Remote sensing offers the capability of observing an object without being in contact with the object. Throughout the recent history of agriculture, researchers have observed that different wavelengths of light are reflected differently by plant leaves or canopies and that these differences could be used to determine plant biophysical characteristics, e.g., leaf chlorophyll, plant biomass, leaf area, phenological development, type of plant, photosynthetic activity, or amount of ground cover. These reflectance differences could also extend to the soil to determine topsoil properties. The objective of this review is to evaluate how past research can prepare us to utilize remote sensing more effectively in future applications. To estimate plant characteristics, combinations of wavebands may be placed into a vegetative index (VI), i.e., combinations of wavebands related to a specific biophysical characteristic. These VIs can express differences in plant response to their soil, meteorological, or management environment and could then be used to determine how the crop could be managed to enhance its productivity. In the past decade, there has been an expanded use of machine learning to determine how remote sensing can be used more effectively in decision-making. The application of artificial intelligence into the dynamics of agriculture will provide new opportunities for how we can utilize the information we have available more effectively. This can lead to linkages with robotic systems capable of being directed to specific areas of a field, an orchard, a pasture, or a vineyard to correct a problem. Our challenge will be to develop and evaluate these relationships so they will provide a benefit to our food security and environmental quality.

This study examines the rainfall-induced change in soil microroughness of a bare smooth soil surface in an agricultural field. The majority of soil microroughness studies have focused on surface roughness on the order of ∼ 5–50 mm and have reported a decay of soil surface roughness with rainfall. However, there is quantitative evidence from a few studies suggesting that surfaces with microroughness less than 5 mm may undergo an increase in roughness when subject to rainfall action. The focus herein is on initial microroughness length scales on the order of 2 mm, a low roughness condition observed seasonally in some landscapes under bare conditions and chosen to systematically examine the increasing roughness phenomenon. Three rainfall intensities of 30, 60, and 75 mm h⁻¹ are applied to a smoothened bed surface in a field plot via a rainfall simulator. Soil surface microroughness is recorded via a surface-profile laser scanner. Several indices are utilized to quantify the soil surface microroughness, namely the random roughness (RR) index, the crossover length, the variance scale from the Markov–Gaussian model, and the limiting difference. Findings show a consistent increase in roughness under the action of rainfall, with an overall agreement between all indices in terms of trend and magnitude. Although this study is limited to a narrow range of rainfall and soil conditions, the results suggest that the outcome of the interaction between rainfall and a soil surface can be different for smooth and rough surfaces and thus warrant the need for a better understanding of this interaction.

The role of tillage practices on soil aggregate properties has been mainly addressed at the pedon scale (i.e., soilscape scale) by treating landscape elements as disconnected. However, there is observed heterogeneity in aggregate properties along flowpaths, suggesting that landscape scale hydraulic processes are also important. This study examines this supposition using field, laboratory and modeling analysis to assess aggregate size and stability along flowpaths under different management conditions: (1) tillage-induced abrasion effects on aggregate size were evaluated with the dry mean weight diameter (DMWD); (2) raindrop impact effects were evaluated with small macroaggregate stability (SMAGG_STAB) using rainfall simulators; and (3) these aggregate proxies were studied in the context of connectivity through the excess bed shear stress (δ), quantified using a physically-based landscape model. DMWD and SMAGG_STAB decreased along the flowpaths for all managements, and a negative correspondence between the proxies and δ was observed. δ captured roughness effects on connectivity along the flowpaths: highest connectivity was noted for parallel-ridge-till flowpaths, where δ ranged from 0–8.2 Pa, and lowest connectivity for contour-ridge-till flowpaths, where δ ranged from 0–1.1 Pa. High tillage intensity likely led to an increase in aggregate susceptibility to hydraulic forcing, reflected in the higher gradients of aggregate size and stability trendlines with respect to δ. Finally, a linear relationship between DMWD and SMAGG_STAB was established.

This study aimed to better understand how tillage row orientation with respect to dominant flow‐pathway along hillslope impacts runoff and the transport of different sediment size fractions. Experimental plots were constructed in contour ridge till (CRT) and parallel ridge till (PRT) sites to monitor runoff and sediment fluxes. Particle size fractions of the sediment, along with organic C and N contents were measured to quantify physical and chemical enrichment ratios for the two tillage orientations. In the CRT plot, tillage produced large oriented roughness elements along the contours, which acted as little check dams, while in the PRT site, the roughness elements helped confine and concentrate the runoff. In the CRT, the “check dams” resulted in a runoff coefficient of .03, while in the PRT, the flow confinement between rows produced a runoff coefficient of .82. Moreover, the erosion rates at the CRT site were 97% less than those in the PRT plot. Large contours produced finer sediment fractions due to the selective sorting in ponded furrows. More aggregate sediment fractions were present in the PRT site, which was dominated by rill erosion. Physical enrichment revealed selective entrainment of finer sediment particles during erosion. Finer‐grain particles with higher specific surface areas, attached more organic C resulting in chemical enrichment. Physical and chemical enrichment methods were found to be in good agreement. These findings suggest transport models that can simulate size fraction updates to the soil active layer can be used to estimate SOC redistribution and hence more accurate C budgets.

The Midwestern US is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.) production, and the carbon dynamics of this region are dominated by these production systems. An accurate regional estimate of gross primary production (GPP) is imperative and requires upscaling approaches. The aim of this study was to upscale corn and soybean GPP (referred to as GPP_calc) in four counties in Central Iowa in the 2016 growing season (DOY 145–269). Eight eddy-covariance (EC) stations recorded carbon dioxide fluxes of corn (n = 4) and soybean (n = 4), and net ecosystem production (NEP) was partitioned into GPP and ecosystem respiration (RE). Additional field-measured NDVI was used to calculate radiation use efficiency (RUE_max). GPP_calc was calculated using 16 MODIS satellite images, ground-based RUE_max and meteorological data, and improved land use maps. Seasonal NEP, GPP, and RE ( $\overline{x}$ ± SE) were 678 ± 63, 1483 ± 100, and −805 ± 40 g C m⁻² for corn, and 263 ± 40, 811 ± 53, and −548 ± 14 g C m⁻² for soybean, respectively. Field-measured NDVI aligned well with MODIS fPAR (R² = 0.99), and the calculated RUE_max was 3.24 and 1.90 g C MJ⁻¹ for corn and soybean, respectively. The GPP_calc vs. EC-derived GPP had a RMSE of 2.24 and 2.81 g C m⁻² d⁻¹, for corn and soybean, respectively, which is an improvement to the GPP_MODIS product (2.44 and 3.30 g C m⁻² d⁻¹, respectively). Corn yield, calculated from GPP_calc (12.82 ± 0.65 Mg ha⁻¹), corresponded well to official yield data (13.09 ± 0.09 Mg ha⁻¹), while soybean yield was overestimated (6.73 ± 0.27 vs. 4.03 ± 0.04 Mg ha⁻¹). The approach presented has the potential to increase the accuracy of regional corn and soybean GPP and grain yield estimates by integrating field-based flux estimates with remote sensing reflectance observations and high-resolution land use maps.

Raindrop impact derives from the kinetic energy of falling raindrops. Determining the kinetic energy of rainfall requires the size distribution and terminal velocity of raindrops, which necessitates complex instrumentation. To avoid this, empirical relations have been developed that relate rainfall intensity and the rate of kinetic energy, i.e., time‐specific kinetic energy (KE_time). In this study, a dynamic rain gauge system (DRGS) was used to quantify the KE_time generated by a rainfall simulator without need of measuring raindrop size distributions or impact velocities. In a series of 10 rainfall tests, the KE_time and rainfall intensity were 860.9 (±88.6) J m² h⁻¹ and 72.1 (±1.9) mm h⁻¹, respectively. Estimated KE_time was found to agree well with the power‐law relation presented by Petrů and Kalibová for high‐intensity simulated rainfall, which are the conditions when higher deviations occur. The DRGS may be a useful tool in quantifying the KE_time of rainfall simulators in hopes to better understand raindrop impact mechanisms.