Geology topics

Britt Windsor Smith

Publications and source records attributed to Britt Windsor Smith.

8 recordsLinked to original sources

USGS2026

Satellite embeddings for crop type classification: A comparative examination

Embedding datasets encode complex relationships among multiple sources of Earth observation data into a compact format. Here, we evaluated the utility of a 10-m global Satellite Embedding product (SE) for classifying crop types in central California for the year 2020. We compared the classification accuracy of a random forest model based exclusively on the SE layer to an existing random forest model with multiple imagery inputs. Our results showed the SE-based classification had higher agreement with the reference dataset (California Department of Water Resources crop map) than the classification based on Landsat and National Agricultural Imagery Program inputs (94.7% versus. 91.9% overall accuracy, respectively). The performance of individual crop types was consistent across models, ranging from high agreement for rice (98.4% versus 98% accuracy) to lower agreement for pasture, grain, and fallow/young perennial classes (< 65% accuracy in both models). The SE-based workflow used three times less cloud-based computational resources and represented substantial savings of predictor development time. Geospatial embedding products can aid classification efforts by reducing predictor development time and processing demands while maintaining classification accuracy.

California↗

USGS2026

Multi-temporal mapping and analyses of post-wildfire surface soil moisture modeled from Landsat Thermal-IR and Sentinel-1 SAR

Wildfires alter surface soil moisture by consuming vegetation and exposing the soil to increased solar radiation, increased precipitation throughfall, and reduced infiltration from soil hydrophobicity. This can affect the water balance within the watershed and, in turn, influence post-fire nutrient cycling and ecosystem succession. Modeling surface soil moisture over large areas using satellite remote sensing can aid monitoring of soil moisture patterns and dynamics. We trained a random forest regression model using an in situ soil moisture network and examined pre- and post-fire soil moisture dynamics for three wildfires that burned in 2016 and 2017 in coastal California, U.S.A. Model inputs included Sentinel-1A synthetic aperture radar (SAR) and Landsat thermal infrared imagery (TIR), which produced a high resolution (30 m) soil moisture map time series with 18–20 estimates across 2 years. An unbiased root mean square error of 0.065 and an r 2 of 0.624 indicates a good fit between the model and reference data. We found a significant interaction between treatment (wildfire or unburned) and period (before or after wildfire) by examining the modeled surface soil moisture in high-severity burned areas. Before-after control-impact metrics showed that modeled surface soil moisture was lower after each fire within wildfire areas compared to unburned controls, and overall changes in soil moisture were greater in the wildfire perimeters than unburned areas. These results demonstrate the potential of Sentinel-1 SAR and Landsat TIR data for the development of high-resolution soil moisture time series products to support monitoring and assessment of post-fire soil hydrologic conditions.

California↗

USGS2025

Assessing causes and consequences of winter surface water dynamics in California’s Central Valley using satellite remote sensing

California's Central Valley is increasingly vulnerable to winter floods. A comprehensive spatial baseline of flood extents is critical for inundation analyses that can enhance future flood predictions, but cloud cover has prevented the regular observation of surface water extents with optical satellite imagery. In this study, we leveraged the daily resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to create a continuous series of monthly Dynamic Surface Water Extent (DSWEmod) images across the Central Valley from January 2003 to January 2023. We used the timeseries to assess the climatic driving forces of winter (Oct–April) surface water variability at sub-basin and pixel scales. At the sub-basin scale, we evaluated the influences of winter precipitation, occurrence of atmospheric rivers, and antecedent soil moisture on monthly surface water extents and found that the greatest correspondence occurs in mid-winter (Dec–Feb); in contrast, non-precipitation drivers such as water management play a stronger role in autumn and spring. The pixel-level analysis identified the probabilities of precipitation-driven surface water occurrences in the Sacramento basin are highest along rivers, conveyance channels, and floodways, with higher probabilities under wetter antecedent soil moisture conditions. Precipitation-driven surface water occurrences are also common in leveed areas and outside flood boundaries designated by state and federal agencies where exposure of structures to inundation was larger in terms of their value. Finally, areas with more frequent precipitation-driven flooding have poor recharge potential but are commonly within 5 km of areas classified as having good potential. This study demonstrates a novel approach for exploring the utility of MODIS for understanding surface water dynamics in mid-winter, a period characterized by peak precipitation, flood risk, and surface water extent. This information can provide valuable insights for (1) assessing flood risks for infrastructure and populations, (2) identifying areas most suited to strategic water management investments to increase recharge, and (3) analyzing precipitation thresholds that trigger flooding to allow proactive water management strategies to minimize damage and maximize recharge.

California↗

USGS2025

Linking fire radiative power to land cover, fire history, and environmental setting in Alaska, 2003–2022

Background Fire radiative power (FRP) shows promise as a diagnostic and predictive indicator of fire behavior and post-fire effects in Alaska, USA. Aims To investigate relationships between FRP, vegetation functional groups, and environmental settings in Alaska (2003–2022) under various fire history conditions. Methods We tested for distinctness of MODIS FRP distributions associated with vegetation classes and fire legacies (frequency and number of previous burns). We used a random forest model to examine relative importance of vegetation class for FRP versus bottom-up biophysical and temporal parameters. Key results FRP distributions are statistically distinct among vegetation functional groups with contrasting fuel biomass, or within functional groups with contrasting burn characteristics. Location and topography, which constrain vegetation class, strongly determine FRP, and fire history is of lesser importance over the 19-year analysis period. Conclusions FRP can be used to identify wildfire consumption in dissimilar vegetation classes but is highly conditioned by geographic location. The complex and evolving vegetation composition of post-fire boreal landscapes precludes a clear association of expected FRP at distinct stages of recovery. Implications These results can inform further study of FRP as an indicator of fire behavior and fuel consumption and for informing dynamics of post-fire recovery across Alaska.

Alaska↗

USGS2024

The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning

The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national-scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland-specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non-wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

Arizona↗

USGS2024

Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020

California produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high-resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National Agriculture Imagery Program and Landsat imagery were used to classify nine crop types that are common in the study region: grain crops, field crops, rice, citrus and subtropical, deciduous fruit and nut, vineyard, berry and vegetable, pasture, and fallow/young perennial crop types. To create labeled data, we sampled 1253 fields and manually identified crop types for each examined year using high-resolution imagery and Landsat normalized difference vegetation index time series. We applied a random forest machine learning algorithm in Google Earth Engine. Results show that the mean overall classification accuracy of the nine-class map was 93.1%, with individual accuracies ranging from 99.3% (rice) to 89.5% (fallow/young perennial). Mann–Kendall trend tests showed significant ( p less than 0.05) declines in field crop and pasture area during the study period, while deciduous fruit and nut, citrus and subtropical, and fallow/young perennial crop types experienced significant increases. At an aggregate level, there was a general shift from annual crop types to perennial crop types. These data provide a 16-year time span of spatially explicit crop type classifications, trends, and patterns in central California that can be used to aid managers and decision makers for resource planning or hazard mitigation.

California↗

USGS2022

DSWEmod - The production of high-frequency surface water map composites from daily MODIS images

Optical satellite imagery is commonly used for monitoring surface water dynamics, but clouds and cloud shadows present challenges in assembling complete water time series. To test whether the daily revisit rate of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery can reduce cloud obstruction and improve high-frequency surface water mapping, we compared map results derived from Landsat (30-m) and MODIS (250-m) data across the state of California for 2003–2019. We adapted the Dynamic Surface Water Extent (DSWE) model in Google Earth Engine to generate surface water map composites from MODIS imagery every 5, 10, 15, and 30 days, and compared products to monthly Landsat-based DSWE maps. Results for DSWEmod (DSWE MODIS) in California suggest that more than 5% data loss (cloud obstruction, etc.) was present in only 2% of the 15-day time series, as compared to 32% of the monthly Landsat DSWE time series. The five-day DSWEmod composites averaged 8.4% obscuration in the winter months. Area estimates derived from cloud-filtered MODIS and Landsat monthly products have the highest linear correlations compared to streamgage discharge records, suggesting that monthly scale analyses best explain the relationship between surface water area and general streamflow dynamics. Shorter-interval DSWEmod products have lower correlations but utility for understanding the timing of surface water peaks and past flood events.

Journal of the American Water Resources Associatio↗

USGS2022

Using Landsat and MODIS satellite collections to examine extent, timing, and potential impacts of surface water inundation in California croplands☆

The state of California, United States of America produces many crop products that are both utilized domestically and exported throughout the world. With nearly 39,000 km 2 of croplands, monitoring unintentional and intentional surface water inundation is important for water resource management and flood hazard readiness. We examine inundation dynamics in California croplands from 2003 to 2020 by intersecting monthly surface water maps (n = 216 months) derived using two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with a high-quality cropland map generated by the California Department of Water Resources. Surface water maps were produced using the Dynamic Surface Water Extent model, in which satellite image pixels are classified into different levels of detection confidence. Our analysis focused on calculating monthly and annual occurrence of “high confidence” water for each satellite collection across eight cropland types and 58 counties. Results indicate that 49.9% (MODIS) to 56.4% (Landsat) of croplands were inundated at least once during the 18-year timespan. Rice crops, due to their unique need of consistent surface water and dominance as a crop type in CA, had the highest proportion of and mean annual inundation area, while citrus crops had the lowest. Mean monthly inundation patterns in most croplands followed California's precipitation patterns with high inundation during the winter and spring rainy season. At the county level, croplands in the southern Central Valley typically had high occurrences of inundation in conjunction with large crop areas. Exposure and sensitivity of inundation for three crop types (citrus, deciduous, and vineyards) that are typically less associated with intentional inundation were geographically variable, but overall were generally highest in counties in the southern Central Valley, California's primary agricultural region. Flood and precipitation related crop insurance claims indicated that rice had the highest mean indemnity payout for any month with damages typically occurring in March and April. Insurance claims were also high in deciduous fruit and nut crops, which had peak damages in February. A comparison between inundation results and insurance claims suggests that the inundation mapped by our process coincides with claim activity. These data elucidate water inundation patterns across the state that can serve to inform farmers, insurers, decision makers, resource managers, and flood mitigation professionals.

California↗