Cover crops are gaining traction in many agricultural regions, partly driven by increased public subsidies and by private markets for ecosystem services. These payments are motivated by environmental benefits, including improved soil health, reduced erosion, and increased soil organic carbon. However, previous work based on experimental plots or crop modeling indicates cover crops may reduce crop yields. It remains unclear, though, how recent cover crop adoption has affected productivity in commercial agricultural systems. Here we perform the first large‐scale, field‐level analysis of observed yield impacts from cover cropping as implemented across the US Corn Belt. We use validated satellite data products at sub‐field scales to analyze maize and soybean yield outcomes for over 90,000 fields in 2019–2020. Because we lack data on cover crop species or timing, we seek to quantify the yield impacts of cover cropping as currently practiced in aggregate. Using causal forests analysis, we estimate an average maize yield loss of 5.5% on fields where cover crops were used for 3 or more years, compared with fields that did not adopt cover cropping. Maize yield losses were larger on fields with better soil ratings, cooler mid‐season temperatures, and lower spring rainfall. For soybeans, average yield losses were 3.5%, with larger impacts on fields with warmer June temperatures, lower spring and late‐season rainfall, and, to a lesser extent, better soils. Estimated impacts are consistent with multiple mechanisms indicated by experimental and simulation‐based studies, including the effects of cover crops on nitrogen dynamics, water consumption, and soil oxygen depletion. Our results suggest a need to improve cover crop management to reduce yield penalties, and a potential need to target subsidies based on likely yield impacts. Ultimately, avoiding substantial yield penalties is important for realizing widespread adoption and associated benefits for water quality, erosion, soil carbon, and greenhouse gas emissions.

Cover crops have critical significance for agroecosystem sustainability and have long been promoted in the U.S. Midwest. Knowledge of cover cropping variations and impacts of government policies remains very limited. We developed an accurate and cost‐effective approach utilizing satellite fusion data, environmental variables, and machine learning to quantify cover cropping in corn and soybean fields from 2000 to 2021 in the U.S. Midwest. We found that cover crop adoption in most counties was stagnant from 2000 to 2011, but has significantly increased from 2011 to 2021. The adoption of 2021 is four times that of 2011, which was highly correlated to the funding for conservation programs. However, the percentage of cover crop adoption in the U.S. Midwest is still low (7.2%). Our work fills a critical gap in quantifying long‐term field‐level cover crop adoption at large regions and highlights the potential importance of incentive programs to promote sustainable agricultural practices.

Coastal water regions represent some of the most fragile ecosystems, exposed to both climate change and human activities. While remote sensing provides unprecedented amounts of data for water quality monitoring on regional to global scales, the performance of satellite observations is frequently impeded by revisiting intervals and unfavorable conditions, such as cloud coverage and sun glint. Therefore, it is crucial to evaluate the impacts of varied sampling strategies (time and frequency) and insufficient observations on the monitoring of short-term and long-term tendencies of water quality parameters, such as suspended solids (SS), in highly dynamic coastal waters. Taking advantage of the first high-frequency in situ SS dataset (at 30 min sampling intervals from 2007 to 2008), collected in Deep Bay, China, this paper presents a quantitative analysis of the influences of sampling strategies on the monitoring of SS, in terms of sampling frequency and time of day. Dramatic variations of SS were observed, with standard deviation coefficients of 48.9% and 54.1%, at two fixed stations; in addition, significant uncertainties were revealed, with the average absolute percent difference of approximately 13%, related to sampling frequency and time, using nonlinear optimization and random simulation methods. For a sampling frequency of less than two observations per day, the relative error of SS was higher than 50%, and stabilized at approximately 10%, when at least four or five samplings were conducted per day. The optimal recommended sampling times for SS were at around 9:00, 12:00, 14:00, and 16:00 in Deep Bay. The “pseudo” MODIS SS dataset was obtained from high-frequency in situ SS measurements at 10:30 and 14:00, masked by the temporal gap distribution of MODIS coverage to avoid uncertainties propagated from atmospheric correction and SS models. Noteworthy uncertainties of daily observations from the Terra/Aqua MODIS were found, with mean relative errors of 19.2% and 17.8%, respectively, whereas at the monthly level, the mean relative error of Terra/Aqua MODIS observations was approximately 10.7% (standard deviation of 8.4%). Sensitivity analysis between MODIS coverage and SS relative errors indicated that temporal coverage (the percentage of valid MODIS observations for a month) of more than 70% is required to obtain high-precision SS measurements at a 5% error level. Furthermore, approximately 20% of relative errors were found with the coverage of 30%, which was the average coverage of satellite observations over global coastal waters. These results highlight the need for high-frequency measurements of geostationary satellites like GOCI and multi-source ocean color sensors to capture the dynamic process of coastal waters in both the short and long term.

Suspended sediment (SS) is an important water quality indicator of coastal and estuarine ecosystems. Field measurement and satellite remote sensing are the most common approaches for water quality monitoring. However, the efficiency and precision of both methods are typically affected by their sampling strategy (time and interval), especially in highly dynamic coastal and estuarine waters, because only limited measurements are available to analyze the short-term variations or the long-term trends of SS. Dramatic variations of SS were observed, with standard deviation coefficients of 48.9% and 54.1%, at two fixed stations in Deep Bay, China. Therefore, it is crucial to resolve the temporal variations of SS and its main influencing factors, and thus to develop an improved sampling strategy for estuarine ecosystems. Based on two years of continuous high-frequency measurements of SS and concurrent tidal and meteorological data, we demonstrated that the tide is the dominant factor influencing the SS variation among tide, wind (speed and direction), and rainfall in Deep Bay, China. For the monitoring of maximum suspended sediment concentration (SSC), the recommended optimum sampling time coincides with the occurrence of the ebb tides, whereas multiple sampling times are recommended for monitoring of minimum SSC. Although variations of SS are also affected by other factors, the recommended sampling strategy could capture the maximum and minimum SSC variations exactly more than 85% days in a year on average in Deep Bay. This study provides a baseline of SS variation and direct sampling strategy guidance for future SS monitoring and could be extended to other coastal or estuarine waters with similar climatological/tidal exposures.