National Agricultural Statistics Service

Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements.

The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) produces the Cropland Data Layer (CDL) product, which is a raster-formatted, geo-referenced, crop-specific, land cover map. CDL program inputs include medium resolution satellite imagery, USDA collected ground truth and other ancillary data, such as the National Land Cover Data set. A decision tree-supervised classification method is used to generate the freely available state-level crop cover classifications and provide crop acreage estimates based upon the CDL and NASS June Agricultural Survey ground truth to the NASS Agricultural Statistics Board. This paper provides an overview of the NASS CDL program. It describes various input data, processing procedures, classification and validation, accuracy assessment, CDL product specifications, dissemination venues and the crop acreage estimation methodology. In general, total crop mapping accuracies for the 2009 CDLs ranged from 85% to 95% for the major crop categories.

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 Landsat-1, 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

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.

A new 1 km global IIASA-IFPRI cropland percentage map for the baseline year 2005 has been developed which integrates a number of individual cropland maps at global to regional to national scales. The individual map products include existing global land cover maps such as GlobCover 2005 and MODIS v.5, regional maps such as AFRICOVER and national maps from mapping agencies and other organizations. The different products are ranked at the national level using crowdsourced data from Geo-Wiki to create a map that reflects the likelihood of cropland. Calibration with national and subnational crop statistics was then undertaken to distribute the cropland within each country and subnational unit. The new IIASA-IFPRI cropland product has been validated using very high-resolution satellite imagery via Geo-Wiki and has an overall accuracy of 82.4%. It has also been compared with the EarthStat cropland product and shows a lower root mean square error on an independent data set collected from Geo-Wiki. The first ever global field size map was produced at the same resolution as the IIASA-IFPRI cropland map based on interpolation of field size data collected via a Geo-Wiki crowdsourcing campaign. A validation exercise of the global field size map revealed satisfactory agreement with control data, particularly given the relatively modest size of the field size data set used to create the map. Both are critical inputs to global agricultural monitoring in the frame of GEOGLAM and will serve the global land modelling and integrated assessment community, in particular for improving land use models that require baseline cropland information. These products are freely available for downloading from the http://cropland.geo-wiki.org website.

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Program (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.

The Earth Surface Mineral Dust Source Investigation, EMIT, is planned to operate from the International Space Station starting no earlier than the fall of 2021. EMIT will use visible to short wavelength infrared imaging spectroscopy to determine the mineral composition of the arid land dust source regions of the Earth to advance our knowledge of the radiative forcing effect of these aerosols. Mineral dust emitted into the atmosphere under high wind conditions is an element of the Earth system with many impacts to the Earth's energy balance, atmosphere, surface, and oceans. The Earth's mineral dust cycle with source, transport, and deposition phases are studied with advanced Earth System Models. Because the chemical composition, optical and surface properties of soil particles vary strongly with the mineral composition of the source, these models require knowledge of surface soil mineral dust source composition to accurately understand dust impacts on the Earth system now and in the future. At present, compositional knowledge of the Earth's mineral dust source regions from existing data sets is uncertain as a result of limited measurements. EMIT will use spectroscopically-derived surface mineral composition to update the prescribed boundary conditions for state-of-the-art Earth System Models. The EMIT-initialized models will be used to investigate the impact of direct radiative forcing in the Earth system that depends strongly on the composition of the mineral dust aerosols emitted into the atmosphere. These new measurements and related products will be used to address the EMIT science objectives and made available to the science community for additional investigations. An overview of the EMIT science, development, and mission is presented in this paper.

Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps.

Summary 1. We propose a model of plant strategies in temperate fluvial hydrosystems that considers the hydraulic and geomorphic features that control plant recruitment, establishment and growth in river floodplains. 2. The model describes first how the disturbance gradient and the grain‐size of the river bed load affect the relative proportion of erosion and deposition processes, and how the frequency of flood disturbance affects the intensity of such processes. 3. Secondly, the model predicts plant strategies according to direct and indirect effects of floods (disturbances through erosion versus deposition processes, and associated nutrient excess or limitation). 4. The relevance of the model as a prediction tool is discussed. Some proposals are made to validate the model, and traits are proposed that should be considered in future research for improving the predicting value of the model.

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.

Personal protection measures against biting arthropods include topical insect repellents, area repellents, insecticide-treated bednets and treated clothing. The literature on the effectiveness of personal protection products against arthropods is mainly limited to studies of prevention of bites, rather than prevention of disease. Tungiasis was successfully controlled by application of topical repellents and scrub typhus was reduced through the use of treated clothing. Successful reduction of leishmaniasis was achieved through the use of topical repellents, treated bednets and treated clothing in individual studies. Malaria has been reduced by the use of insecticide-treated bednets (ITN), certain campaigns involving topical repellents, and the combination of treated bednets and topical repellents. Although area repellents such as mosquito coils are used extensively, their ability to protect humans from vector-transmitted pathogens has not been proven. Taken together, the literature indicates that personal protection measures must be used correctly to be effective. A study that showed successful control of malaria by combining treated bednets and topical repellents suggests that combinations of personal protection measures are likely to be more effective than single methods. Implementation of successful programmes based on personal protection will require a level of cooperation commonly associated with other basic societal functions, such as education and food safety.

The efficacy of fungicides in managing soybean rust was evaluated in 12 environments in South America and southern Africa over three growing seasons from 2002 to 2005. There were differences in final soybean rust severity, defoliation, and yield among the treatments at most locations. In locations where soybean rust was not severe, all the fungicides evaluated reduced severity. In locations where soybean rust was severe, applications of triazole and triazole + strobilurin fungicides resulted in lower severity and higher yields compared with other fungicides. The strobilurin fungicides provided the highest yields in many locations; however, severity tended to be higher than that of the triazole fungicides. There also were differences in yield and severity between the trials with two and three applications of several fungicides, with three applications resulting in less severe soybean rust and higher yields. However, the third application of tebuconazole, tetraconazole, and the mixtures containing azoxystrobin and pyraclostrobin was not needed to maintain yield. These fungicides were among the most effective for managing soybean rust and maintaining yield over most locations.

OBJECTIVE: Resilience is a construct addressed in the psycho-oncology literature and is especially relevant to cancer survivorship. The purpose of this paper is to propose a model for resilience that is specific to adults diagnosed with cancer. METHODS: To establish the proposed model, a brief review of the various definitions of resilience and of the resilience literature in oncology is provided. RESULTS: The proposed model includes baseline attributes (personal and environmental) which impact how an individual responds to an adverse event, which in this paper is cancer-related. The survivor has an initial response that fits somewhere on the distress-resilience continuum; however, post-cancer experiences (and interventions) can modify the initial response through a process of recalibration. CONCLUSIONS: The literature reviewed indicates that resilience is a common response to cancer diagnosis or treatment. The proposed model supports the view of resilience as both an outcome and a dynamic process. Given the process of recalibration, a discussion is provided of interventions that might facilitate resilience in adults with cancer.

The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R2) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1–3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications.

When we estimate the population total for a survey variable or variables, calibration forces the weighted estimates of certain covariates to match known or alternatively estimated population totals called benchmarks. Calibration can be used to correct for sample-survey nonresponse, or for coverage error resulting from frame undercoverage or unit duplication. The quasi-randomization theory supporting its use in nonresponse adjustment treats response as an additional phase of random sampling. The functional form of a quasi-random response model is assumed to be known, its parameter values estimated implicitly through the creation of calibration weights. Unfortunately, calibration depends upon known benchmark totals while the covariates in a plausible model for survey response may not be the benchmark covariates. Moreover, it may be prudent to keep the number of covariates in a response model small. We use calibration to adjust for nonresponse when the benchmark model and covariates may differ, provided the number of the former is at least as great as that of the latter. We discuss the estimation of a total for a vector of survey variables that do not include the benchmark covariates, but that may include some of the model covariates. We show how to measure both the additional asymptotic variance due to the nonresponse in a calibration-weighted estimator and the full asymptotic variance of the estimator itself. All variances are determined with respect to the randomization mechanism used to select the sample, the response model generating the subset of sample respondents, or both. Data from the U.S. National Agricultural Statistical Service's 2002 Census of Agriculture and simulations are used to illustrate alternative adjustments for nonresponse. The paper concludes with some remarks about adjustment for coverage error.

BACKGROUND: Investigation into personal health has become focused on conditions at an increasingly local level, while response rates have declined and complicated the process of collecting data at an individual level. Simultaneously, social media data have exploded in availability and have been shown to correlate with the prevalence of certain health conditions. OBJECTIVE: Facebook likes may be a source of digital data that can complement traditional public health surveillance systems and provide data at a local level. We explored the use of Facebook likes as potential predictors of health outcomes and their behavioral determinants. METHODS: We performed principal components and regression analyses to examine the predictive qualities of Facebook likes with regard to mortality, diseases, and lifestyle behaviors in 214 counties across the United States and 61 of 67 counties in Florida. These results were compared with those obtainable from a demographic model. Health data were obtained from both the 2010 and 2011 Behavioral Risk Factor Surveillance System (BRFSS) and mortality data were obtained from the National Vital Statistics System. RESULTS: Facebook likes added significant value in predicting most examined health outcomes and behaviors even when controlling for age, race, and socioeconomic status, with model fit improvements (adjusted R(2)) of an average of 58% across models for 13 different health-related metrics over basic sociodemographic models. Small area data were not available in sufficient abundance to test the accuracy of the model in estimating health conditions in less populated markets, but initial analysis using data from Florida showed a strong model fit for obesity data (adjusted R(2)=.77). CONCLUSIONS: Facebook likes provide estimates for examined health outcomes and health behaviors that are comparable to those obtained from the BRFSS. Online sources may provide more reliable, timely, and cost-effective county-level data than that obtainable from traditional public health surveillance systems as well as serve as an adjunct to those systems.

SUMMARY: Refractive index measurements were made on the spores and vegetative cells of strains of Bacillus cereus, B. cereus var. mycoides and B. megaterium by phase contrast and interference microscopy with protein immersion. The refractive indices of the spores were found to be very high and comparable with that of dehydrated protein, suggesting that they contained very little water. Those of the vegetative cells were much lower, and indicated a solid content of about 30 %, w/v.

Net annual soil carbon change, fossil fuel emissions from cropland production, and cropland net primary production were estimated and spatially distributed using land cover defined by NASA's moderate resolution imaging spectroradiometer (MODIS) and by the USDA National Agricultural Statistics Service (NASS) cropland data layer (CDL). Spatially resolved estimates of net ecosystem exchange (NEE) and net ecosystem carbon balance (NECB) were developed. The purpose of generating spatial estimates of carbon fluxes, and the primary objective of this research, was to develop a method of carbon accounting that is consistent from field to national scales. NEE represents net on-site vertical fluxes of carbon. NECB represents all on-site and off-site carbon fluxes associated with crop production. Estimates of cropland NEE using moderate resolution (approximately 1 km2) land cover data were generated for the conterminous United States and compared with higher resolution (30-m) estimates of NEE and with direct measurements of CO2 flux from croplands in Illinois and Nebraska, USA. Estimates of NEE using the CDL (30-m resolution) had a higher correlation with eddy covariance flux tower estimates compared with estimates of NEE using MODIS. Estimates of NECB are primarily driven by net soil carbon change, fossil fuel emissions associated with crop production, and CO2 emissions from the application of agricultural lime. NEE and NECB for U.S. croplands were -274 and 7 Tg C/yr for 2004, respectively. Use of moderate- to high-resolution satellite-based land cover data enables improved estimates of cropland carbon dynamics.

Stable isotope analysis of diet switching by fishes often is hampered by slow turnover rates of the tissues analyzed (usually muscle or fins). We examined epidermal mucus as a potentially faster turnover “tissue” that might provide a more rapid assessment of diet switching. In a controlled hatchery experiment, we switched the diet of juvenile steelhead (sea-run rainbow trout, Oncorhynchus mykiss ) from a plant-based feed with low δ 13 C and δ 15 N to a fish-meal-based diet with higher delta values. We found mucus to provide a significantly more rapid response to diet switching (half-life = 36 days for δ 15 N, 30 days for δ 13 C) than muscle tissue (half-life = 94 days for δ 15 N, 136 days for δ 13 C), even for growing juvenile fish. Mucus may provide a rapid turnover “tissue” for analysis of diet (or habitat) switching by fish. It has the additional advantage that it may be sampled nonlethally in some fishes, thereby avoiding problems in studying threatened or endangered species. This is the first report of the use of fish mucus in stable isotope analyses of fish tissues.