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Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data. Core Ideas: High‐density soil moisture sampling was conducted in agricultural fields.Time‐invariant spatial structures of within‐field soil moisture were evaluated.Strong but field‐specific relationships between soil moisture and optical remote sensing were found.A pathway for remote sensing based high‐resolution soil moisture estimation was proposed. [ABSTRACT FROM AUTHOR]

Cover crops have been reported as one of the most effective practices to increase soil organic carbon (SOC) for agroecosystems. Impacts of cover crops on SOC change vary depending on soil properties, climate, and management practices, but it remains unclear how these control factors affect SOC benefits from cover crops, as well as which management practices can maximize SOC benefits. To address these questions, we used an advanced process‐based agroecosystem model, ecosys, to assess the impacts of winter cover cropping on SOC accumulation under different environmental and management conditions. We aimed to answer the following questions: (1) To what extent do cover crops benefit SOC accumulation, and how do SOC benefits from cover crops vary with different factors (i.e., initial soil properties, cover crop types, climate during the cover crop growth period, and cover crop planting and terminating time)? (2) How can we enhance SOC benefits from cover crops under different cover crop management options? Specifically, we first calibrated and validated the ecosys model at two long‐term field experiment sites with SOC measurements in Illinois. We then applied the ecosys model to six cover crop field experiment sites spanning across Illinois to assess the impacts of different factors on SOC accumulation. Our modeling results revealed the following findings: (1) Growing cover crops can bring SOC benefits by 0.33 ± 0.06 MgC ha−1 year−1 in six cover crop field experiment sites across Illinois, and the SOC benefits are species specific to legume and non‐legume cover crops. (2) Initial SOC stocks and clay contents had overall small influences on SOC benefits from cover crops. During the cover crop growth period (i.e., winter and spring in the US Midwest), high temperature increased SOC benefits from cover crops, while the impacts from larger precipitation on SOC benefits varied field by field. (3) The SOC benefits from cover crops can be maximized by optimizing cover crop management practices (e.g., selecting cover crop types and controlling cover crop growth period) for the US Midwestern maize–soybean rotation system. Finally, we discussed the economic and policy implications of adopting cover crops in the US Midwest, including that current economic incentives to grow cover crops may not be sufficient to cover costs. This study systematically assessed cover crop impacts for SOC change in the US Midwest context, while also demonstrating that the ecosys model, with rigorous validation using field experiment data, can be an effective tool to guide the adaptive management of cover crops and quantify SOC benefits from cover crops. The study thus provides practical tools and insights for practitioners and policy‐makers to design cover crop related government agricultural policies and incentive programs for farmers and agri‐food related industries. [ABSTRACT FROM AUTHOR]

Climate change is increasing the frequency and intensity of heavy precipitation in the US Midwest, overwhelming existing tile drainage, and resulting in temporary soil ponding across the landscape. However, lack of direct observations of the dynamics of temporal soil ponding limits our understanding of its impacts on crop growth and biogeochemical cycling. Satellite remote sensing offers a unique opportunity to observe and analyze this dynamic phenomenon at the landscape scale. Here we analyzed a series of red–green–blue (RGB) and near infrared (NIR) remote sensing images from the Planet Labs CubeSat constellation following a period of heavy precipitation in May 2017 to determine the spatiotemporal characteristics of ponding events in the maize–soybean cropland of Champaign County, Illinois USA. We trained Random Forest algorithms for near-daily images to create binary classifications of surface water versus none, which achieved kappa values around 0.9. We then analyzed the morphology of classification results for connected pixels across space and time and found that 2.5% (5180 ha) of this cropland was classified as water surface at some point during this period. The frequency distribution of areal ponding extent exhibited a log–log relationship; the mean and median areas of ponds were 1231 m2 and 126 m2, respectively, with 26.1% of identified ponds being at the minimum threshold area of 45 m2, and 2.5% of the ponds having an area greater than 104 m2 (1 ha). Ponds lasted for a mean duration of 2.4 ± 1.7 days, and 2.3% of ponds lasted for more than a week. Our results suggest that transient ponding may be significant at the landscape scale and ought to be considered in assessments of crop risk, soil and water conservation, biogeochemistry, and sustainability. [ABSTRACT FROM AUTHOR]

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5 m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented "ground truth" data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi "ground truth" data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R2 = 0.22, relative RMSE = 32.09%). We also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales. [Display omitted] • A scalable framework to detect field-level crop residue cover from sensing was developed. • Cross-scale framework integrates proximal, airborne, and satellite sensing to detect residue cover. • ResNet-50 and image segmentation facilitate efficient labeling of residue cover from field photos. • Airborne hyperspectral sensing can be deployed as "ground truth" collectors for tillage practices. • Shortwave infrared reflectance in Sentinel-2 and Landsat is vital to quantify crop residue cover. [ABSTRACT FROM AUTHOR]

• Non-legume cover crops cause 3.9 % yield penalty for maize, no penalty for soybean. • Legume cover crops have no significant impacts on maize and soybean yield. • Pathways for yield penalty are N and O 2 deficiency, water stress and cooler soil. • Optimizing cover crop growth windows could maximize the benefit from cover crops. • The ecosys model is an effective tool to quantify cover crop impacts on crop yield. Growing cover crops is one of the most promising conservation practices with multiple benefits. However, the impacts of cover crops on the productivity of the maize-soybean [ Zea mays L. - Glycine max (L.) Merr.] rotation system in the U.S. Midwest still have large uncertainties based on results obtained from field experiments, specifically across different soil properties, climate conditions, and land management practices. Process-based models fully validated with data from field experiments across these diverse conditions provide an effective tool to quantify cover crop impacts on cash crop productivity and optimize the management of cover crops accordingly. In this study, we aim to answer the following questions: (1) What are the impacts of cover crops on cash crop yield in the U.S. Midwest agroecosystems? (2) What are the mechanistic pathways of cover crop to affect cash crop yield (e.g. through influencing soil water, nitrogen and O 2 dynamics)? (3) What management practices can be used to mitigate the negative impacts on cash crop yield caused by cover crops? To address these questions, we calibrated and validated a sophisticated process-based agroecosystem model, ecosys , using field experimental data from 2013 to 2018 across Illinois, and then used ecosys to assess the impacts of winter cover crops on maize and soybean yield under different management practices. Our study revealed the following findings: (1) planting non-legume cover crops can cause 3.9 ± 3% yield reduction for maize and no significant impacts on soybean yield, while planting legume cover crops has no significant impact on the yield of either maize or soybean; (2) the maize yield reduction caused by planting cover crops can be mainly explained by nitrogen deficiency induced by increased immobilization, water competition in dry areas, cooler soil surface and oxygen competition; (3) later termination of non-legume cover crops before maize can result in larger maize yield loss due to intensive competition for resources (e.g. water and nitrogen), and the impacts of non-legume cover crops on maize yield reduction can be minimized by optimizing cover crop termination time in the spring. Overall, in the U.S. Midwestern maize-soybean rotation system, we found that although non-legume cover crops cause yield reduction for maize through resource competition, this yield reduction can be minimized through management practices, such as controlling termination time of cover crop and proper fertilizer management. [ABSTRACT FROM AUTHOR]

Traditional gas exchange measurements are cumbersome, which makes it difficult to capture variation in biochemical parameters, namely the maximum rate of carboxylation measured at a reference temperature (V cmax25 ) and the maximum electron transport at a reference temperature (J max25 ), in response to growth temperature over time from days to weeks. Hyperspectral reflectance provides reliable measures of V cmax25 and J max25 ; however, the capability of this method to capture biochemical acclimations of the two parameters to high growth temperature over time has not been demonstrated. In this study, V cmax25 and J max25 were measured over multiple growth stages during two growing seasons for field-grown soybeans using both gas exchange techniques and leaf spectral reflectance under ambient and four elevated canopy temperature treatments (ambient+1.5, +3, +4.5, and +6°C). Spectral vegetation indices and machine learning methods were used to build predictive models for V cmax25 and J max25 , based on the leaf reflectance. Results showed that these models yielded an R 2 of 0.57-0.65 and 0.48-0.58 for V cmax25 and J max25 , respectively. Hyperspectral reflectance captured biochemical acclimation of leaf photosynthesis to high temperature in the field, improving spatial and temporal resolution in the ability to assess the impact of future warming on crop productivity., (© 2021 John Wiley & Sons Ltd.)