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Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake's watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near-stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near-stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake-specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality., (© 2020 by the Ecological Society of America.)

Although remote sensing of lake surface water temperature has been ongoing for decades and has led to important discoveries regarding the warming of lake surface temperature in some regions, the ability to access and use remote sensing data is still primarily limited to remote sensing experts. Effective use of remote sensing data involves technical skills in coding (often in multiple programming languages), application of appropriate atmospheric corrections, and integrating spatially heterogeneous remote sensing data with in situ data obtained at specific geographic locations. To improve access to remote sensing data and broaden the understanding of changes in lake surface water temperature over the past four decades, we created lakeCoSTR (lake Collection 2 Surface Temperature Retrieval), a user‐friendly, cloud‐based script that gives ecologists lacking specialized training in remote sensing the ability to access the Landsat Collection 2 temperature estimates for lakes with a surface area of at least 0.4 ha. Additionally, if in situ data are provided, a paired dataset can be created within the tool. To demonstrate lakeCoSTR, we retrieved surface water temperature data for a lake with a long monitoring history, Lake Sunapee, NH, USA, and compared long‐term surface temperature trends between data obtained via lakeCoSTR and in situ measurements. When compared with Landsat Collection 1 temperature estimates derived from a single‐channel algorithm, the Landsat Collection 2 data from lakeCoSTR required no calibration to in situ data. This suggests that lakeCoSTR can be used to document temporal trends in lakes and to reprocess analyses that relied on Collection 1 data. From 1983 until 2020, remotely sensed surface water temperature estimates at Lake Sunapee increased by 0.07–0.09°C year−1 during July and August, a trend that agreed with long‐term in situ record. As climate change continues to impact freshwater systems, a better understanding of long‐term temperature data will be vital. Remotely sensed data, like those acquired using lakeCoSTR, may provide a window into temperature trends for lakes. [ABSTRACT FROM AUTHOR]