Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r2 = 0.38). Adding hydrological variables reduced the error to 15.3% (r2 = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r2 = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r2 = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency.

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

Where Plato's Republic argues that women are similar if inferior and thus should be able to perform all roles in society, Xenophon's Oeconomicus appears to argue that women are dissimilar and should work within the home, though their work is of equal importance to that of men in supporting the household. A wider reading of Xenophon's works suggests that women's natures have more in common with men's and that they can play a wider range of roles.

AbstractVegetation health forecasting (NDVI as a proxy) informs decision‐makers about the end of season crop yield productivity but is not well‐documented. This study tests improvements in vegetation health forecasting by developing a data‐driven Dynamic Agricultural Productivity Indicator (DAPI), which simultaneously incorporates satellite‐based root zone soil moisture (RZSM) and satellite‐based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in the CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology‐based NDVI forecasts, with the largest improvements in water‐limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food‐insecure areas, predicting crop price changes, and projecting expected commodities market trends.

Introduction The July 23, 1972 launch of the Earth Resources Technology Satellite (ERTS), later renamed Landsat 1, marks the first milepost of the Landsat journey. While the inception of the Landsat program began several years earlier (Goward et al. 2017), that first launch and the subsequent launches of eight more Landsat satellites define a timeline that leads us to celebrate the 50th anniversary of an ongoing satellite program with profound impacts on our observation and understanding of the Earth. Prior histories have addressed technical, programmatic, and scientific aspects of the Landsat program in a more-or-less chronological order (Mack 1990; Goward et al. 2017). Here, we briefly address the cumulative impacts of the Landsat program on key Earth science and environmental topics.

The 14,582 km2 Neuse River Basin in North Carolina was characterized based on a user-defined land-cover (LC) classification system developed specifically to support spatially explicit, non-point source nitrogen allocation modeling studies. Data processing incorporated both spectral and GIS rule-based analytical techniques using multiple-date SPOT 4 (XS), Landsat 7(ETM+), and ancillary data sources. Unique LC classification elements included the identification of urban classes based on impervious surfaces and specific row crop type identifications. Individual pixels were aggregated to produce variable minimum mapping units or landscape “patches” corresponding to both riparian buffer zones (0.1 ha), and general watershed areas (0.4 ha). An accuracy assessment was performed using reference data derived from in situ field measurements and required to support these spatially explicit modeling approaches, imagery (camera) data. Multiple data interpretations were used to develop a reference database with known data variability to support a quantitative accuracy assessment of LC classification results. Confusion matrices were constructed to incorporate the variability of the reference data directly in the accuracy assessment process. Accuracies were reported for hierarchal classification levels with overall Level 1 classification accuracy of 82% (n = 825) for general watershed areas, and 73% (n = 391) for riparian buffer zone locations. A Kappa Test Z statistic of 3.3 indicated a significant difference between the two results. Classes that performed poorly were largely associated with the confusion of herbaceous classes in both urban and agricultural areas.

Since 1972, the Landsat program has been continually monitoring the Earth, to now provide 50 years of digital, multispectral, medium spatial resolution observations. Over this time, Landsat data were crucial for many scientific and technical advances. Prior to the Landsat program, detailed, synoptic depictions of the Earth's surface were rare, and the ability to acquire and work with large datasets was limited. The early years of the Landsat program delivered a series of technological breakthroughs, pioneering new methods, and demonstrating the ability and capacity of digital satellite imagery, creating a template for other global Earth observation missions and programs. Innovations driven by the Landsat program have paved the way for subsequent science, application, and policy support activities. The economic and scientific value of the knowledge gained through the Landsat program has been long recognized, and despite periods of funding uncertainty, has resulted in the program's 50 years of continuity, as well as substantive and ongoing improvements to payload and mission performance. Free and open access to Landsat data, enacted in 2008, was unprecedented for medium spatial resolution Earth observation data and substantially increased usage and led to a proliferation of science and application opportunities. Here, we highlight key developments over the past 50 years of the Landsat program that have influenced and changed our scientific understanding of the Earth system. Major scientific and programmatic impacts have been realized in the areas of agricultural crop mapping and water use, climate change drivers and impacts, ecosystems and land cover monitoring, and mapping the changing human footprint. The introduction of Landsat collection processing, coupled with the free and open data policy, facilitated a transition in Landsat data usage away from single images and towards time series analyses over large areas and has fostered the widespread use of science-grade data. The launch of Landsat-9 on September 27, 2021, and the advanced planning of its successor mission, Landsat-Next, underscore the sustained institutional support for the program. Such support and commitment to continuity is recognition of both the historic impact the program, and the future potential to build upon Landsat's remarkable 50-year legacy.

Machine learning methods are increasingly used in analyzing remotely sensed data and studying different aspects of agricultural production. In particular, several of these flexible models are widely adopted to predict regional crop yield during or after the growing season. However, most existing models cannot be applied when dealing with functional covariates. In this paper, an approach based on multidimensional scaling is proposed to generate a set of artificial covariates from empirical density functions of different phenomena captured within specific administrative boundaries through satellites. In contrast to traditional aggregation methods, this approach is designed to reduce the loss of information associated with the use of summary statistics as covariates. The proposed methodology is applied to NASA remote sensing data, combined with information from surveys and USDA's end-of-season county estimates, to study the prediction accuracy of different crop-yield models for three major crops in North Dakota.

Phosphoinositide 3-kinases (PI3Ks) are a family of enzymes that control a wide variety of cellular functions such as cell growth, proliferation, differentiation, motility, survival, and intracellular trafficking. PI3Kγ plays a critical role in mediating leukocyte chemotaxis as well as mast cell degranulation, making it a potentially interesting target for autoimmune and inflammatory diseases. We previously disclosed a novel series of PI3Kγ inhibitors derived from a benzothiazole core. The truncation of the benzothiazole core led to the discovery of a structurally diverse alkynyl thiazole series which displayed high PI3Kγ potency and subtype selectivity. Further medicinal chemistry optimization of the alkynyl thiazole series led to identification of compounds such as 14 and 32, highly potent, subtype selective, and CNS penetrant PI3Kγ inhibitors. Compound 14 showed robust inhibition of PI3Kγ mediated neutrophil migration in vivo.

Crop yield monitoring is an important component in agricultural assessment. Multi-spectral remote sensing instruments onboard space-borne platforms such as Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) have shown to be useful for efficiently generating timely and synoptic information on the yield status of crops across regional levels. However, the coarse spatial resolution data inherent to these sensors provides little utility at the management level. Recent satellite imagery collection advances toward finer spatial resolution (down to 1 m) alongside increased observational cadence (near daily) implies information on crops obtainable at field and within-field scales to support farming needs is now possible. To test this premise, we focus on assessing the efficiency of multiple satellite sensors, namely WorldView-3, Planet/Dove-Classic, Sentinel-2, and Landsat 8 (through Harmonized Landsat Sentinel-2 (HLS)), and investigate their spatial, spectral (surface reflectance (SR) and vegetation indices (VIs)), and temporal characteristics to estimate corn and soybean yields at sub-field scales within study sites in the US state of Iowa. Precision yield data as referenced to combine harvesters’ GPS systems were used for validation. We show that imagery spatial resolution of 3 m is critical to explaining 100% of the within-field yield variability for corn and soybean. Our simulation results show that moving to coarser resolution data of 10 m, 20 m, and 30 m reduced the explained variability to 86%, 72%, and 59%, respectively. We show that the most important spectral bands explaining yield variability were green (0.560 μm), red-edge (0.726 μm), and near-infrared (NIR − 0.865 μm). Furthermore, the high temporal frequency of Planet and a combination of Sentinel-2/Landsat 8 (HLS) data allowed for optimal date selection for yield map generation. Overall, we observed mixed performance of satellite-derived models with the coefficient of determination (R2) varying from 0.21 to 0.88 (averaging 0.56) for the 30 m HLS and from 0.09 to 0.77 (averaging 0.30) for 3 m Planet. R2 was lower for fields with higher yields, suggesting saturation of the satellite-collected reflectance features in those cases. Therefore, other biophysical variables, such as soil moisture and evapotranspiration, at similar fine spatial resolutions are likely needed alongside the optical imagery to fully explain the yields.

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

Soil moisture deficiency is a major factor in determining crop yields in water-limited agricultural production regions. Evapotranspiration (ET), which consists of crop water use through transpiration and water loss through direct soil evaporation, is a good indicator of soil moisture availability and vegetation health. ET therefore has been an integral part of many yield estimation efforts. The Evaporative Stress Index (ESI) is an ET-based crop stress indicator that describes temporal anomalies in a normalized evapotranspiration metric as derived from satellite remote sensing. ESI has demonstrated the capacity to explain regional yield variability in water-limited regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to interannual phenological variability. This investigation selected three study sites across the U.S. Corn Belt – Mead, NE, Ames, IA and Champaign, IL – to investigate the potential operational value of 30-m resolution, phenologically corrected ESI datasets for yield prediction. The analysis was conducted over an 8-year period from 2010 to 2017, which included both drought and pluvial conditions as well as a broad range in yield values. Detrended yield anomalies for corn and soybean were correlated with ESI computed using annual ET curves temporally aligned based on (1) calendar date, (2) crop emergence date, and (3) a growing degree day (GDD) scaled time axis. Results showed that ESI has good correlations with yield anomalies at the county scale and that phenological corrections to the annual temporal alignment of the ET timeseries improve the correlation, especially when the time axis is defined by GDD rather than the calendar date. Peak correlations occur in the silking stage for corn and the reproductive stage for soybean – phases when these crops are particularly sensitive to soil moisture deficiencies. Regression equations derived at the time of peak correlation were used to estimate yields at county scale using a leave-one-out cross-validation strategy. The ESI-based yield estimates agree well with the USDA National Agricultural Statistics Service (NASS) county-level crop yield data, with correlation coefficients ranging from 0.79 to 0.93 and percent root-mean-square errors of 5–8%. These results demonstrate that remotely sensed ET at high spatiotemporal resolution can convey valuable water stress information for forecasting crop yields across the Corn Belt if interannual phenological variability is considered.

Crop type maps were created without the traditional need for in-season training data across the Corn Belt and Great Plains regions of the United States. This was accomplished through machine learning of historical land cover information, paired with a time-series of multi-spectral satellite imagery composites spanning the growing season, to develop rulesets, which were used for real-time prediction in the current year. Specifically, a decade's worth of annual 30 m resolution crop specific maps, known as the Cropland Data Layer (CDL), provided the foundation, and prior and current year's satellite imagery from Landsat 7 and 8 and Sentinel-2a and -2b built upon it. Four modeling scenarios, all using random forests, were performed to understand the crop mapping abilities of the datasets independently and combined. They were 1) use of CDLs only (i.e. prediction based solely on crop rotation history) 2) use of Landsat 7 and 8 bottom-of-atmosphere surface reflectance imagery only, 3) use of Sentinel-2a and -2b top-of-atmosphere imagery only, and 4) integration of the CDL, Landsat, and Sentinel-2 information together in a unified effort. Furthermore, the model runs were generated monthly, beginning in April, through the growing season to provide understanding of classification performance as a function of time. The 2020 crop year, relatively normal in terms of planting and weather, was used for the test. Accuracy statistics were generated by randomly sampling 50 counties and comparing those classification outputs to the actual 2020 CDL. Pixel-level results showed that prediction by midsummer using only the CDL information provided a crop type map with corn and soybean consumer and producer agreement above 70% and winter wheat just below 50%. The early season imagery-based classifications were markedly worse. However, as Landsat or Sentinel-2 imagery accumulated through July, those classifications became significantly better than those reliant on the use of the CDL information only. Ultimately, the very best crop maps resulted from integrating the CDLs with a full season's worth of Landsat and Sentinel-2 imagery. At that point in late September, the corn and soybean agreements were around 85% and winter wheat near 70%. All analysis was performed within Google Earth Engine cloud-based public imagery repository and high-performance computing system. The classification outputs provide practitioners with US crop type maps in near real-time.

“Self-Mastery, Piety, and Reciprocity in Xenophon’s Ethics” focuses on the following aspects of Xenophon’s ethics. Xenophon’s interest in leadership makes ethics a central concern across his wide-ranging body of work. The foundation of virtue, for Xenophon, is enkrateia (self-mastery), which he believed could be squared with Socratic intellectualism as it was required both for the acquisition of knowledge and for the successful application of knowledge in the face of non-intellectual drives. Xenophon’s famous piety is also of ethical import, as he argues that the gods designed the world to our benefit, benefit the pious through divination, and established unwritten laws which should regulate our conduct. Among those laws is one rewarding reciprocity, which is the central factor in successful interpersonal relationships and friendship. Xenophon, despite his emphasis on self-mastery, believed that the best life was also the most pleasant life, though he also distinguished between pleasures.

Formal planning and development of what became the first Landsat satellite commenced over 50 years ago in 1967. Now, having collected earth observation data for well over four decades since the 1972 launch of Landsat1, the Landsat program is increasingly complex and vibrant. Critical programmatic elements are ensuring the continuity of high quality measurements for scientific and operational investigations, including ground systems, acquisition planning, data archiving and management, and provision of analysis ready data products. Free and open access to archival and new imagery has resulted in a myriad of innovative applications and novel scientific insights. The planning of future compatible satellites in the Landsat series, which maintain continuity while incorporating technological advancements, has resulted in an increased operational use of Landsat data. Governments and international agencies, among others, can now build an expectation of Landsat data into a given operational data stream. International programs and conventions (e.g., deforestation monitoring, climate change mitigation) are empowered by access to systematically collected and calibrated data with expected future continuity further contributing to the existing multi-decadal record. The increased breadth and depth of Landsat science and applications have accelerated following the launch of Landsat-8, with significant improvements in data quality. Herein, we describe the programmatic developments and institutional context for the Landsat program and the unique ability of Landsat to meet the needs of national and international programs. We then present the key trends in Landsat science that underpin many of the recent scientific and application developments and followup with more detailed thematically organized summaries. The historical context offered by archival imagery combined with new imagery allows for the development of time series algorithms that can produce information on trends and dynamics. Landsat-8 has figured prominently in these recent developments, as has the improved understanding and calibration of historical data. Following the communication of the state of Landsat science, an outlook for future launches and envisioned programmatic developments are presented. Increased linkages between satellite programs are also made possible through an expectation of future mission continuity, such as developing a virtual constellation with Sentinel-2. Successful science and applications developments create a positive feedback loop-justifying and encouraging current and future programmatic support for Landsat.