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

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

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

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

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

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

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

Dissolved Constituents in Agricultural Drainage Waters

Efflux of dissolved solutes in agricultural subsurface drainage systems adversely affects the ecosystems of receiving waters, degrades soil fertility, and represents an economic loss to farmers. These solutes are frequently studied without regard to their associated ions, which play a fundamental role in their transport characteristics. In this study, we conducted a literature review to identify major dissolved constituents in agricultural drainage waters characteristic of central Iowa and pinpointed causes of variability in the leaching rate of these constituents. This literature review is complemented by a thorough field investigation that analyzes major solute concentrations with respect to seasonal conditions, common cropping systems, and relationships among ions. Results from this investigation reveal that primary dissolved constituents consist of bicarbonate, calcium, nitrate, magnesium, chloride, sodium, and sulfate (in order of decreasing ppm concentration). Analysis of seasonal drainage samples showed that bicarbonate, calcium, and magnesium were present at greater concentrations during the post-growing season, while nitrate and chloride concentrations were greatest during the growing season. Seasonal variability of sulfate and sodium was negligible. Continuous corn and corn in annual rotation with soybeans had greater magnesium and chloride concentrations than soybeans in annual rotation with corn. Conversely, calcium concentrations were greater for soybean cropping systems compared to corn cropping systems. Bicarbonate and nitrate were not significantly different among any of the cropping systems. A strong correlation between bicarbonate and calcium suggests that agricultural lime dissolution was caused by mineral weathering, rather than by acidification due to N fertilizer applications or nitrification. An analysis of observed drainage flows, pH, and temperatures suggested that these parameters were not good indicators of differences in ionic composition

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

Soil water dynamics under various agricultural land covers on a subsurface drained field in north-central Iowa, USA

Modification of land cover systems is being studied in subsurface drained Iowa croplands due to their potential benefits in increasing soil water and nitrogen depletion thus reducing drainage and NO3–N loss in the spring period. The objective of this study was to evaluate the impacts of modified land covers on soil water dynamics. In each individual year, modified land covers including winter rye–corn (rC), winter rye–soybean (rS), kura clover as a living mulch for corn (kC), and perennial forage (PF), as well as conventional corn (C) and soybean (S), were grown in subsurface drained plots in north-central Iowa. Results showed that subsurface drainage was not reduced under modified land covers in comparison to conventional corn and soybean. Soil water storage (SWS) was significantly reduced by PF treatments during the whole growing seasons and by kC during May through July when compared to the cropping system with corn or soybean only (p \u3c 0.05). Treatments of rC and rS typically maintained higher SWS than C and S, respectively, during the 3 years of this study. In the spring during a 10–15-day period when the rainfall was minimal, SWS in plots with rye, kura clover, and forage decreased at a significantly higher rate than the C and S plots which were bare. Estimated evapotranspiration (ET) during this period was significantly higher in rS, kC, and PF treatments than C and S. The results of this study suggested that significantly higher ET and similar drainage for modified land covers may increase water infiltration, which would be expected to reduce surface runoff thus to decrease stream flow. Because subsurface drainage reduction was not seen in this study, impact of modified land covers on NO3–N loss needs further investigation