Relationships between leaf wetness and plant diseases have been studied for centuries. The progress and risk of many bacterial, fungal, and oomycete diseases on a variety of crops have been linked to the presence of free water on foliage and fruit under temperatures favorable to infection. Whereas the rate parameters for infection or epidemic models have frequently been linked with temperature during the wet periods, leaf wetness periods of specific time duration are necessary for the propagule germination of most phytopathogenic fungi and for their penetration of plant tissues. Using these types of relationships, disease-warning systems were developed and are now being used by grower communities for a variety of crops. As a component of Integrated Pest Management, disease-warning systems provide growers with information regarding the optimum timing for chemical or biological management practices based on weather variables most suitable for pathogen dispersal or host infection. Although these systems are robust enough to permit some errors in the estimates or measurements of leaf wetness duration, the need for highly accurate leaf wetness duration data remains a priority to achieve the most efficient disease management.

Soil moisture affects the spatial variation of land–atmosphere interactions through its influence on the balance of latent and sensible heat fluxes. Wetter soils are more prone to flooding because a smaller fraction of rainfall can infiltrate into the soil. The Soil Moisture Ocean Salinity (SMOS) satellite carries a remote sensing instrument able to make estimates of near-surface soil moisture on a global scale. One way to validate satellite observations is by comparing them with observations made with sparse networks of in situ soil moisture sensors that match the extent of satellite footprints. The rate of soil drying after significant rainfall observed by SMOS is found to be higher than the rate observed by a U.S. Department of Agriculture (USDA) soil moisture network in the watershed of the South Fork Iowa River. This leads to the conclusion that SMOS and the network observe different layers of the soil: SMOS observes a layer of soil at the soil surface that is a few centimeters thick, while the network observes a deeper soil layer centered at the depth at which the in situ soil moisture sensors are buried. It is also found that SMOS near-surface soil moisture is drier than the South Fork network soil moisture, on average. The conclusion that SMOS and the network observe different layers of the soil, and therefore different soil moisture dynamics, cannot explain the dry bias. However, it can account for some of the root-mean-square error in the relationship. In addition, SMOS observations are noisier than the network observations.

Accurate estimates of biomass are imperative for understanding the global carbon cycle. However, measurements of biomass and water in the biomass are difficult to obtain at a scale consistent with measurements of mass and energy transfer, ~1 km, leading to substantial uncertainty in dynamic global vegetation models. Here we use a novel cosmic ray neutron method to estimate a stoichiometric predictor of ecosystem‐scale biomass and biomass water equivalent over tens of hectares. We present two experimental studies, one in a ponderosa pine forest and the other in a maize field, where neutron‐derived estimates of biomass water equivalent are compared and found consistent with direct observations. Given the new hectometer scale of nondestructive observation and potential for continuous measurements, we anticipate this technique to be useful to many scientific disciplines.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite mission provides microwave L-band measurements of vegetation optical thickness over the Earth. Optical thickness is related to water held in vegetation. The water content of crops varies over the growing season from a minimum during planting to a maximum during reproduction and back to a minimum during senescence. We found that in Iowa in 2010 the change in SMOS optical thickness over the growing season can be related to crop yields. However, there are inconsistencies in the optical thickness data, particularly high-frequency variation and unexpected changes outside of the growing season. We hypothesize that the unexpected changes during the dormant periods are due to changes in soil surface roughness caused by land management activities and show a relationship between changes in roughness and changes in optical thickness, which may be confusing the SMOS retrieval algorithm.