Fusion of Remotely Sensed Imagery and Minimal Ground Sampling for Soil Moisture Mapping

A methodology for mapping surface soil moisture content across an agricultural field from optical remote sensing and limited ground sampling is developed. This study uses remotely sensed spectral measurements of soil reflectance in a single visible wavelength and historical measurements of volumetric soil moisture within the top 6 cm, in conjunction with a single ground measurement. Results indicate that combining reflectance and ground measurements can yield more detailed maps of soil moisture than ground measurement alone

Genetic algorithm for parameter and scale selection to predict soil moisture patterns

Soil moisture is a critical component of hydrological processes, and its spatio-temporal distribution depends on many geographical factors (such as elevation, slope, and aspect, etc.). Each of the factors is likely influential over a different scale and to a different degree. Near-surface soil moisture data were collected across a working 10-ha field southwest of Ames, IA in growing seasons of 2004 to 2007. A genetic algorithm is developed to compare geographical factors to the moisture patterns over a range of scales. The genetic algorithm will develop a model in which each factor is computed over a different scale for use in prediction of reference variable. Optimized scales for each parameter are arrived at through successive generations, including crossover and mutation of this evolutionary algorithm. Using this approach, not only are the primary influential relationships uncovered, but the most appropriate scale for comparison to moisture pattern is identified. The results of this analysis can be used to predict the spatio-temporal patterns of soil moisture across a region a priori

Analysis of the Influence of Soil Roughness, Surface Crust and Soil Moisture on Spectral Reflectance

Soil moisture is an important component of numerous systems, influencing crop development, and runoff and infiltration partitioning, among other things. However, due to its spatial and temporal variability, it is difficult to estimate soil moisture consistently using conventional techniques such as gravimetric sampling, which is point-based and time-consuming. Therefore, to overcome this drawback in soil moisture estimation and mapping, and to facilitate its measurement spatially and temporarily, remote sensing is a promising technique. Measurement of soil surface reflectance in the visible and near infrared (VIS/NIR) may be used for this purpose. However, soil reflectance within this spectral range is affected by numerous factors, including soil surface roughness and the presence of soil crust. Thus, in order to determine the utility of VIS/NIR remote sensing for surface soil moisture estimation, roughness and crusting must be considered. In this study, we quantify the effects of these three components (moisture, roughness, and degree of crusting) on soil surface reflectance within the spectral range of 450 nm to 1000 nm in order to determine the extent to which moisture can be estimated under different soil surface conditions

Understanding Spatio-temporal Patterns of Soil Moisture at the Field Scale

Spatial patterns of soil moisture across a field seem to exhibit some degree of temporal stability, which has been proved to be related to such invariant attributes as topography and soil characteristics. However, how these patterns and locations might be predicted from these attributes is not well understood. Motivated by a desire to understand these relationships, the objective of this study is to determine how elevation relates to underlying stable and consistent moisture patterns. The characteristics of temporal stability of soil moisture across the field have been analyzed during the 2004 and 2005 growing seasons for a 10-ha field near Ames, IA. Ordinary Kriging (OK) and kriging with external drift (KED) have been used as interpolation tools to estimate the spatial pattern of soil moisture across the field in each observing date. Temporally stable locations can be used to accurately predict the field mean soil moisture. Also, kriging predictions of soil moisture on un-sampled locations using OK and KED have no significant differences in the predicted soil moisture surfaces, but on their standard error of prediction

Modeling zone management in precision agriculture through Fuzzy C-Means technique at spatial database

Predict the optimal number of zones to manage tasks evolved in precision agriculture applications is challenging issue in classification tasks. Important decisions in the farm required maps of yield classes which contain relative large, similar and spatially contiguous partitions and sometimes without a priori knowledge of the field. The main goal of this study was to apply Fuzzy C-means (FCM), an unsupervised classification technique, in a geo-referenced yield and grain moisture dataset in order to find optimal number for homogeneous zones. Those data were produced by Long-Term Ecological Research in a Biological Station (KBS-LTER), Michigan, during growing season at 2008. The best results presented by this algorithm ranged from 8 to 10 zones which were validated using the indexes Partition Coefficient (PC), Classification Entropy (CE) and Dunn’s Index (DI). Even though, only two attributes were collected in the dataset, the Fuzzy C-means has shown promissing results for zone mapping

Quantifying uncertainty of sediment TMDLs using a stochastic approach

Scientific uncertainty inherent in the development of Total Maximum Daily Load (TMDL) standards for non-point source pollutants such as sediment hampers the program’s effectiveness. Sediment is an important water quality parameter because deposition in streams and lakes adversely affects aquatic ecosystems. Equally important, suspended sediment is a transport mechanism for nutrients, pesticides and pathogens. This paper presents an alternative methodology that permits statistically valid calculation of sediment TMDL uncertainty. The sediment delivery computer simulation technology used for this project, the Geo-spatial interface for the Water Erosion Prediction Project (GeoWEPP), is capable of simulating single storm events and provides daily output useful for TMDL statistical analysis

Nonpoint source pollution uncertainty: Stakeholder perceptions

High variability of nonpoint source (NPS) pollutant loads caused primarily by uncontrollable precipitation events creates great uncertainty for those charged with NPS management. Stakeholder disagreement on the best way to address the uncertainty issue can lead to inaction. However, understanding different stakeholder perspectives could promote consensus and a unified effort to effectively address this difficult pollution problem. This paper probes methodologies for quantifying the uncertainty of soil erosion and sediment load predictions and evaluates stakeholder perceptions of the issue through a focus group study. Three groups, each consisting of 5 to 8 individuals, convened to answer a set of questions designed to promote discussion of soil erosion and sediment load prediction uncertainty. One group was composed of natural resource professionals and scientists, another of individuals with environmental interests, and the third of producers and producer association representatives. The goal of the study is to gain insight into perceptions of NPS pollution uncertainty, the need for its quantification, and its impact on water quality improvement efforts. The findings of this study have important implications for EPA’s TMDL program and other NPS pollution control initiatives

Data Assimilation of Near-Surface In-Situ Soil Moisture Using the DSSAT Crop Model

Soil water is an important variable in agricultural environments as it contributes to yield response as well as areas of environmental concern including erosion, runoff, and nitrogen leaching (through deep drainage). Crop models have been established as a method for simulating agricultural production and examining ecosystem responses. However, because all crop models are based on limited system information, models contain errors which increase uncertainty around their predictions. Data assimilation provides the opportunity to merge both model and observational data in order to obtain a better representation of the true physical system. The objectives of our experiments are to (1) evaluate the efficacy and feasibility of implementing a simple data assimilation algorithm for near-surface soil moisture in the DSSAT (Decision Support System for Agrotechnology Transfer) Model and (2) examine changes in yield from different data assimilation cases. In this paper we use direct insertion, a simple data assimilation method, to examine how assimilation of near-surface (0 – 5 cm) soil water content observations impacts maize yields. Three synthetic experiments were performed using 20 years of simulated climate data, two common Iowa soil types, and two nitrogen rates. The CERES-Maize component of the DSSAT Model was used for simulations. The first experiment consists of simple perturbations of model observations, the second experiment uses incorrect model soil parameters, and the third experiment examines a model bias. The results of the experiments performed here show that it is possible to implement a direct insertion algorithm for near-surface soil water content into the DSSAT model. Yield differences varied according to year, soil type, and nitrogen rate. The results of all three experiments showed that yield differences can occur between scenarios which use the original model generated values and assimilated values even when a simple assimilation method (direct insertion) is used. This information provides preliminary insights into the feasibility and impact of using data assimilation with agricultural systems

Under the ASABE Umbrella — Engineering Degree Programs Need Curriculum Reform

The first-ever issue of Transactions of the ASAE, published in 1907, opens with a talk given by Howard W. Riley (after whom Riley-Robb Hall at Cornell University would later be named) that\u27s modestly titled The Courses in Agricultural Engineering that Should be Offered. Responses from several other luminaries, including J. B. Davidson (after whom Davidson Hall at Iowa State University would later be named), are included and make for fascinating reading for any student or practitioner of our discipline

Electrical Conductivity of Agricultural Drainage Water in Iowa

Assessing the effectiveness of management strategies to reduce agricultural nutrient efflux is hampered by the lack of affordable, continuous monitoring systems. Generalized water quality monitoring is possible using electrical conductivity. However environmental conditions can influence the ionic ratios, resulting in misinterpretations of established electrical conductivity and ionic composition relationships. Here we characterize specific electrical conductivity (k25) of agricultural drainage waters to define these environmental conditions and dissolved constituents that contribute to k25. A field investigation revealed that the magnitude of measured k25 varied from 370 to 760 µS cm-1. Statistical analysis indicated that variability in k25 was not correlated with drainage water pH, temperature, nor flow rate. While k25 was not significantly different among drainage waters from growing and post-growing season, significant results were observed for different cropping systems. Soybean plots in rotation with corn had significantly lower conductivities than those of corn plots in rotation with soybeans, continuous corn plots, and prairie plots. In addition to evaluating k25 variability, regression analysis was used to estimate the concentration of major ions in solution from measured k25. Regression results indicated that HCO3-, Ca2+, NO3-, Mg2+, Cl-, Na2+, SO42- were the major drainage constituents contributing to the bulk electrical conductivity. Calculated ionic molal conductivities of these analytes suggests that HCO3-, Ca2+, NO3-, and Mg2+ account for approximately 97% of the bulk electrical conductivity