Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform ( https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

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Elevated nutrient concentrations in agricultural runoff contribute to seasonal eutrophication and hypoxia in the lower portion of the San Joaquin River, California. Interception and filtration of agricultural runoff by constructed wetlands may improve water quality of return flows ultimately destined for major water bodies. This study evaluated the efficacy of two small flow-through wetlands (2.3 and 7.3 ha; hydraulic residence time = 11 and 31 h) for attenuating various forms of P from irrigation tailwaters during the 2005 irrigation season (May to September). Our goal was to examine transformations and removal efficiencies for bioavailable P in constructed wetlands. Inflow and outflow water volumes were monitored continuously and weekly water samples were collected to measure total P (TP), dissolved-reactive P (DRP), and bioavailable P (BAP). Suspended sediment was characterized and fractionated into five operationally-defined P fractions (i.e., NH4Cl, bicarbonate-dithionite, NaOH, HCl, residual) to evaluate particulate P (PP) transformations. DRP was the major source of BAP with the particulate fraction contributing from 11 to 26%. On a seasonal basis, wetlands removed 55 to 65% of PP, 61 to 63% of DRP, 57 to 62% of BAP, and 88 to 91% of TSS. Sequential fractionation indicated that the bioavailable fraction of PP was largely associated with clay-sized particles that remain in suspension, while less labile P forms preferentially settle with coarser sediment. Thus, removal of potentially bioavailable PP is dependent on factors that promote particle settling and allow for the removal of colloids. This study suggests that treatment of tailwaters in small, flow-through wetlands can effectively remove BAP. Wetland design and management strategies that enhance sedimentation of colloids can improve BAP retention efficiency.

1. Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage, and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision making at multiple scales.2. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on-the-ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter, and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatially-explicit, pixel-level estimates of uncertainty. We evaluated the model with 5,780 on-the-ground vegetation plots removed from the training data.3. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Model performance increased across all functional groups compared to the previously produced fractional product. 4. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel-level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on-the-ground data, expert knowledge, land use history, scientific literature, and other sources of information when making interpretations. When being used to inform decision-making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.