Optical Satellite Observations Research Articles

Investigation on Miage/Brenva Glaciers in The Alps From 50s to-date Based on Remote-Sensing Data

Abstract. The Miage and Brenva Glaciers in Mount Blanc Massif (Italy) are two of the major glaciers in the Alps. To assess the evolution of these glaciers, we use multiple data sources from remote sensing, and we adopt different techniques to extract important information on their conditions during a long period (i.e., ~60 years). The first approach, which is based on the use of aerial photos and satellite imagery (SPOT 6), allows assessing the volume change of Miage Glacier over the period 1952–2014. The second approach, which is based on the use of optical satellite observations such as Landsat 5/8/9 and Sentinel 2, aims to evaluate the spatial variability and the temporal trends of the snow cover of both glaciers from 1984 up to now. The achieved results, that are coherent with the ones reported by the scientific literature, show that volumetric changes of Miage Glacier underwent a period of gain between 1975–1991, followed by an impressive decay phase. Concerning the snow cover analysis, our findings highlight that for both glaciers the area covered by snow reduces persistently year-by-year, especially in the case of the Miage Glacier.

Monitoring discharge from deep-sea mining ships via optical satellite observations

Monitoring discharge from deep-sea mining ships via optical satellite observations

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Disparate permafrost terrain changes after a large flood observed from space

AbstractThe 2015 spring flood of the Sagavanirktok River inundated large swaths of tundra as well as infrastructure near Prudhoe Bay, Alaska. Its lasting impact on permafrost, vegetation, and hydrology is unknown but compels attention in light of changing Arctic flood regimes. We combined InSAR and optical satellite observations to quantify subdecadal permafrost terrain changes and identify their controls. While the flood locally induced quasi‐instantaneous ice‐wedge melt, much larger areas were characterized by subtle, spatially variable post‐flood changes. Surface deformation from 2015 to 2019 estimated from ALOS‐2 and Sentinel‐1 InSAR varied substantially within and across terrain units, with greater subsidence on average in flooded locations. Subsidence exceeding 5 cm was locally observed in inundated ice‐rich units and also in inactive floodplains. Overall, subsidence increased with deposit age and thus ground ice content, but many flooded ice‐rich units remained stable, indicating variable drivers of deformation. On average, subsiding ice‐rich locations showed increases in observed greenness and wetness. Conversely, many ice‐poor floodplains greened without deforming. Ice wedge degradation in flooded locations with elevated subsidence was mostly of limited intensity, and the observed subsidence largely stopped within 2 years. Based on remote sensing and limited field observations, we propose that the disparate subdecadal changes were influenced by spatially variable drivers (e.g., sediment deposition, organic layer), controls (ground ice and its degree of protection), and feedback processes. Remote sensing helps quantify the heterogeneous interactions between permafrost, vegetation, and hydrology across permafrost‐affected fluvial landscapes. Interdisciplinary monitoring is needed to improve predictions of landscape dynamics and to constrain sediment, nutrient, and carbon budgets.

Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics to Monitor Deforestation

State-of-the-art methodologies to monitor deforestation rely mostly on optical satellite observations. High-density optical time series can enable the detection of deforestation almost as soon as it occurs. However, deforestation monitoring in the tropics can be hindered by high cloud coverage, and thus the responsiveness of managers, enforcement agencies, and scientists. To understand the implications of cloud contamination in freely available optical data we analyzed combined time series from Landsat 7, 8, and Sentinel-2 over the tropics from 2017–2021. Datasets derived for each 30 m × 30 m of the 59.4 M km2 domain include a) number of cloud-free observations per year, b) maximum consecutive days without clear imagery within a year, and c) final date of the longest waiting period. The datasets reflect where and when data gaps in optical time series exist due to cloud contamination. Scripts to access and extend the datasets are shared and documented. The datasets can be used to prioritize areas where complementary observations, such as radar imagery, are needed for implementing effective deforestation alert systems.

National-scale spatial prediction of soil organic carbon and total nitrogen using long-term optical and microwave satellite observations in Google Earth Engine

Modeling accurate and detailed soil spatial information is essential for environmental modeling, precision soil management and decision-making. In this study, we integrated long-term optical (Sentinel-2) and radar (Sentinel-1) satellite observations via the Google Earth Engine (GEE) platform for high-resolution national-scale digital mapping of soil organic carbon (SOC) and total soil nitrogen (TSN) in Austria. Our soil predictive models based on boosted regression tree (BRT) and regression kriging (RK) methods were constructed from 449 soil samples (0–20 cm) covering the study area in the LUCAS soil database and Sentinel observations synthesized with different time intervals. The different input predictors of these soil predictive models resulted in seven modeling scenarios, and their prediction performance was evaluated by a cross-validation technique. Comparative analysis indicated that satellite sensors, modeling techniques, and SAR data acquisition configurations greatly affected the model outputs. Cross-polarization and co-polarization had similar performance in TSN and SOC predictions, and their combination improved the prediction accuracy. Predictive models based on Sentinel-1 with the “ASCENDING” orbits outperformed the models involving the “DESCENDING” orbits; the prediction accuracy of the former was comparable to models involving two orbital data. The models built by Sentinel-1 and Sentinel-2 performed similarly in predicting SOC (R2 = 0.51 vs. R2 = 0.52, respectively) and TSN (their R2 were both 0.42); their synergistic utilization improved the prediction results. Models involving more years of Sentinel observations on the GEE platform provided more accurate modeling results. The best soil predictive models explained 55% and 45% of soil variability for SOC and TSN, respectively, both constructed from long-term Sentinel-1/2 observations using the RK method. The overall trends of the mapping results of the models constructed by Sentinel-1 and Sentinel-2 and their combinations were consistent. The predicted digital soil maps displayed high spatial heterogeneity: SOC and TSN—shared similar spatial patterns—were greater in high-altitude central and western regions than other regions. This study provides valuable information for revealing the effects of satellite sensors, modeling techniques and SAR configurations on mapping SOC and TSN.

Effects of optical and radar satellite observations within Google Earth Engine on soil organic carbon prediction models in Spain

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and “ASCENDING” orbits outperformed those involving copolarization, a single time period and “DESCENDING” orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping.

Irrigation Timing Retrieval at the Plot Scale Using Surface Soil Moisture Derived from Sentinel Time Series in Europe

The difficulty of calculating the daily water budget of irrigated fields is often due to the uncertainty surrounding irrigation amounts and timing. The automated detection of irrigation events has the potential to greatly simplify this process, and the combination of high-resolution SAR (Sentinel-1) and optical satellite observations (Sentinel-2) makes the detection of irrigation events feasible through the use of a surface soil moisture (SSM) product. The motivation behind this study is to utilize a large irrigation dataset (collected during the ESA Irrigation + project over five sites in three countries over three years) to analyze the performance of an established algorithm and to test potential improvements. The study’s main findings are (1) the scores decrease with SSM observation frequency; (2) scores decrease as irrigation frequency increases, which was supported by better scores in France (more sprinkler irrigation) than in Germany (more localized irrigation); (3) replacing the original SSM model with the force-restore model resulted in an improvement of about 6% in the F-score and narrowed the error on cumulative seasonal irrigation; (4) the Sentinel-1 configuration (incidence angle, trajectory) did not show a significant impact on the retrieval of irrigation, which supposes that the SSM is not affected by these changes. Other aspects did not allow a definitive conclusion on the irrigation retrieval algorithm: (1) the lower scores obtained with small NDVI compared to large NDVI were counter-intuitive but may have been due to the larger number of irrigation events during high vegetation periods; (2) merging different runs and interpolating all SSM data for one run produced comparable F-scores, but the estimated cumulative sum of irrigation was around −20% lower compared to the reference dataset in the best cases.

Optimization of Cross Correlation Algorithm for Annual Mapping of Alpine Glacier Flow Velocities; Application to Sentinel-2

Nowadays, satellite observations cover most of the Earth’s surface in a repetitive manner. This information is crucial for documenting variability and environmental changes such as glacier surface velocity. With this in mind, digital image processing has been developed and improved over the past decades. The processing challenges are now related to optimizing parameters that account for the high variability of natural processes, as well as filtering and aggregating the results to provide useful products to end-users. Based on the normalized cross-correlation (NCC) method applied to Sentinel-2 optical satellite observations up to 400 days apart, we present a series of tests to derive optimal parameter values for the quantification of alpine glacier ice velocity that we have applied to the Mont-Blanc massif where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements are available. We found that a search distance adapted to the temporal baseline, a 16×16 pixel window size and a 5×5 pixels sampling provide an appropriate combination of parameters to process Sentinel-2 with the NCC method when applied to small alpine glaciers. Combining several spatial and temporal filters applied to a large set of more than 18,000 displacement maps obtained between 2015 and 2020, then aggregating these filtered maps using statistical or linear regressions into annual maps, yields near-complete maps of the test region with a RMSE reduced to about 12 m.yr <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> compared to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements.

Surface Water Dynamics from Space: A Round Robin Intercomparison of Using Optical and SAR High-Resolution Satellite Observations for Regional Surface Water Detection

Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

Availability of Sentinel-2-based time-series observations: which vegetation phenology-based metrics perform best for mapping farming systems in complex landscapes?

ABSTRACT The characterization of intra- and interannual variations in optical satellite observations has proven to be effective to differentiate between land use classes. However, there is relatively little knowledge of the accuracy and usefulness of vario us satellite-based phenology variables and metrics. This letter provides quick insights into the processing of vegetation seasonality and phenology data, applied to the European Space Agency (ESA) Sentinel-2 satellite observations. The following time-series data methods were compared for their potential to enhance land use mapping in two study areas in Kenya and Tanzania: a) monthly maximum Normalized Difference Vegetation Index (NDVI) composites, b) NDVI-based time-series statistics, c) NDVI-based harmonic regressions, and d) threshold-based NDVI-based phenological metrics. When using the maximum NDVI composites, as classification inputs, the overall accuracies were found to be 7–18% higher than when using the other methods (predictors). Overall, the Muranga site (Kenya) showed a higher classification accuracy for the best performing predictor set (overall accuracy = 86.5%) than the Kilimanjaro site (Tanzania) (overall accuracy = 80.9%). These findings confirm the potential of phenology-based compositing methods for large-scale mapping of agro-ecological farming systems.

A North American dust emission climatology (2001–2020) calibrated to dust point sources from satellite observations

• Modelled Dust emission occurs mostly from the Great Plains and semi-arid deserts. • Wind friction changes spatially with varying vegetation roughness and surface winds. • Deserts relatively smooth, dust activated in areas prone to high wind speeds. • Great Plains relatively high winds, with dust vegetation roughness limited. Measurements of atmospheric dust have long influenced our understanding of dust sources and dust model calibration. However, assessing dust emission magnitude and frequency may reveal different dust source dynamics and is critical for informing land management. Here we use MODIS (500 m) albedo-based daily wind friction estimates to produce a new dust emission climatology of North America (2001–2020), calibrated by the novel use of dust point sources from optical satellite observations (rather than being tuned to dust in the atmosphere). Calibrated dust emission occurred predominantly in the biomes of the Great Plains (GP) and North American Deserts (NAD), in broad agreement with maps of aerosol optical depth and dust deposition but with considerably smaller frequency and magnitude. Combined, these biomes produced 7.2 Tg y -1 with contributions split between biomes (59.8% NAD, 40.2% GP) due to the contrasting conditions. Dust emission is dependent on different wind friction conditions on either side of the Rocky Mountains. In general, across the deserts, aerodynamic roughness was persistently small and dust sources were activated in areas prone to large wind speeds; desert dust emissions were wind speed limited. Across the Great Plains, large winds persist, and dust emission occurred when vegetation cover was reduced; vegetated dust emissions were roughness limited. We found comparable aerodynamic roughness exists across biomes/vegetation classes demonstrating that dust emission areas are not restricted to a single biome, instead they are spread across an ‘envelope’ of conducive wind friction conditions. Wind friction dynamics, describing the interplay between changing vegetation roughness (e.g., due to climate and land management) and changing winds (stilling and its reversal), influence modelled dust emission magnitude and frequency and its current and future climatology. We confirm previous results that in the second half of the 21st century the southern Great Plains is the most vulnerable to increased dust emission and show for the first time that risk is due to increased wind friction (by decreased vegetation roughness and / or increased wind speed). Regardless of how well calibrated models are to atmospheric dust, assuming roughness is static in time and / or homogeneous over space, will not adequately represent current and future dust source dynamics.

Integrating optical and microwave satellite observations for high res-olution soil moisture estimate and applications in CONUS drought analyses

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.

Global Change in Terrestrial Ecosystem Detected by Fusion of Microwave and Optical Satellite Observations

The detection of global land change via satellite observation is a major challenge in improving the understanding of global environmental change. In this study, we develop a new vegetation index which can be used as a proxy for the fractions of tree canopy and short vegetation, based on the simple linear regression between microwave vegetation optical depth (VOD) and optical leaf area index (LAI). Although we use no high-resolution reference data, the newly developed vegetation index successfully detects global land change which has been reported by previous estimations based on high-resolution reference data. We find that the relationship between VOD and LAI is non-stationary and the temporal change in the VOD-LAI relationship is an important signal for detecting global change in the terrestrial ecosystem.

Large scale mapping of soil organic carbon concentration with 3D machine learning and satellite observations

Canada has extensive forests and peatlands that play key roles in global carbon cycle. Canadian soils and peatlands are assumed to store approximately 20% of the world’s soil carbon stock. However, the spatial and vertical distributions of the soil organic carbon (SOC) concentration in Canada have not been systematically investigated. SOC concentration, expressed in g C kg−1 soil, affects the chemical and physical properties of the soil, such as water infiltration ability, moisture holding capacity, nutrient availability, and the biological activity of microorganisms. In this study, we tested a three dimensional (3D) machine learning approach and 40 spatial predictors derived from 20 years of optical and microwave satellite observations to estimate the spatial and vertical distributions of SOC concentration in Canada in six depth intervals up to 1 m. A quantile regression forest method was used to build an uncertainty map showing 80% of prediction intervals. Results showed that a random forest model associated with 25 covariates was successful in capturing 83% of spatial and vertical SOC variation over the country. Soil depth was the most important covariate to predict SOC concentration, followed by surface temperature and elevation. The SOC concentration in forested areas was highest in the top layers (0–5 cm), but soils located in peatlands showed higher C concentration in all soil depths. Among the terrestrial ecozones of Canada, Pacific Maritime and the Hudson Plain mostly covered by forest trees and peatlands, respectively, show highest SOC concentration, while the lowest concentration are observed in the Prairies and Mixed Wood Plain ecosystems that are the agricultural areas of the country. This study provides a deeper understanding of the major factors controlling SOC concentration in Canada and shows potential areas with high carbon reserves, which are crucial in view of the ongoing climate change. In addition, the presented methodological framework has great potential to be used in future soil carbon storage inventories using satellite observations. Mapping SOC concentration and associated uncertainties in Canada are fundamental to detect trends of gains or losses in SOC linked to recent and future national or global policy decisions related to soil quality and carbon sequestration.

Satellite and UAV-based remote sensing for assessing the flooding risk from Tibetan lake expansion and optimizing the village relocation site

Climate change in recent decades led to the remarkable expansions for most lakes in endorheic basins of the Tibetan Plateau (TP). Enlarged lake inundation areas may pose adverse effects and potential threats on the local human living environment, especially for high-risk villages adjacent to rapidly expanding lakes. Taking a rapidly expanding lake, Angzi Co in the central TP as a study case, we investigated the flooding risk of lake growth on the local living environment and proposed an optimized solution of village relocation selection on the basis of satellite and unmanned aerial vehicle (UAV) remote sensing. The detection of spatiotemporal variations of Angzi Co using optical and altimetric satellite observations revealed a significant area and water level increase by 81.28 km2 and 5.78 m, respectively, from 2000 to 2020. We also assessed the vertical accuracy of multi-source digital elevation model (DEM) products using Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) altimetry data and further examined the flooding risk and potential influences of lake expansion on adjacent settlements (Guozha Village). Results indicated that UAV-DEM achieves excellent advantages in depicting details of lake shoreline variations and simulating potential submergence regions, followed by Advanced Land Observing Satellite World 3D DEM (AW3D DEM). Moreover, assuming that Angzi Co maintains the water level at a growth rate of 0.29 m/a (the average change rate during 2000–2020), the village will be submerged in approximate 10 years based on our assessment. Furthermore, we designed an optimal relocation site southwest of Guozha Village and approximately 3 km away based on the GIS-MVDA method and field investigations. An initial remote sensing-based approach for assessing the flooding risk from dramatic lake expansions in the TP and optimizing the village relocation site was proposed in this study to provide an essential scientific reference for formulating risk mitigation solutions under future climate change scenarios.

Cloud Cover throughout All the Paddy Rice Fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI Optical Observations

Cloud cover hinders the effective use of vegetation indices from optical satellite-acquired imagery in cloudy agricultural production areas, such as Guangdong, a subtropical province in southern China which supports two-season rice production. The number of cloud-free observations for the earth-orbiting optical satellite sensors must be determined to verify how much their observations are affected by clouds. This study determines the quantified wide-ranging impact of clouds on optical satellite observations by mapping the annual total observations (ATOs), annual cloud-free observations (ACFOs), monthly cloud-free observations (MCFOs) maps, and acquisition probability (AP) of ACFOs for the Sentinel 2 (2017–2019) and Landsat 8 (2014–2019) for all the paddy rice fields in Guangdong province (APRFG), China. The ATOs of Landsat 8 showed relatively stable observations compared to the Sentinel 2, and the per-field ACFOs of Sentinel 2 and Landsat 8 were unevenly distributed. The MCFOs varied on a monthly basis, but in general, the MCFOs were greater between August and December than between January and July. Additionally, the AP of usable ACFOs with 52.1% (Landsat 8) and 47.7% (Sentinel 2) indicated that these two satellite sensors provided markedly restricted observation capability for rice in the study area. Our findings are particularly important and useful in the tropics and subtropics, and the analysis has described cloud cover frequency and pervasiveness throughout different portions of the rice growing season, providing insight into how rice monitoring activities by using Sentinel 2 and Landsat 8 imagery in Guangdong would be impacted by cloud cover.

Forest Canopy Changes in the Southern Amazon during the 2019 Fire Season Based on Passive Microwave and Optical Satellite Observations

Canopy dynamics associated with fires in tropical forests play a critical role in the terrestrial carbon cycle and climate feedbacks. The aim of this study was to characterize forest canopy dynamics in the southern Amazon during the 2019 fire season (July–October) using passive microwave-based vegetation optical depth (VOD) and three optical-based indices. First, we found that precipitation during July–October 2019 was close to the climatic means, suggesting that there were no extreme hydrometeorological events in 2019 and that fire was the dominant factor causing forest canopy anomalies. Second, based on the active fire product (MCD14ML), the total number of active fires over each grid cell was calculated for each month. The number of active fires during the fire season in 2019 was above average, particularly in August and September. Third, we compared the anomalies of VOD and optical-based indices (the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the normalized burn ratio (NBR)) against the spatiotemporal distribution of fires during July–October 2019. Spatially, the location with a concentrated distribution of significant negative VOD anomalies was matched with the grid cells with fire activities, whereas the concentrated distribution of strong negative anomalies in optical-based indices were found in both burned and unburned grid cells. When we focused on the temporal pattern over the grid cells with fire activity, the VOD and the optical-based indices behaved similarly from July to October 2019, i.e., the magnitude of negative anomalies became stronger with increased fire occurrences and reached the peak of negative anomalies in September before decreasing in October. A discrepancy was observed in the magnitude of negative anomalies of the optical-based indices and the VOD; the magnitude of optical-based indices was larger than the VOD in August–September and recovered much faster than the VOD over the grid cells with relatively low fire activity in October. The most likely reason for their different responses is that the VOD represents the dynamics of both photosynthetic (leaf) and nonphotosynthetic (branches) biomass, whereas optical-based indices are only sensitive to photosynthetic (leaf) active biomass, which recovers faster. Our results demonstrate that VOD can detect the spatiotemporal of canopy dynamics caused by fire and postfire canopy biomass recovery over high-biomass rainforest, which enables more comprehensive assessments, together with classic optical remote sensing approaches.

Monitoring Surface Soil Moisture Content over the Vegetated Area by Integrating Optical and SAR Satellite Observations in the Permafrost Region of Tibetan Plateau

Surface soil moisture (SSM), the average water content of surface soil (up to 5 cm depth), plays a key role in the energy exchange within the ecosystem. We estimated SSM in areas with vegetation cover (grassland) by combining microwave and optical satellite measurements in the central Tibetan Plateau (TP) in 2015. We exploited TERRA moderate resolution imaging spectroradiometer (MODIS) and Sentinel-1A synthetic aperture radar (SAR) observations to estimate SSM through a simplified water-cloud model (sWCM). This model considers the impact of vegetation water content (VWC) to SSM retrieval by integrating the vegetation index (VI), the normalized difference water index (NDWI), or the normalized difference infrared index (NDII). Sentinel-1 SAR C-band backscattering coefficients, incidence angle, and NDWI/NDII were assimilated in the sWCM to monitor SSM. The soil moisture and temperature monitoring network on the central TP (CTP-SMTMN) measures SSM within the study area, and ground measurements were applied to train and validate the model. Via the proposed methods, we estimated the SSM in vegetated area with an R2 of 0.43 and a ubRMSE of 0.06 m3/m3 when integrating the NDWI and with an R2 of 0.45 and a ubRMSE of 0.06 m3/m3 when integrating the NDII.

Integrating Optical and Microwave Satellite Observations for High Resolution Soil Moisture Estimate and Applications in CONUS Drought Analyses

&lt;strong&gt;In this study, optical and microwave satellite observations are integrated to estimate soil moisture at high spatial resolution and applied for drought analysis in the continental United States. To estimate soil moisture, a new refined model is proposed to estimate soil moisture (SM) with auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed SM model using accumulated precipitation demonstrated close agreements with the &lt;/strong&gt;&lt;strong&gt;U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively high spatial resolution, thus drought maps based on soil moisture anomalies can be obtained at high spatial resolution on daily basis and made the flash drought analysis and monitoring become possible.&lt;/strong&gt;