recharge estimates that compared most favorably to estimates based on a method utilizing water table fluctuations. Recharge rates for this probe represented 24 to 27 percent of 1999 precipitation. Recharge based on the 30 cm long horizontal TDR probes was 29 to 37 percent of 1999 precipitation. By comparison, recharge based on the water table fluctuation method represented about 29 percent of precipitation.     

EMPHASIS ON DISTRIBUTION: STRATEGIES FOR SMALL SCALE WATER RESOURCES DEVELOPMENT IN THAILAND1

This paper discusses strategies for the development of water resources, emphasizing the delivery of reliable water supplies, for both domestic and production purposes, to every village and to every farmer. This necessitates a shift of emphasis from the construction of large storage reservoirs to the construction, operation, and maintenance of water distribution systems capable of reaching the largest number of farms, and a shift from projects that benefit the few, to projects that benefit the many. Water distribution in this context takes on three interrelated meanings: a geographical meaning, a technical meaning, and an economic meaning. The geographical meaning focuses on the spatial distribution of the recipient population as a key to identifying the proper distribution of water projects in physical space. The technical meaning relates to the physical distribution of water through canal systems to the farmers' fields. The economic meaning refers to the equitable distribution of benefits from water projects. The paper provides an illustration of the need for an emphasis on distribution, using the state of development of water resources in northeast Thailand as an example, with a proposed program for the further development of these resources. The northeast, the poorest region in the country, has been recognized by the Government of Thailand as a priority area for accelerated regional development efforts.