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Results for “Journal of Applied Remote Sensing”

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At least 73 records · Page 4Linked to original sources
USGS2013

Next generation of global land cover characterization, mapping, and monitoring

Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC).

International Journal of Applied Earth Observation↗
USGS2013

The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth

Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on remote sensing input data – to estimate cheatgrass beginning of spring growth (BOSG) in the northern Great Basin. The model was then applied to map the location and timing of cheatgrass spring growth for the entire area. The model was strong ( R 2 = 0.85) and predicted an average cheatgrass BOSG across the study area of 29 March–4 April. Of early cheatgrass BOSG areas, 65% occurred at elevations below 1452 m. The highest proportion of cheatgrass BOSG occurred between mid-April and late May. Predicted cheatgrass BOSG in this study matched well with previous Great Basin cheatgrass green-up studies.

Great Basin↗
USGS2015

Quantitative imaging of volcanic plumes — Results, needs, and future trends

Recent technology allows two-dimensional “imaging” of trace gas distributions in plumes. In contrast to older, one-dimensional remote sensing techniques, that are only capable of measuring total column densities, the new imaging methods give insight into details of transport and mixing processes as well as chemical transformation within plumes. We give an overview of gas imaging techniques already being applied at volcanoes (SO 2 cameras, imaging DOAS, FT-IR imaging), present techniques where first field experiments were conducted (LED-LIDAR, tomographic mapping), and describe some techniques where only theoretical studies with application to volcanology exist (e.g. Fabry–Pérot Imaging, Gas Correlation Spectroscopy, bi-static LIDAR). Finally, we discuss current needs and future trends in imaging technology.

Journal of Volcanology and Geothermal Research↗
USGS1982

Evaluation of AMOEBA: a spectral-spatial classification method

Muitispectral remotely sensed images have been treated as arbitrary multivariate spectral data for purposes of clustering and classifying. However, the spatial properties of image data can also be exploited. AMOEBA is a clustering and classification method that is based on a spatially derived model for image data. In an evaluation test, Landsat data were classified with both AMOEBA and a widely used spectral classifier. The test showed that irrigated crop types can be classified as accurately with the AMOEBA method as with the generally used spectral method ISOCLS; the AMOEBA method, however, requires less computer time.

Journal of Applied Photographic Engineering↗
USGS2025

Ultraviolet and visible remote sensing of volcanic gases

As magma rises in volcanic systems, volatile species exsolve from the silicate melt and are emitted as gases into the atmosphere. Measuring the magnitude and composition of gas emissions from volcanoes provides insights into processes occurring deep within the Earth and helps constrain the impact of volcanic degassing on atmospheric chemistry. Optical remote sensing techniques allow volcanic gas emissions to be characterized without the need to access hazardous areas near active volcanic vents. This paper reviews the state of the art in ultraviolet and visible volcanic gas remote sensing from the ground, air, and space. Special attention is given to discussing the physics of atmospheric radiative transfer on which these techniques are based. The functionality and limitations of different remote sensing instruments are examined, making clear that the ideal choice of instrumentation will depend on the volcanic system to which it is applied and the sought measurement parameters. Common algorithms for determining trace gas column densities, gas burdens, and volcanic emission rates from measurements of spectral radiance are outlined and compared, showing how some algorithms attempt to model the physics of the measurement while others maximize sensitivity. Several examples demonstrate how remote sensing measurements continue to advance our understanding of volcanic systems and their impact on the atmosphere. Finally, a few promising directions of inquiry are suggested that could lead to improvements in remote sensing instrumentation and analysis techniques. By combining spectroscopic and imaging techniques, improving our understanding of atmospheric radiative transfer, expanding the suite of target gases, and increasing the coverage and frequency of observations, we stand to significantly improve our ability to detect and quantify volcanic gas emissions and gain new insights into important Earth-system processes.

Journal of Volcanology and Geothermal Research↗
USGS2017

Automated cropland mapping of continental Africa using Google Earth Engine cloud computing

The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS1975

Information through color imagery

The color-sensing capability of the human eye is a powerful tool. In remote sensing we should use color to display data more meaningfully, not to re-create the scene. Color disappears with distance, and features change color with viewing angle. Color infrared film lets us apply color with additional meaning even though we introduce a false color response. Although the marginal gray scale on an ERTS (Earth Resources Technology Satellite) image may indicate balance between the green, red, and infrared bands, and although each band may be printed in a primary color, tests show that we are not fully applying the three primary colors. Therefore, contrast in the green band should be raised. For true three-color remote sensing of the Earth, we must find two generally meaningful signatures in the visible spectrum, or perhaps extend our spectral range. Before turning to costly digital processing we should explore analog processing. Most ERTS users deal with relative spectral radiance; the few concerned with absolute radiance could use the computer-compatible tapes or special annotations. NASA (National Aeronautics and Space Administration), which assigns the range and contrast to the ERTS image, controls processing and could adjust the density range for maximum contrast in any ERTS scene. NASA cannot alter processing for local changes in reflective characteristics of the Earth but could adjust for Sun elevation and optimize the contrast in a given band.

Journal of Research of the U.S. Geological Survey↗
USGS2016

Dynamic reusable workflows for ocean science

Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic notebooks across the geoscience domains.

Journal of Marine Science and Engineering↗
USGS2018

Current research in land, water, and agroecosystems: ASABE journals 2017 year in review

This article highlights current research into land and water resources, agroecosystems, and agricultural production systems published by the Natural Resources and Environmental Systems (NRES) community of ASABE journals (Transactions of the ASABE and Applied Engineering in Agriculture) in 2017. This article reviews the context, scope, and key results of the published articles and perhaps more importantly recommends areas for increased research attention. Experimental and modeling advances were described in hydrology, agroecosystems, climate-change effects, soil erosion, irrigation, drainage, forest resources, livestock systems, natural treatment systems, international water issues, and water quality topic areas. Three special collections were published (International Watershed Technology, Crop Modeling to Optimize Water Use, and Advances in Drainage). Other focal areas included 14 articles relating to livestock waste management, 13 concerning irrigated agricultural systems, 8 addressing climate change effects on land and water resources, and 16 on various aspects of soil erosion measurement and modeling. Building on the articles reviewed from 2017 and toward a vision of future agroecosystems research, the NRES community of ASABE journals strives to grow its role in making new knowledge accessible to sustain agricultural and natural systems in a changing world. In this vane, recommendations for future research direction are discussed with an emphasis on increased application of remote sensing data to agroecosystems research, improved assessment of agroecosystem resiliency and vulnerability to land and climate change, development of integrated models of agroecosystem services, meeting stubborn water management challenges in agricultural production systems, and focusing on publishing fully reproducible model results.

Transactions of the ASABE↗
USGS2018

A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m 2 , 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ± 0.004 Mg/ha). Estimated C stocks for predefined jurisdictional areas ranged from 1023 ± 39 Mg in the Nisqually National Wildlife Refuge in Washington to 507,761 ± 14,822 Mg in the Terrebonne and St. Mary Parishes in Louisiana. This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included in the coastal wetland section of the U.S. National Greenhouse Gas Inventory. With the increased availability of free post-processed satellite data, we provide a tractable means of modeling tidal marsh aboveground biomass and carbon at the global extent as well.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2015

Spatially explicit estimation of aboveground boreal forest biomass in the Yukon River Basin, Alaska

Quantification of aboveground biomass (AGB) in Alaska’s boreal forest is essential to the accurate evaluation of terrestrial carbon stocks and dynamics in northern high-latitude ecosystems. Our goal was to map AGB at 30 m resolution for the boreal forest in the Yukon River Basin of Alaska using Landsat data and ground measurements. We acquired Landsat images to generate a 3-year (2008–2010) composite of top-of-atmosphere reflectance for six bands as well as the brightness temperature (BT). We constructed a multiple regression model using field-observed AGB and Landsat-derived reflectance, BT, and vegetation indices. A basin-wide boreal forest AGB map at 30 m resolution was generated by applying the regression model to the Landsat composite. The fivefold cross-validation with field measurements had a mean absolute error (MAE) of 25.7 Mg ha −1 (relative MAE 47.5%) and a mean bias error (MBE) of 4.3 Mg ha −1 (relative MBE 7.9%). The boreal forest AGB product was compared with lidar-based vegetation height data; the comparison indicated that there was a significant correlation between the two data sets.

Alaska↗
USGS2026

Topography-aware registration refinement of Landsat 8/9 L1TP products with Sentinel-2 L1C and NAIP orthoimages

Satellite orthoimage products are widely available, yet residual geometric misregistration across independently processed products remains common, limiting their direct interoperability for multi-source and multi-temporal analysis. This paper introduces a residual Multi-Layer Perceptron (MLP)–based, topography-aware framework to further increase the registration quality of existing orthoimages based on dense matching without requiring any sensor-specific metadata or physical imaging parameters. We evaluate our approach by precisely co-registering 26 Landsat 8/9 Collection 2 Level-1 Terrain Precision (L1TP) scenes across Indiana (USA) and Berlin (Germany) with Sentinel-2 L1C and National Agriculture Imagery Program (NAIP) orthoimages. The baseline Landsat misregistration to Sentinel-2 or NAIP typically ranges from 3 to 10 m, which is reduced to 2–4 m after applying our MLP-based refinement, outperforming conventional polynomial and Rational Polynomial Coefficient (RPC) models. Further empirical evaluation demonstrates that consistent registration precision can be achieved across various terrain datasets; specifically, 30 m global digital elevation models (DEMs) yield comparable performance to that of high resolution (1–10 m) national or regional DEMs. The developed MLP approach provides a precise and reliable framework that transcends Landsat-specific tasks. It is readily adaptable to other registration applications requiring high-fidelity geometric alignment and consistent integration of heterogeneous image sources.

Indiana↗
USGS2008

Soil sail content estimation in the Yellow River delta with satellite hyperspectral data

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl 2 . Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487–1527, 1971–1991, 2032–2092, and 2163–2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSE C = 0.753 ( R 2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSE V = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 ( R 2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area.

Canadian Journal of Remote Sensing↗
USGS2007

A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images

On 31 May 2003, the Landsat Enhanced Thematic Plus (ETM+) Scan Line Corrector (SLC) failed, causing the scanning pattern to exhibit wedge-shaped scan-to-scan gaps. We developed a method that uses coincident spectral data to fill the image gaps. This method uses a multi-scale segment model, derived from a previous Landsat SLC-on image (image acquired prior to the SLC failure), to guide the spectral interpolation across the gaps in SLC-off images (images acquired after the SLC failure). This paper describes the process used to generate the segment model, provides details of the gap-fill algorithm used in deriving the segment-based gap-fill product, and presents the results of the gap-fill process applied to grassland, cropland, and forest landscapes. Our results indicate this product will be useful for a wide variety of applications, including regional-scale studies, general land cover mapping (e.g. forest, urban, and grass), crop-specific mapping and monitoring, and visual assessments. Applications that need to be cautious when using pixels in the gap areas include any applications that require per-pixel accuracy, such as urban characterization or impervious surface mapping, applications that use texture to characterize landscape features, and applications that require accurate measurements of small or narrow landscape features such as roads, farmsteads, and riparian areas.

International Journal of Remote Sensing↗
USGS2022

Development of the LCMAP annual land cover product across Hawai'i

Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various remote sensing sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at https://doi.org/10.5066/P91E8M23 .

Hawaii↗
USGS2011

Habitat suitability and nest survival of white-headed woodpeckers in unburned forests of Oregon

We evaluated habitat suitability and nest survival of breeding white-headed woodpeckers ( Picoides albolarvatus ) in unburned forests of central Oregon, USA. Daily nest-survival rate was positively related to maximum daily temperature during the nest interval and to density of large-diameter trees surrounding the nest tree. We developed a niche-based habitat suitability model (partitioned Mahalanobis distance) for nesting white-headed woodpeckers using remotely sensed data. Along with low elevation, high density of large trees, and low slope, our habitat suitability model suggested that interspersion–juxtaposition of low- and high-canopy cover ponderosa pine ( Pinus ponderosa ) patches was important for nest-site suitability. Cross-validation suggested the model performed adequately for management planning at a scale >1 ha. Evaluation of mapped habitat suitability index (HSI) suggested that the maximum predictive gain (HSI = 0.36), where the number of nest locations are maximized in the smallest proportion of the modeled landscape, provided an objective initial threshold for identification of suitable habitat. However, managers can choose the threshold HSI most appropriate for their purposes (e.g., locating regions of low–moderate suitability that have potential for habitat restoration). Consequently, our habitat suitability model may be useful for managing dry coniferous forests for white-headed woodpeckers in central Oregon; however, model validation is necessary before our model could be applied to other locations.

Journal of Wildlife Management↗
USGS2010

A self-trained classification technique for producing 30 m percent-water maps from Landsat data

Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.

International Journal of Remote Sensing↗
USGS2020

Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements

Surface soil temperature is the soil temperature from the surface to 10 cm in depth. Surface soil temperature plays a significant role in agricultural drought monitoring, ecosystem energy transfer modeling, and global carbon cycle evaluation. Studies have been proposed to estimate surface soil temperature, but surface soil temperature monitoring within forested areas still poses a significant challenge. In this study, we proposed a surface soil temperature retrieval method using combined satellite observations and in-situ measurements for the Great Dismal Swamp (GDS). The GDS is a U.S. protected area managed and protected by the U.S. Fish and Wildlife Service. It is located along the boundary of Virginia and North Carolina, with maple gum, Atlantic white cedar, and pine pocosin as the main forest cover types. Ground-based surface soil temperature measurements were collected for these forest types from May 2015 to April 2017. Both the Land Remote Sensing Satellite (Landsat) Thermal Infrared Sensor (TIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) carry two thermal infrared (TIR) channels. The TIR channels with similar corresponding wavelengths were first fused using an improved fusing model to generate high resolution TIR measurements. Then the enterprise algorithm was applied to calculate land surface temperature (LST) from the fused TIR bands. An improved soil temperature retrieval method was applied to generate surface soil temperature based on LST and vegetation index (VI) within the study area for the three forest types. In-situ measurements were used to build the surface soil temperature retrieval method, and results were then validated. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated separately as VIs in the model to monitor surface soil temperature. The R 2 for retrieved surface soil temperature through satellite observations was 0.76, and the RMSE was 1.96 ℃ ">℃ ℃ when NDVI was integrated in the model; the R 2 was 0.78, and the RMSE was 1.85 ℃ ">℃ ℃ when EVI was used.

North Carolina, Virginia↗