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USGS2021

Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud

Executive Summary Global food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations; and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product. Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface; (2) composition of analysis-ready data (ARD) cubes; (3) creation of a large global-reference data hub for machine learning; (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud; and (5) Google Earth Engine (GEE) cloud computing. The five key steps involved nine distinct phases. First, the world was segmented into 74 agroecological zones (AEZs). Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. Third, more than 100,000 reference-training data samples were collected using ground data (some of which were collected using a mobile application), as well as submeter- to 5-m-resolution, very high-resolution imagery sourced from other reliable sources. Fourth, reference-training data were used to create a knowledge base for separating cropland from noncropland. Fifth, MLAs such as the pixel-based supervised random forest and support-vector machines were written on the GEE using Python and JavaScript. Sixth, object-based recursive hierarchical segmentation algorithm was used, in addition to MLAs, to overcome uncertainties. Seventh, MLAs used the knowledge base to classify and separate cropland from noncropland. Eighth, accuracy assessment was conducted by generating error matrices for each of the 74 AEZs using 19,171 independent validation-data samples. Ninth, cropland areas were computed for all countries of the world and compared with United Nation’s (UN’s) Food and Agricultural Organization (FAO) and other national statistics. The outcome was a Landsat-derived global cropland-extent product at 30-m resolution (GCEP30), which has an overall accuracy of 91.7 percent. For the cropland class, producer’s accuracy was 83.4 percent, and user’s accuracy was 78.3 percent. GCEP30 calculated (using direct pixel count) the global net-cropland area (GNCA) for the year 2015 as 1.873 billion hectares (~12.6 percent of the Earth’s terrestrial area). The continental cropland distribution as a percentage of GNCA was Asia, 33 percent; Europe, 25.5 percent; Africa, 16.7 percent; North America, 14.4 percent; South America, 8.1 percent; and Australia and Oceania, 2.4 percent. The worldwide cropland areas in GCEP30 for 2015 were higher by 236 to 299 million hectares (Mha) compared to national statistics reported elsewhere for the same year (for example, in Food and Agriculture Organization’s corporate statistical database [FAOSTAT] and in the monthly irrigated and rainfed crop areas [MIRCA] database). The global cropland area reported for 2015 increased by 344 Mha (22.5 percent), compared to the year 2000. During the same period (2000–2015), the world’s population increased by 20 percent. Whereas some of these areal increases are real increases in cropland areas, others are due to the types of data, methods, and approaches used. Using the highest known resolution (compared to previous coarse-resolution global products) enabled this study to capture fragmented croplands. Coarse-resolution data compute areas on the basis of subpixels, which, for a large proportion of certain land use–land cover classes, will show only a certain percentage of the total pixel area as actual area. Subpixel areas can lead to substantial uncertainties in area computation, as determining the exact fraction of cropland areas within a coarse-resolution pixel is resource intensive and subject to errors. Other innovations in GCEP30 include reference-data hubs, machine learning, and cloud computing. Cropland areas in 214 countries, territories, departments, and regions were calculated for the year 2015 using GCEP30, on the basis of UN’s global administrative unit layers (GAUL) boundaries. The 10 leading countries in terms of cropland area (as a percentage of the GNCA) were India (9.6 percent), United States (8.95 percent), China (8.82 percent), Russia (8.32 percent), Brazil (3.42 percent), Ukraine (2.32 percent), Canada (2.29 percent), Argentina (2.05 percent), Indonesia (2 percent), and Nigeria (1.91 percent). Together, these 10 countries occupy 50 percent of the global cropland, and they have 52 percent of the global population. Their combined cropland area increased by 2 percent between 2000 and 2015, compared to the substantial increase in population of 517 million (15.5 percent). Together, India, United States, China, and Russia encompass 36 percent of the total area. In the United States and Canada, from 2000 to 2015, cropland decreased by about 2 percent, whereas their populations increased by 14 and 13 percent, respectively. The additional food requirements in these 10 countries, which are caused by increased populations, as well as increasing nutritional demands, are met by production increases in existing cropland or through virtual food trade, or both. More than 18 countries, territories, departments, or regions had 60 percent or more of their geographic area as cropland: Republic of Moldova, San Marino, and Hungary had more than 80 percent of the country’s area as cropland; Denmark, Ukraine, Ireland, and Bangladesh, 70 to 80 percent; and Uruguay, Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czechia, Italy, India, and Azerbaijan, 60 to 70 percent. Europe and South Asia can be considered agricultural capitals of the world, on the basis of their percentages of geographic area as cropland. United States, China, and Russia, which all have high cropland areas, are ranked second, third, and fourth in the world; India is ranked first. However, the amount of cropland as a percentage of the country’s geographic area is relatively very low for United States (18.3 percent), China (17.7 percent), and Russia (9.5 percent), whereas it is 60.5 percent for India. Most African and South American countries, territories, departments, or regions have less than 15 percent of their geographic area as cropland. China and India together house 36 percent of the world’s population; however, between 2000 and 2015, the amount of China’s cropland area fell by 18.9 percent, owing to urban expansion and the abandonment of farmlands caused by demographic changes (that is, the movement of population from villages to cities). In contrast, China’s population grew by 10 percent. The amount of India’s cropland increased by 8.5 percent, whereas its population grew by 20 percent. This study showed that, out of the 10 leading cropland countries, Ukraine, Nigeria, Russia, and Indonesia showed an 18 to 31 percent increase in cropland areas, on the basis of GCEP30 by the year 2015, compared to 2000. Nigeria’s cropland area increased by 25 percent, and its population increased by 31 percent in the same period. In these countries, food security is maintained by cropland expansion, productivity increases, and virtual food trade. Nevertheless, this trend of increasing net-cropland area and productivity will likely become difficult to maintain, owing to diminishing arable lands and plateauing of 50 years of continual yield increases, requiring policymakers to explore novel and data-supported approaches to solving future food security issues. The GCEP30 product, which can be browsed at full resolution at www.croplands.org , has been released for public download and use through U.S. Geological Survey (USGS)–National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (see https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/ ).

Professional Paper↗
USGS2025

Landsat-derived rainfed and irrigated-area product for conterminous United States for the year 2020 (LRIP30 CONUS 2020) using supervised and unsupervised machine learning on the cloud

Accurate maps of irrigated and rainfed croplands are crucial for assessing global food and water security. Irrigated croplands yield two to four times more grain and biomass than rainfed croplands. To meet rising food demand, the proportion of cropland that is irrigated must be increased globally. Because agriculture uses 80% to 90% of global fresh water, understanding changes in cropland extent, crop type, and irrigation is critical for meeting nutritional needs sustainably. The United States has one of the most productive rainfed and irrigated croplands in the world and is a leading producer and exporter of agricultural crops. Precise maps of irrigated and rainfed croplands in the United States are crucial for assessing the current and the future agricultural production capacity in supporting food security. We developed a 30-m resolution rainfed and irrigated area map for the conterminous United States derived from 2019 to 2021 multi-date Landsat-8 data (LRIP30 CONUS 2020). A total of 96 harmonized spectral bands comprising monthly median value composites of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR, and enhanced vegetation index [EVI]) were used. A cropland mask was then applied, and reference data were sourced from various sources. A pixel based supervised random forest classifier, and pixel based unsupervised ISODATA clustering classifier were implemented on Google Earth Engine and the ERDAS Imagine workstation to classify, identify, map, and assess accuracies of irrigated and rainfed cropland areas. The LRIP30 CONUS 2020 product achieved an overall accuracy of 93.9%. The irrigated and rainfed classes had producer's accuracies of 90.2% and 95.7%, respectively, and user's accuracies of 90.8% and 95.4%, respectively. The total net cropland area was estimated at 139.4 million hectares (Mha), of which 94.9 Mha (68%) was classified as rainfed and 44.5 Mha (32%) was classified as irrigated. State level summaries highlight regional differences and their implications for national and global food and water security.

conterminous United States↗
USGS2017

Inland fisheries – Invisible but integral to the UN Sustainable Development Agenda for ending poverty by 2030

The United Nations’ (UN) 2030 Agenda for Sustainable Development defines the formidable challenge of integrating historically separate economic, social, and environmental goals into a unified ‘plan of action for people, planet, and prosperity.’ We highlight the substantial contribution inland fisheries can make towards preventing increased poverty and, in some cases, alleviating poverty (i.e. addressing Sustainable Development Goal [SDG] 1: No Poverty) as an opportunity to inform the next set of development agendas and their associated budgets and priorities. Overlooking the contribution of inland fisheries to poverty prevention and alleviation may undermine the capacity to successfully meet the development goals, especially in rural communities in Low-Income Food-Deficit countries. Inland fisheries are essential for food and economic security as the vast majority are small-scale operations or subsistence, predominantly used by poorer groups. Protecting inland fisheries from diverse threats from other water users and associated sectors requires robust, multi-sectoral, and multinational policies that can be brought about by global initiatives like the SDGs. Without such protection, their vital contribution towards sustainable livelihoods and poverty issues becomes uncertain. Further, integrating inland fisheries into sustainable development frameworks strengthens the likelihood of achieving the UN Agenda for Sustainable Development. In this perspective article, we posit that including inland fisheries in national policy statements and programs can prove beneficial to promoting economic and social growth for the poor, preventing further poverty, and achieving SDG 1 and other SDG targets, especially those related to food security.

Global Environmental Change↗
USGS2025

Investigating the influence of forest disturbance on grizzly bear habitat ecology and fitness in the Northern Continental Divide Ecosystem

The Northern Continental Divide Ecosystem (NCDE) grizzly bear (Ursus arctos horribilis) population of northwest Montana was listed as threatened in 1975 under the Endangered Species Act. Recovery objectives for this population have centered predominantly on limiting humancaused mortality and protecting habitat. Consequently, empirically informed habitat management has been an integral component of grizzly bear recovery in the NCDE and will remain a major component of future conservation and management efforts. The NCDE has a long history of forest management and wildfire activity. Studies have demonstrated both positive and negative effects of timber harvest on grizzly bears. Grizzly bears are associated with forest edges and early successional forests (Munro et al. 2006, Stewart et al. 2013), which often have higher abundances of preferable forage (Mace and Jonkel 1986, Souliere et al. 2020). Forest management activities that reinstate early successional vegetation can thus have a positive effect on forage quality for grizzly bears (Nielsen et al. 2004). However, roadbuilding and human access associated with timber harvest and silvicultural treatments can have a negative effect on grizzly bear habitat selection and survival (Kasworm and Manley 1990, Mace et al. 1996, 2012, Schwartz et al. 2010, Boulanger and Stenhouse 2014), potentially negating the positive effects of timber harvest and certain silvicultural treatments. Wildfire disturbance is likely to have variable effects on grizzly bears. Post-fire, early successional vegetation provides high quality forage, and grizzly bears likely select these postfire stands to take advantage of increases in graminoids, forbs, roots, ungulate prey, and fruiting shrubs that emerge post disturbance (Mace and Jonkel 1986, Hamer and Herrero 1987, Best et al. 2024). On larger temporal scales, historical variation in spatial patterning of infrequent, predominantly stand-replacing fires in the NCDE, has contributed to heterogeneous landscape conditions (Hood et al. 2021). Such conditions likely benefit grizzly bears by providing various landscape features required for denning, access to foods, and security. However, more frequent, and larger high severity fires are projected to increase throughout the Rocky Mountains (Abatzoglou and Williams 2016, Parks and Abatzoglou 2020, Higuera et al. 2021), which could reduce cover and impact regrowth of herbaceous vegetation (Bard and Cain 2020). Future fire activity could change the distribution of food resources across the landscape and potentially reduce expected fire benefits to grizzly bears. Our objectives are to investigate the effects of forest disturbance from timber harvest and fire on grizzly bears in the NCDE to understand conditions that promote or degrade grizzly bear habitat quality. Balancing grizzly bear conservation while managing forest disturbances is a complex and dynamic challenge for land managers. This proposed research could help address this challenge by providing information to assist managers in developing strategies that balance species conservation and forest management objectives.

Montana↗
USGS2022

Measuring and attributing sedimentary and geomorphic responses to modern climate change: Challenges and opportunities

Today, climate change is affecting virtually all terrestrial and nearshore settings. This commentary discusses the challenges of measuring climate-driven physical landscape responses to modern global warming: short and incomplete data records, land use and seismicity masking climatic effects, biases in data availability and resolution, and signal attenuation in sedimentary systems. We identify opportunities to learn from historical and paleo data, select especially sensitive study sites, and report null results to better characterize the extent and nuances of climate-change effects. We then discuss efforts to improve attribution practices, which will lead to better predictive capabilities. We encourage the earth-science community to prioritize scientific research on climate-driven physical landscape changes so that societies will be better prepared to manage the effects on health and safety, infrastructure, water–food–energy security, economics, and ecosystems that follow from climate-driven physical landscape change.

Earth's Future↗
USGS2026

Geospatial assessment of agrivoltaic opportunities and land use requirements in Nigeria

Agrivoltaics, the co-location of agriculture and solar photovoltaic (PV) infrastructure, can deliver co-benefits like reduced plant drought stress and improved yields of shade-tolerant crops, particularly in water-scarce regions. Despite growing global interest, the technical potential and opportunities for agrivoltaics remain poorly understood in many regions facing both food and energy insecurity, such as sub-Saharan Africa. Here we provide a spatial assessment of agrivoltaic opportunities in Nigeria by integrating cropland distribution, solar resources, and water stress. We find that northern states—where cropland is abundant and water-stressed, solar irradiance is high, and electricity access remains low—offer the greatest potential for agrivoltaic systems to generate co-benefits. In contrast, the humid forest regions of southern Nigeria exhibit lower suitability, with sparse cropland and weaker solar potential. We also estimate that northern states could fully meet their projected 2050 solar energy targets by allocating less than 1% of existing cropland to agrivoltaics, whereas southern states would require much larger fractions (5.9–18.9%). Notably, in the northern state of Kano, the country’s most populous, allocating 0.6–1.8% of cropland would be sufficient to meet mid-century solar energy projections. Collectively, our findings highlight priority regions where agrivoltaics could most effectively strengthen food-energy security linkages and support equitable energy transition in Nigeria.

Scientific Reports↗
USGS2010

Conserving migratory land birds in the New World: Do we know enough?

Migratory bird needs must be met during four phases of the year: breeding season, fall migration, wintering, and spring migration; thus, management may be needed during all four phases. The bulk of research and management has focused on the breeding season, although several issues remain unsettled, including the spatial extent of habitat influences on fitness and the importance of habitat on the breeding grounds used after breeding. Although detailed investigations have shed light on the ecology and population dynamics of a few avian species, knowledge is sketchy for most species. Replication of comprehensive studies is needed for multiple species across a range of areas. Information deficiencies are even greater during the wintering season, when birds require sites that provide security and food resources needed for survival and developing nutrient reserves for spring migration and, possibly, reproduction. Research is needed on many species simply to identify geographic distributions, wintering sites, habitat use, and basic ecology. Studies are complicated, however, by the mobility of birds and by sexual segregation during winter. Stable-isotope methodology has offered an opportunity to identify linkages between breeding and wintering sites, which facilitates understanding the complete annual cycle of birds. The twice-annual migrations are the poorest-understood events in a bird's life. Migration has always been a risky undertaking, with such anthropogenic features as tall buildings, towers, and wind generators adding to the risk, Species such as woodland specialists migrating through eastern North America have numerous options for pausing during migration to replenish nutrients, but some species depend on limited stopover locations. Research needs for migration include identifying pathways and timetables of migration, quality and distribution of habitats, threats posed by towers and other tall structures, and any bottlenecks for migration. Issues such as human population growth, acid deposition, climate change, and exotic diseases are global concerns with uncertain consequences to migratory birds and even lesscertain remedies. Despite enormous gaps in our understanding of these birds, research, much of it occurring in the past 30 years, has provided sufficient information to make intelligent conservation efforts but needs to expand to handle future challenges. ?? 2010 by the Ecological Society of America.

Ecological Applications↗
USGS1990

Search path of a fossorial herbivore, Geomys bursarius , foraging in structurally complex plant communities

The influence of habitat patchiness and unpalatable plants on the search path of the plains pocket gopher ( Geomys bursarius ) was examined in outdoor enclosures. Separate experiments were used to evaluate how individual animals explored (by tunnel excavation) enclosures free of plants except for one or more dense patches of a palatable plant ( Daucus carota ), a dense patch of an unpalatable species ( Pastinaca sativa ) containing a few palatable plants ( D. carota ), or a relatively sparse mixture of palatable ( D. carota ) and unpalatable ( Raphanus sativus ) species. Only two of eight individuals tested showed the predicted pattern of concentrating search effort in patches of palatable plants. The maintenance of relatively high levels of effort in less profitable sites may reflect the security afforded food resources by the solitary social system and fossorial lifestyle of G. bursarius . Unpalatable plants repelled animals under some conditions, but search paths in the sparsely planted mixed-species treatment suggest animals can use visual or other cues to orient excavations. Evidence supporting area-restricted search was weak. More information about the use of visual cues by G. bursarius and the influence of experience on individual search mode is needed for refining current models of foraging behavior in this species.

Journal of Mammalogy↗
USGS1993

Time-activity budget of Northern Pintails using nonhunted rice fields in southwest Louisiana (Presupuesto de actividades cotidianas por parte de individuos de anas acuta en campos de arroz no utilizados para cazar en el suroeste de Louisiana)

Monthly behavioral observations of Northern Pintails ( Anas acuta ) were conducted on five leased tracts of nonhunted private land, each including 60 ha of flooded rice fields, in southwest Louisiana during November-February 1988-1989. Pintails spent 52% of diurnal time resting, 21% feeding, 16% in comfort activities, 6% in locomotion, 4% courting and 1% in other behaviors. Activities differed among months and periods of the day. Pintails used nonhunted rice fields only during the day, departing after sunset. Pintail time budgets in Louisiana approximated those reported in California. Pintails using rice fields during the day, however, fed more than did those roosting on open water pools at Lacassine National Wildlife Refuge (NWR), Louisiana. Small (60 ha), nonhunted rice fields provided wintering Northern Pintails security plus food during the day. Desde noviembre a febrero de 1988-1989 hicimos observaciones mensuales de la conducta de individuos de Anas acuta en cinco localidades privadas y libres de cacería, del suroeste de Lousiana. Cada área incluyó un campo de arroz inundado de 60 hectáreas. Los patos emplearon el 52% del tiempo diurno para descansar, 21% para alimentarse, 16% para actividades de confort, 6% para locomoción, 4% para cortejo y 1% para otras actividades. Hubo diferencias en las actividades a través de los meses de estudio y períodos del día. Los patos utilizaron las áreas libres de cacería durante el período diurno y partieron de las mismas con la puesta del sol. El presupuesto de tiempo de las aves de Louisiana se aproximó a los informados en California para la especie. Sin embargo, los patos que utilizaron los campos de arroz se alimentaron más que los que descansaron en pozas del Refugio Nacional de Vida Silvestre de Lacassine. Los pequeños campos de arroz, libres de cacería, proveyeron a los patos invernales de lugares seguros y de alimentación durante el período diurno.

Louisiana↗
USGS2023

Managing the threat of infectious disease in fisheries and aquaculture using structured decision making

Fisheries and aquaculture provide food and economic security, especially in the developing world, but both face challenges from infectious disease. Here, we consider management of disease issues from a structured decision-making perspective to examine how infectious disease can threaten seafood production and influence management decisions. For both wild fisheries and aquaculture, disease-management objectives generally aim to mitigate the severity and economic burden of outbreaks. General management strategies include manipulating host densities, reducing system connectivity, conserving or improving habitat, and implementing direct treatments or some other biological interventions. To inform decisions, mathematical models can be used to explore disease dynamics and to forecast the potential effectiveness of alternative management actions. Developing and implementing disease-management strategies also involve considering uncertainties and balancing competing stakeholder interests and risk tolerances. We conclude by outlining several steps for applying structured decision making that are broadly useful to decision makers facing issues related to disease.

Frontiers in Ecology and the Environment↗
USGS2024

Supporting climate adaptation for rural Mekong River Basin communities in Thailand

Climate change impacts on large river basins, such as the Mekong River Basin (MRB), are complex due to shared governance and interconnected socioeconomic areas, making them highly vulnerable to change. The MRB, spanning six countries including Thailand, is crucial for the food and economic security of > 60 million people. However, in 2021, Thailand was ranked as the 9th highest risk country affected by climate change. To integrate climate adaptation in Thailand's MRB, we examined the effects of climate change on rapidly developing farmer and fisher communities in northeastern Thailand and explored feasible adaptation options. Using an interdisciplinary approach that included literature review, participatory action methods, and the resist-accept-direct (RAD) framework, we found that climate change is projected to increase temperatures, precipitation, extreme events, erosion, and water clarity, while decreasing heavy sediment transport. These changes negatively impact agriculture, fisheries, human health, and tourism. We identified several adaptation strategies across environmental, ecological, and human health categories to accommodate local needs, such as preventing habitat degradation (e.g., from dams and deforestation), providing fish refuge and passage, and supporting technical capacity. Community-driven adaptation planning and implementation are essential for supporting global sustainable development in a changing climate.

Mitigation and Adaptation Strategies for Global Ch↗
USGS2021

Wetland conservation: Challenges related to water law and farm policy

Water is essential for wetland function and sustaining migratory networks for wetland wildlife across broad landscapes. Groundwater declines and surface flow reductions that impact aquatic and wetland organisms are common in the western U.S. and increasingly in the eastern U.S. Agriculture is the largest consumptive water user in the U.S. and understanding economic incentives of water-use practices and the legal context of water rights is foundational to identifying meaningful water solutions. In this paper, we provide a brief legal overview of water rights in the U.S. and synthesize the literature to provide a broad overview of how federal farm policy influences water-use decisions. We conclude that the ultimate cause of many water-use conflicts is an inefficient, farm economy that is driven by several proximate factors, of which outdated water laws and subsidies that encourage increased water use are among the most important. Development of multi-scale water budgets to assess project impacts and by working more intensively at local watershed and aquifer scales can improve conservation efforts. Finally, detailed analyses to understand and minimize the impacts of specific federal policies on agricultural water use would enhance water conservation efforts, facilitate long-term food and water security, and provide greater protection for wetland and aquatic resources.

Wetlands↗
USGS2016

POLARIS: A 30-meter probabilistic soil series map of the contiguous United States

A new complete map of soil series probabilities has been produced for the contiguous United States at a 30 m spatial resolution. This innovative database, named POLARIS, is constructed using available high-resolution geospatial environmental data and a state-of-the-art machine learning algorithm (DSMART-HPC) to remap the Soil Survey Geographic (SSURGO) database. This 9 billion grid cell database is possible using available high performance computing resources. POLARIS provides a spatially continuous, internally consistent, quantitative prediction of soil series. It offers potential solutions to the primary weaknesses in SSURGO: 1) unmapped areas are gap-filled using survey data from the surrounding regions, 2) the artificial discontinuities at political boundaries are removed, and 3) the use of high resolution environmental covariate data leads to a spatial disaggregation of the coarse polygons. The geospatial environmental covariates that have the largest role in assembling POLARIS over the contiguous United States (CONUS) are fine-scale (30 m) elevation data and coarse-scale (~ 2 km) estimates of the geographic distribution of uranium, thorium, and potassium. A preliminary validation of POLARIS using the NRCS National Soil Information System (NASIS) database shows variable performance over CONUS. In general, the best performance is obtained at grid cells where DSMART-HPC is most able to reduce the chance of misclassification. The important role of environmental covariates in limiting prediction uncertainty suggests including additional covariates is pivotal to improving POLARIS' accuracy. This database has the potential to improve the modeling of biogeochemical, water, and energy cycles in environmental models; enhance availability of data for precision agriculture; and assist hydrologic monitoring and forecasting to ensure food and water security.

Geoderma↗
USGS2018

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R 2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282 .

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2023

Supplying ecosystem services on US rangelands

Rangelands comprise 40% of the conterminous United States and they supply essential ecosystem services to society. A scenario assessment was conducted to determine how accelerating biophysical and societal drivers may modify their future availability. Four scenarios emerged: two may maintain rural communities by sustaining the prevailing ecosystem service of beef cattle production, and two may transform rural communities through expansion of renewable energy technologies and infusion of external capital from amenity land sales. Collaborative organizations representing diverse societal sectors may most effectively identify and manage trade-offs among ecosystem service availability, and equitably prioritize food and energy security, environmental quality and cultural identity.

Nature Sustainability↗
USGS2022

New generation hyperspectral sensors DESIS and PRISMA provide improved agricultural crop classifications

Using new remote sensing technology to study agricultural crops will support advances in food and water security. The recently launched, new generation spaceborne hyperspectral sensors, German DLR Earth Sensing Imaging Spectrometer (DESIS) and Italian PRecursore IperSpettrale della Missione Applicativa (PRISMA), provide unprecedented data in hundreds of narrow spectral bands for the study of the Earth. Therefore, our overarching goal in this study was to use these data to explore advances that can be made in agricultural research. We selected PRISMA and DESIS images during the 2020 growing season in California's Central Valley to study seven major crops. PRISMA and DESIS images were highly correlated (R 2of 0.9–0.95). Out of the 235 DESIS bands (400–1000 nm) and 238 PRISMA bands (400–2500 nm), 26 (11%) and 45 (19%) bands, respectively, were optimal to study agricultural crops. These optimal bands provided crop type classification accuracies of 83–90%. Hyperspectral vegetation indices to estimate plant pigment content, stress, biomass, moisture, and cellulose/lignin content were also identified.

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USGS2024

Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user's accuracies of 95% to 100%.

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USGS2025

Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data

Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.

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