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USGS2017

Groundwater development stress: Global-scale indices compared to regional modeling

The increased availability of global datasets and technologies such as global hydrologic models and the Gravity Recovery and Climate Experiment (GRACE) satellites have resulted in a growing number of global-scale assessments of water availability using simple indices of water stress. Developed initially for surface water, such indices are increasingly used to evaluate global groundwater resources. We compare indices of groundwater development stress for three major agricultural areas of the United States to information available from regional water budgets developed from detailed groundwater modeling. These comparisons illustrate the potential value of regional-scale analyses to supplement global hydrological models and GRACE analyses of groundwater depletion. Regional-scale analyses allow assessments of water stress that better account for scale effects, the dynamics of groundwater flow systems, the complexities of irrigated agricultural systems, and the laws, regulations, engineering, and socioeconomic factors that govern groundwater use. Strategic use of regional-scale models with global-scale analyses would greatly enhance knowledge of the global groundwater depletion problem.

Groundwater↗

USGS2012

The hydrology of a drained topographical depression within an agricutlural field in north-central Iowa

North-central Iowa is an agriculturally intensive area comprising the southeastern portion of the Prairie Pothole Region, a landscape containing a high density of enclosed topographical depressions. Artificial drainage practices have been implemented throughout the area to facilitate agricultural production. Vertical surface drains are utilized to drain the topographical depressions that accumulate water. This study focuses on the hydrology of a drained topographical depression located in a 39.5 ha agricultural field. To assess the hydrology of the drained depression, a water balance was constructed for 11 ponding events during the 2008 growing season. Continuous pond and groundwater level data were obtained with pressure transducers. Flows into the vertical surface drain were calculated based on pond depth. Precipitation inflows and evaporative outflows of the ponds were calculated using climatic data. Groundwater levels were used to assess groundwater/pond interactions. Results of the water balances show distinct differences between the inflows to and outflows from the depression based on antecedent conditions. In wet conditions, groundwater inflow sustained the ponds. The ponds receded only after the groundwater level declined to below the land surface. In drier conditions, groundwater was not a source of water to the depression. During these drier conditions, infiltration comprised 30% of the outflows from the depression during declining pond stages. Over the entire study period, the surface drain, delivering water to the stream, was the largest outflow from the pond, accounting for 97% of the outflow, while evapotranspiration was just 2%. Precipitation onto the pond surface proved to be a minor component, accounting for 4% of the total inflows.

Iowa↗

USGS2022

Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis

The Upper Rio Grande Basin (URGB) is a critical international water resource under pressure from a myriad of climatic, ecological, infrastructural, water-use, and legal constraints. The objective of this study is to provide a comprehensive assessment of the spatial distribution and temporal trends of selected water-budget components (snow processes, evapotranspiration (ET), streamflow processes, and groundwater storage) using integrated analyses, such as watershed modeling and water availability and use data in the URGB over the past three decades. A spatially distributed snow evolution modeling system simulated snowpack processes over 34 years (1984–2017). It highlighted snow water equivalent declines from -35 to -77 mm/decade with widespread variability across elevation zones and land cover types. Gridded actual ET data from the SSEBop model were developed and tested for the URGB and demonstrated that all land-cover types had significant decreasing trends (1986-2015) ranging from -14 to -80 mm/decade. Conductivity-mass-balance (CMB) hydrograph separation results found that baseflow forms a large component of total streamflow, ranging from 29 to 69% (49% average) of total streamflow at 17 URGB sites upstream of Albuquerque, NM. Three of 4 graphical hydrograph separation methods in the U.S. Geological Survey Groundwater Toolbox were found to be inappropriate for estimating baseflow in the URGB; the most promising method, baseflow index (BFI) Standard, was optimized using CMB data and tested at three URGB sites, with resulting overestimation of 0 to 47%. Simulated changes in groundwater storage were extracted from historical and recent groundwater-flow models of select alluvial basins (San Luis, Española, Middle Rio Grande, and Tularosa-Hueco). In general, decreases in groundwater storage were observed from 1903 to 2013 except for the San Luis alluvial basin (Colorado), where periods of recovery are observed. The PRMS hydrologic model was successfully calibrated for 9 near-native subbasins (Nash-Sutcliffe efficiency 0.47 to 0.85) and parameters translated to the remaining subbasins; compared to simulated near-native flows (with minimal influence of reservoirs or diversions), observed Rio Grande streamgage flows demonstrated reductions of 40% or more for New Mexico and Texas areas of the basin. Significant decreasing trends (1980-2015) in precipitation, snowmelt rate, streamflow, and baseflow were observed at many of the 12 streamgage basins studied, which suggests that the decreasing trends for actual ET may be related to overall decreasing water availability in the basin, with negative implications for agricultural production and groundwater abstraction. Water security concerns arise from our findings of higher fraction precipitation as rain, slower snowmelt rates leading to decreasing streamflow production, and an increasing fraction of baseflow, all of which will affect the timing and magnitude of water available for human needs in the basin.

Upper Rio Grande Basin↗

USGS2017

Assessing the impacts of future climate conditions on the effectiveness of winter cover crops in reducing nitrate loads into the Chesapeake Bay Watershed using SWAT model

Winter cover crops (WCCs) have been widely implemented in the Coastal Plain of the Chesapeake Bay watershed (CBW) due to their high effectiveness at reducing nitrate loads. However, future climate conditions (FCCs) are expected to exacerbate water quality degradation in the CBW by increasing nitrate loads from agriculture. Accordingly, the question remains whether WCCs are sufficient to mitigate increased nutrient loads caused by FCCs. In this study, we assessed the impacts of FCCs on WCC nitrate reduction efficiency on the Coastal Plain of the CBW using Soil and Water Assessment Tool (SWAT) model. Three FCC scenarios (2085 – 2098) were prepared using General Circulation Models (GCMs), considering three Intergovernmnental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) greenhouse gas emission scenarios. We also developed six representative WCC implementation scenarios based on the most commonly used planting dates and species of WCCs in this region. Simulation results showed that WCC biomass increased by ~ 58 % under FCC scenarios, due to climate conditions conducive to the WCC growth. Prior to implementing WCCs, annual nitrate loads increased by ~ 43 % under FCC scenarios compared to the baseline scenario (2001 – 2014). When WCCs were planted, annual nitrate loads were substantially reduced by ~ 48 % and WCC nitrate reduction efficiency water ~ 5 % higher under FCC scenarios relative to the baseline. The increase rate of WCC nitrate reduction efficiency varied by FCC scenarios and WCC planting methods. As CO2 concentration was higher and winters were warmer under FCC scenarios, WCCs had greater biomass and therefore showed higher nitrate reduction efficiency. In response to FCC scenarios, the performance of less effective WCC practices (e.g., barley, wheat, and late planting) under the baseline indicated ~ 14 % higher increase rate of nitrate reduction efficiency compared to ones with better effectiveness under the baseline (e.g., rye and early planting), due to warmer temperatures. According to simulation results, WCCs were effective to mitigate nitrate loads accelerated by FCCs and therefore the role of WCCs in mitigating nitrate loads is even more important in the given FCCs.

Chesapeake Bay↗

USGS2021

Simulating hydrologic effects of wildfire on a small sub-alpine watershed in New Mexico, U.S.

Streamflow records available before and after wildfire in a small, mixed conifer, sub-alpine monsoonal dominated watershed in New Mexico provided a unique opportunity to calibrate a watershed model (PRMS) for pre- and postfire conditions. The calibrated model was then used to simulate the hydrologic effects of fire. Simulated postfire surface runoff averaged 14.7 times greater than prefire for the 29-year simulation period. The relationship between precipitation and streamflow changed dramatically after wildfire, largely from a decreased influence of antecedent soil moisture (ASM) and increased influence of canopy factors (less interception) and soil factors (greater hydrophobicity, less infiltration) in controlling surface runoff. For higher ASM, simulated pre- and postfire streamflow was similarly variable. However, for moderate and lower ASM, soil water storage was too low to contribute baseflow for either prefire or postfire conditions, and thus postfire streamflow maintained a linear, surface runoff-dominated response to precipitation, whereas prefire streamflow showed little response. Postfire streamflow efficiency increased with ASM from a mean of 0.02 at the lowest ASM to 0.30 at the highest ASM, whereas prefire conditions showed no sensitivity to ASM at low to moderate ASM. Postfire streamflow increased (2.1 times greater median flow than prefire), particularly from increased surface runoff (14.7 times greater), which occurred across all ASM conditions. As a result, streamflow shifted from baseflow-dominated to surface runoff-dominated after wildfire. This result indicates that substantial increases in runoff efficiency (20% or more of precipitation volume) can occur across a range of ASM postfire, which may have severe consequences for flooding. This result also indicates that monitoring of soil moisture would enhance raingauge networks for early flood warning.

New Mexico↗

USGS1985

Annual summary of ground-water conditions in Arizona, Spring 1983 to Spring 1984

A summary map shows various aspects of groundwater availability in Arizona. Potential well production, in increments of 0 to 10 gpm, 10 to 500 gpm, and 50 to 2500 gpm (average 1000 gpm) os the primary emphasis of the map; however, data on changes in water level from spring 1983 to spring 1984, status of groundwater inventory, and estimated groundwater pumpage in Arizona in 1983, are also presented. The total water pumpage is also broken down by the following use categories: drainage, public supply, domestic, livestock, industrial and agricultural.

Arizona↗

USGS2020

GIS-Modeling of island hopping through the Philippines demonstrates trade-offs migrant grey-faced buzzards during oceanic crossings

Migration can be costly with consequences that can influence population trajectories. These costs and consequences are especially heightened during over-water travels, which can be high-risk events for birds. We created spatial models to evaluate potential migratory responses of “oceanic”, island-hopping grey-faced buzzards that encounter variation in landscape parameters and weather as they move through and out of the Philippine archipelago. We constrained the modeled routes to enter the island chain at Basco and to use one of four potential exit points in the south of the country, either Balabac, Bongao, Balut Island, or Cape San Agustin. We used all possible combinations of our three external parameters (stopover sites, water crossings and wind direction) to model alternative migratory routes for each of the four exit points (n = 20 migratory routes). Modeled grey-faced buzzard routes were between 1,582 and 2,970 km. Routes overlapped over eastern and central Luzon, along a leading line created by the Sierra Madre Mountains. Routes also overlapped and suggested unavoidable over-water crossings between Mindoro and Palawan, Negros and Zamboanga del Norte, and Leyte and Surigao. Our models suggest that the optimal migratory strategy for these birds is to find the shortest route to an exit point with the greatest possible access to stopover habitats and fewest open-water crossings under wind resistance. Understanding how each of these external factors affected the geography and characteristics of the migratory routes helps us to understand the context for different migratory strategies of birds that face dangerous open-water crossings on migration.

Journal of Engineering, Environment and Agricultur↗

USGS1989

History of the Federal Interagency Sedimentation Project

Since 1939, the date of the Project's inception, the team has operated under the direction of two lead agencies - the U.S. Geological Survey and U.S. Army Corps of Engineers. The supporting agencies are the Agricultural Research Service, Bureau of Reclamation, U.S. Forest Service, Bureau of Land Management, Federal Highway Administration, and the Tennessee Valley Authority. Overall direction of the Project rests with the Subcommittee on Sedimentation, Interagency Advisory Committee on Water Data. The Project's goals focus on improving and maintaining the quality of fluvial sediment data by (1) developing sediment samplers, laboratory analyzers, and automatic gages (2) evaluating methods, (3) standardizing equipment and methods and (4) procuring, calibrating and selling equipment.

Conference Paper↗

USGS2007

River enhancement in the Upper Mississippi River basin: Approaches based on river uses, alterations, and management agencies

The Upper Mississippi River is characterized by a series of locks and dams, shallow impoundments, and thousands of river channelization structures that facilitate commercial navigation between Minneapolis, Minnesota, and Cairo, Illinois. Agriculture and urban development over the past 200 years have degraded water quality and increased the rate of sediment and nutrient delivery to surface waters. River enhancement has become an important management tool employed to address causes and effects of surface water degradation and river modification in the Upper Mississippi River Basin. We report information on individual river enhancement projects and contrast project densities, goals, activities, monitoring, and cost between commercially non-navigated and navigated rivers (Non-navigated and Navigated Rivers, respectively). The total number of river enhancement projects collected during this effort was 62,108. Cost of all projects reporting spending between 1972 and 2006 was about US$1.6 billion. Water quality management was the most cited project goal within the basin. Other important goals in Navigated Rivers included in-stream habitat improvement and flow modification. Most projects collected for Non-navigated Rivers and their watersheds originated from the U.S. Department of Agriculture (USDA). The U.S. Army Corps of Engineers and the USDA were important sources for projects in Navigated Rivers. Collaborative efforts between agencies that implement projects in Non-navigated and Navigated Rivers may be needed to more effectively address river impairment. However, the current state of data sources tracking river enhancement projects deters efficient and broad-scale integration. ?? Journal compilation ?? 2007 Society for Ecological Restoration International.

Restoration Ecology↗

USGS2022

Temporal variability in TiO2 engineered particle concentrations in rural Edisto River

Titanium dioxide (TiO 2 ) is widely used in engineered particles including engineered nanomaterial (ENM) and pigments, yet its occurrence, concentrations, temporal variability, and fate in natural environmental systems are poorly understood. For three years, we monitored TiO 2 concentrations in a rural river basin (Edisto River, < 1% urban land cover) in South Carolina, United States. The total concentrations of Ti, Nb, Al, Fe, Ce, and La in the Edisto River trended higher during spring/summer compared to autumn/winter. Upward trending Ti/Nb ratio in the spring/summer compared to near-background autumn/winter ratios of 255.7 ± 8.9 indicated agricultural preparation and growing-season-related increases in TiO 2 engineered particles. In contrast, downward trending of the Ti/Al and Ti/Fe ratios in the spring and summer compared to the near-background autumn/winter ratios of 0.05 indicated greater mobilization of Fe and Al, relative to Ti during spring/summer. Surface-water concentrations of TiO 2 engineered particles varied between 0 and 128.7 ± 3.9 μg TiO 2 L −1 . Increases in TiO 2 concentrations over the spring/summer were associated with increases in phosphorus, orthophosphate, nitrate, ammonia, anthropogenic gadolinium, water temperature, suspended sediments, organic carbon, and alkalinity, and with decreases in dissolved oxygen. The association between these contaminants together with the timing of the increases in their concentrations is consistent with diffuse wastewater sources, such as reuse application overspray, biosolids fertilization, leaking sewers, or septic tanks, as the driver of instream concentrations; however, other diffuse sources cannot be ruled out. The findings of this study indicate spatially-distributed (non-point source) releases can result in high concentrations of TiO 2 engineered particles, which may pose higher risks to rural stream aquatic ecosystems during the agricultural season. The results illustrate the importance of monitoring seasonal variations in engineered particles concentrations in surface waters for a more representative assessment of ecosystem risk.

South Carolina↗

USGS2006

Environmental Effects of Agricultural Practices - Summary of Workshop Held on June 14-16, 2005

A meeting between the U.S. Geological Survey (USGS) and its partners was held June 14-16, 2005, in Denver, CO, to discuss science issues and needs related to agricultural practices. The goals of the meeting were to learn about the (1) effects of agricultural practices on the environment and (2) tools for identifying and quantifying those effects. Achieving these goals required defining the environmental concerns, developing scientific actions to address assessment of environmental effects, and creating collaborations to identify future research requirements and technical gaps. Five areas of concern were discussed-emerging compounds; water availability; genetically modified organisms; effects of conservation practices on ecosystems; and data, methods, and tools for assessing effects of agricultural practices.

Scientific Investigations Report↗

USGS2021

Solutions in microbiome engineering: Prioritizing barriers to organism establishment

Microbiome engineering is increasingly being employed as a solution to challenges in health, agriculture, and climate. Often manipulation involves inoculation of new microbes designed to improve function into a preexisting microbial community. Despite, increased efforts in microbiome engineering inoculants frequently fail to establish and/or confer long-lasting modifications on ecosystem function. We posit that one underlying cause of these shortfalls is the failure to consider barriers to organism establishment. This is a key challenge and focus of macroecology research, specifically invasion biology and restoration ecology. We adopt a framework from invasion biology that summarizes establishment barriers in three categories: (1) propagule pressure, (2) environmental filtering, and (3) biotic interactions factors. We suggest that biotic interactions is the most neglected factor in microbiome engineering research, and we recommend a number of actions to accelerate engineering solutions.

The ISME Journal: Multidisciplinary Journal of Mic↗

USGS2019

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

GIScience and Remote Sensing↗

USGS2014

Differentiating transpiration from evaporation in seasonal agricultural wetlands and the link to advective fluxes in the root zone

The current state of science and engineering related to analyzing wetlands overlooks the importance of transpiration and risks data misinterpretation. In response, we developed hydrologic and mass budgets for agricultural wetlands using electrical conductivity (EC) as a natural conservative tracer. We developed simple differential equations that quantify evaporation and transpiration rates using flowrates and tracer concentrations atwetland inflows and outflows. We used two ideal reactormodel solutions, a continuous flowstirred tank reactor (CFSTR) and a plug flow reactor (PFR), to bracket real non-ideal systems. From those models, estimated transpiration ranged from 55% (CFSTR) to 74% (PFR) of total evapotranspiration (ET) rates, consistent with published values using standard methods and direct measurements. The PFR model more appropriately represents these nonideal agricultural wetlands in which check ponds are in series. Using a fluxmodel, we also developed an equation delineating the root zone depth at which diffusive dominated fluxes transition to advective dominated fluxes. This relationship is similar to the Peclet number that identifies the dominance of advective or diffusive fluxes in surface and groundwater transport. Using diffusion coefficients for inorganic mercury (Hg) and methylmercury (MeHg) we calculated that during high ET periods typical of summer, advective fluxes dominate root zone transport except in the top millimeters below the sediment–water interface. The transition depth has diel and seasonal trends, tracking those of ET. Neglecting this pathway has profound implications: misallocating loads along different hydrologic pathways; misinterpreting seasonal and diel water quality trends; confounding Fick's First Law calculations when determining diffusion fluxes using pore water concentration data; and misinterpreting biogeochemicalmechanisms affecting dissolved constituent cycling in the root zone. In addition,our understanding of internal root zone cycling of Hg and other dissolved constituents, benthic fluxes, and biological irrigation may be greatly affected.

Science of the Total Environment↗

USGS2023

Mapping vegetation index-derived actual evapotranspiration across croplands using the Google Earth Engine platform

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands in drylands, we modeled and mapped ETa using empirical RS methods across the Zayandehrud river basin in Iran for two decades (2000–2019) on the Google Earth Engine platform using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index 2 (EVI2). Developed ET-VI products in this study comprise three NDVI-based ETa (ET-NDVI*, ET-NDVI* scaled , and ET-NDVI Kc ) and an EVI2-based ETa (ET-EVI2). We (a) applied, for the first time, the ET-NDVI* method to croplands as a crop-independent index and then compared its performance with the ET-EVI2 and crop ET, and (b) assessed the ease and feasibility of the transferability of these methods to other regions. Comparing four ET-VI products showed that annual ET-EVI2 and ET-NDVI* scaled estimations were close. ET-NDVI Kc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVI Kc were easy to parametrize and adopt to other regions, while ET-NDVI* and ET-NDVI* scaled are site-dependent and sensitive to image acquisition time. ET-EVI2 performed robustly in arid and semi-arid regions making it a better tool. Future research should further develop and confirm these findings by characterizing the accuracy of VI-based ETa over croplands in drylands by comparing them with available ETa products and examining their performance using crop-specific comparisons.

Remote Sensing↗

USGS2009

Mineral resource of the month: bromine

The article offers information on bromine, a natural element considered as a dissolved species in seawater, saltwater lakes and underground brines linked with petroleum deposits. Bromine belongs to the halogen group of elements and is characterized with brownish-red color and beach-like odor. It is commonly used in flame retardants, agriculture and drilling.

Earth↗

USGS2019

Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine

Accurate and timely information on the distribution of irrigated croplands is crucial to research on agriculture, water availability, land use, and climate change. While agricultural land use has been well characterized, less attention has been paid specifically to croplands that are irrigated, in part due to the difficulty in mapping and distinguishing irrigation in satellite imagery. In this study, we developed a semi-automatic training approach to rapidly map irrigated croplands across the conterminous United States (CONUS) at 30 m resolution using Google Earth Engine. To resolve the issue of lacking nationwide training data, we generated two intermediate irrigation maps by segmenting Landsat-derived annual maximum greenness and enhanced vegetation index using county-level thresholds calibrated from an existing coarse resolution irrigation map. The resulting intermediate maps were then spatially filtered to provide a training data pool for most areas except for the upper midwestern states where we visually collected samples. We then used random samples extracted from the training pool along with remote sensing-derived features and climate variables to train ecoregion-stratified random forest classifiers for pixel-level classification. For ecoregions with a large training pool, the procedure of sample extraction, classifier training, and classification was conducted 10 times to obtain stable classification results. The resulting 2012 Landsat-based irrigation dataset (LANID) identified 23.3 million hectares of irrigated croplands in CONUS. A quantitative assessment of LANID showed superior accuracy to currently available maps, with a mean Kappa value of 0.88 (0.75–0.99), overall accuracy of 94% (87.5–99%), and producer’s and user’s accuracy of the irrigation class of 97.3% and 90.5%, respectively, at the aquifer level. Evaluation of feature importance indicated that Landsat-derived features played the primary role in classification in relatively arid regions while climate variables were important in the more humid eastern states. This methodology has the potential to produce annual irrigation maps for CONUS and provide insights into the field-level spatial and temporal aspects of irrigation.

conterminous United States↗

USGS2022

Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security

Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.

GIScience & Remote Sensing↗