Landsat Scenes Research Articles

Snow Cover and Snow Persistence Changes in the Mocho-Choshuenco Volcano (Southern Chile) Derived From 35 Years of Landsat Satellite Images

Mountain regions have experienced above-average warming in the 20th century and this trend is likely to continue. These accelerated temperature changes in alpine areas are causing reduced snowfall and changes in the timing of snowfall and melt. Snow is a critical component of alpine areas - it drives hibernation of animals, determines the length of the growing season for plants and the soil microbial composition. Thus, changes in snow patterns in mountain areas can have serious ecological consequences. Here we use 35 years of Landsat satellite images to study snow changes in the Mocho-Choshuenco Volcano in the Southern Andes of Chile. Landsat images have 30 m pixel resolution and a revisit period of 16 days. We calculated the total snow area in cloud-free Landsat scenes and the snow frequency per pixel, here called “snow persistence” for different periods and seasons. Permanent snow cover in summer was stable over a period of 30 years and decreased below 20 km2 from 2014 onward at middle elevations (1,530–2,000 m a.s.l.). This is confirmed by negative changes in snow persistence detected at the pixel level, concentrated in this altitudinal belt in summer and also in autumn. In winter and spring, negative changes in snow persistence are concentrated at lower elevations (1,200–1,530 m a.s.l.). Considering the snow persistence of the 1984–1990 period as a reference, the last period (2015–2019) is experiencing a −5.75 km2 reduction of permanent snow area (snow persistence > 95%) in summer, −8.75 km2 in autumn, −42.40 km2 in winter, and −18.23 km2 in spring. While permanent snow at the high elevational belt (>2,000 m a.s.l.) has not changed through the years, snow that used to be permanent in the middle elevational belt has become seasonal. In this study, we use a probabilistic snow persistence approach for identifying areas of snow reduction and potential changes in alpine vegetation. This approach permits a more efficient use of remote sensing data, increasing by three times the amount of usable scenes by including images with spatial gaps. Furthermore, we explore some ecological questions regarding alpine ecosystems that this method may help address in a global warming scenario.

The Methodology and Results from Ground Validation of Satellite Observations at Gas Flaring Sites in Nigeria

This study examines the need/importance for ground validation of Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) observation at 2 gas flaring sites in Rivers State of Niger Delta, Nigeria. 12 Landsat imageries (3 Landsat 5 TM and 9 Landsat 7 ETM+) acquired from 25/03/1987 to 08/03/2013 with < 5 % cloud contamination were used. Both sites are located within a single Landsat scene (Path 188, Row 057). Fieldwork measurements and observations at both sites took place from (04/08/2012-21/09/2012) and (05/08/2019-22/09/2019). The parameters measured are coordinates of points and features, air temperature and relative humidity; and photographs of locations and features were taken. Both air temperature and relative humidity were measured at 3 different levels above the ground surface at 1 minute interval. The closer the distance to the flare stack, the higher the air temperature measured and vice versa. Land Surface Temperature (LST) retrieved from Landsat 5 TM and Landsat 7 ETM+ also shows that the closer the distance to the flare stack, the higher the LST and vice versa. The locational error of points obtained from Landsat 5 TM and Landsat 7 ETM+, and fieldwork measurements give negligible difference of 1.0 × 10-6 to 7.3 × 10-6. Several features, and 4 land use and land cover (LULC) within the sites were clarified. The pattern of plumes from the flare stacks moves outwardly at both sites. Results show that the spatial variability in ground air temperature and derived LST from Landsat 5 TM and Landsat 7 ETM+ differs within 0.8 to 6.0 K because air temperature is different from LST. The results of locational error, LULC, air temperature, LST, and the pattern of plumes observed at both sites show that ground validation through measurement of in-situ data is indispensable to the use of Landsat 5 TM and Landsat 7 ETM+ data and remote sensing technology in the Niger Delta.

Multi-sensor fusion using random forests for daily fractional snow cover at 30 m

In addition to providing water for nearly 2 billion people, snow drives resource selection by wildlife and influences the behavior and demography of many species. Because snow cover is highly spatially and temporally variable, mapping its extent using currently available satellite data remains a challenge. At present, there are no sensors acquiring daily data of Earth's entire surface at fine spatial resolutions (< 30 m) in wavelengths required for snow cover retrieval, namely: visible, near-infrared, and shortwave infrared. Fine scale observations at 30 m from Landsat are available at 16-day intervals since 1982 and at 8-day intervals since 1999. However, over this duration, snow can accumulate, ablate, or both, making the Landsat data ineffective for many applications. Conversely, the Moderate Resolution Imaging Spectroradiometer (MODIS) atmospherically corrected daily reflectance data, have a coarse spatial resolution of 463 m and thus, are not ideal for snow cover mapping either. This spatial and temporal resolution tradeoff limits the use of these data for a wide range of snow cover applications and indicates a pressing need for data fusion. To address this need, we use a physically-based, spectral-mixture-analysis approach for mapping fractional snow cover (fSCA) and a two-stage random forest algorithm to produce daily 30 m fSCA. We test our algorithm in the US Sierra Nevada and find MODIS fSCA is the most important predictor. We cross validate using 170 Landsat scenes and while snow cover varies immensely in time we find little variation in errors between seasons, a small bias of 0.01, and an overall accuracy of 0.97 with slightly higher precision than recall. This technique for accurate, daily, high-resolution snow cover retrievals could be applied more broadly for analyses of regional energy budget, validating snow cover in global and regional models, and for quantifying changes in the availability of biotic resources in ecosystems.

REVISITING THE DEMOCRATIC REPUBLIC OF THE CONGO STRATIFICATION MAP FOR THE YEAR 2000 USING CLOUD-BASED COMPOSITING AND OBJECT-BASED CLASSIFICATION ALGORITHMS

National stratification maps are essential to improve forest management systems. For the Democratic Republic of the Congo, the existing maps derived from remote sensing techniques do not allow an optimal representation of the diverse land cover classes constituting the national stratification scheme. This situation is inherent to the cloud persistence, the seasonality effects and the spatial resolution of the input satellite imagery used that is not always adequate for the discrimination of certain land cover classes. This paper explores a cloud-based median luminance best pixel approach to obtain a cloud-free mosaic of optimal quality. The mosaic produced has necessitated nearly 2,500 Landsat scenes and a following object-based classification enabled the generation of a stratification map for the year 2000 according to the national stratification theme. A stratified random sampling approach that required 1,141 reference samples allowed estimating the map accuracy at 79.32%. Land cover classes areas computed using standard good practices recommendations to estimate land areas indicated that the dense moist forest area was about 158,810,975 ± 7,460,671 ha representing 68.40% ± 3.21% of the country area. Thanks to the free, user-friendly and cloud-based platforms for satellite images processing, the methodology implemented is easily replicable for other tropical countries.

Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors

ABSTRACT Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes were processed within the Google Earth Engine (GEE). For the collection of ground truth data, differential manifestations of green cover were considered by dividing the study area into five climatic regions and the training samples were taken from each sub-region. To account for the temporal variation of LULC types, six two-month interval composite layers, including the spectral and thermal bands of Landsat-8, texture and spectral indices, as well as topographic factors were created for the target year 2019. Image segmentation and classification were performed using the simple non-iterative clustering (SNIC) and Random Forest (RF) algorithms, respectively. A computationally effective parallel processing approach was developed, which created a number of tiles and sub-tiles and a bulk command was converted into smaller parallel commands. The generated LULC map showed a satisfactory overall accuracy of 91.7%, with the highest User’s accuracy in water and wetland, and the lowest in rainfed crop and rangeland and the highest Producer’s accuracy in water and barren areas, and the lowest in garden and rangeland. This study highlights the necessity of using multi-temporal data for LULC mapping, in particular, multi-temporal NDVI, for the separation of different green cover types in arid and semi-arid environment.

Surface water extent dynamics from three periods of continuous Landsat time series; subregional differences across Argentine plains

&lt;p&gt;The Pampean region in Argentina is an extensive plain characterized by abundant shallow lakes that fulfill many environmental, ecological, and social functions. This study aims to detect the multiannual lake area changes in this region during 2001-2009 using remote sensing, including lakes as small as ≥10,000 m&lt;sup&gt;2&lt;/sup&gt; or 1 ha. Landsat scenes of the wet (2008-2009), normal (2006), and dry (2008-2009) seasons were obtained, and using remote sensing techniques, the number and area of shallow lakes were calculated. The spatiotemporal variation of shallow lakes was studied in different climate periods in eight singular subregions. Spatial associations between annual precipitation and lake number and area were analyzed through the development of a Geographic Information System (GIS) at a subregional scale. During the study period the total lake area in the Pampean region decreased by 5257.39 km&lt;sup&gt;2 &lt;/sup&gt;(62 %), but each subregion showed different responses to climatic events. In seven of them, the differences between climate periods prove to be statistically significant (P&amp;gt;0.01). The relationship between precipitation and lake number and area revealed the domain of positive association. We conclude that climate factors play a dominant role in lake changes across the Pampean plains. However, other factors such as origin, topographic and edaphic characteristics intensify or mitigate changes in surface hydrology.&lt;/p&gt;

Dynamics of active fire data and their relationship with fires in the areas of regularized indigenous lands in the Southern Amazon

An increase in active fire data in the Amazon puts the entire international community on alert, as they may result in fires and deforestation. Thus, this study aims to present the dynamics of active fire data and their relationship with the fires in 16 Regularized Indigenous Lands (RILs) located entirely in the state of Rondônia, in the southern amazon. The entire historical series of active fire data (from 1998 to 2020) were obtained to achieve the objectives, and nine Landsat-OLI scenes from September 2020 were processed. The active fire data dynamics revealed that the correlation coefficient between the total number of active fire data in the immediate surroundings (10 km) and the number of active fire data within the analyzed indigenous land areas was higher than 0.7. This means that the variation in the number of fire outbreaks within indigenous lands can be explained by the number of outbreaks occurring outside them. Image processing revealed 11 classes of land use and land cover with an overall accuracy of 92%. Based on the mapping of land use and cover for the nine Landsat scenes, it is possible to verify that the burnt area totaled 3,943.50 km2. Besides, 953.90 km2 (24.19%) were located entirely within the indigenous lands, mainly on the Massaco and Uru Eu Wau Wau indigenous lands. By analyzing the density of active fire data, it is possible to verify that most of the outbreaks are located at the edges of the RILs. This shows that these areas work as a barrier to the spread of fire in the South of the Brazilian Amazon. Thus, despite the identification of regions burnt within indigenous lands, the study concluded that RILs are essential for protecting the Amazon's native vegetation.

Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer

A timely and detailed crop-specific land cover map can support many agricultural applications and decision makings. However, in-season crop mapping over a large area is still challenging due to the insufficiency of ground truth in the early stage of a growing season. To address this issue, this paper presents an efficient machine-learning workflow for the rapid in-season mapping of corn and soybeans fields without ground truth data for the current year. We use trusted pixels, a set of pixels that are predicted from the historical Cropland Data Layer (CDL) data with high confidence in the current year’s crop type, to label training samples on multi-temporal satellite images for crop type classification. The entire mapping process only involves a limited number of satellite images acquired within the growing season (normally 3–4 images per scene) and no field data needs to be collected. According to the investigation on 12 states of the U.S. Corn Belt, it is found that a considerable number of trusted pixels can be identified from the historical CDL data by the trusted pixel prediction model based on artificial neural network. According to the experiment on 49 Landsat-8 scenes and 31 Sentinel-2 tiles, the in-season maps of corn and soybeans are expected to reach 85%–95% agreement with CDL as well as field data by mid-July. Once the in-season satellite imagery becomes available, the crop cover map can be rapidly created even with limited computational resources. This study provides a new perspective and detailed guidance for rapid in-season mapping of corn and soybeans, which can be potentially applied to identify more diverse crop types and scaled up to the entire United States.

Drought severity trend analysis based on the Landsat time-series dataset of 1998-2017 in the Iraqi Kurdistan Region

Drought is a natural hazard that significantly impacts economic, agricultural, environmental, and social aspects and is characteristic of Iraq’s climate, particularly the Iraqi Kurdistan Region (IKR). For studying the spatiotemporal characteristics of drought severity in the IKR, a time-series of 120 Landsat images (TM, 7 ETM+, and OLI sensors) over twenty years (1998-2017) was assembled. Twenty separate mosaics of six Landsat scenes were used to derive the Vegetation Condition Index (VCI). The VCI index was employed to capture the drought severity in the study area. Results revealed that 1999, 2000, and 2008 were the most severe drought years. The results also indicated that severe droughts increased by 29.1%, 25.0%, and 26.9 through 1999, 2000, and 2008, respectively. Furthermore, a drop in precipitation averages occurred in the two years and significantly reduced the VCI values. Statistical analysis exhibited significant correlations between the VCI and each precipitation, and crop yield was 0.81 and 0.478, respectively. It can be concluded that the IKR experienced severe to extremely severe agricultural droughts, which caused significant reductions in crop yields, particularly in 2000 and 2008.

Combining UAV and Landsat data to assess glacier changes on the central Tibetan Plateau

AbstractIn recent years, researchers have focused on the applications of uncrewed aerial vehicles (UAVs) in environmental remote sensing tasks. However, studies on glacier monitoring using UAV technology are relatively scarce, especially for high mountain glacier monitoring. To explore the feasibility of UAV technology for high mountain glaciers, four UAV surveys were deployed on two glaciers of the central Tibetan Plateau. Based on the images retrieved by UAV in 2017 and 2019, orthomosaics and digital elevation models were produced to quantify the length, area and elevation changes in the ablation zone of these two glaciers at different times. Additionally, we utilized several Landsat scenes to derive glacier changes over the last 30 years and combined these with the UAV data to assess the advantages and disadvantages of UAV technology in mountain glacier monitoring.

MOSEV: a global burn severity database from MODIS (2000–2020)

Abstract. To make advances in the fire discipline, as well as in the study of CO2 emissions, it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. In this work we present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products from November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the normalized burn ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR) and its relativized version (RdNBR), as well as the post-burn NBR, which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR, and the date of burning. Moreover, in this work we have compared the burn severity metrics included in MOSEV (dNBR, RdNBR and post-burn NBR) with the same ones obtained from Landsat-8 scenes which have an original resolution of 30 m. We calculated the Pearson's correlation coefficients and the significance of the relationships using 13 pairs of Landsat scenes randomly distributed across the globe, with a total BA of 6904 km2 (n=32 163). Results showed that MOSEV and Landsat-8 burn severity indices are highly correlated, particularly the post-burn NBR (R=0.88; P&lt;0.001), and dNBR (R=0.74; P&lt;0.001) showed stronger relationships than RdNBR (R=0.42; P&lt;0.001). Differences between MOSEV and Landsat-8 indices are attributable to variability in reflectance values and to the different temporal resolution of both satellites (MODIS: 1–2 d; Landsat: 16 d). The database is structured according to the MODIS tiling system and is freely downloadable at https://doi.org/10.5281/zenodo.4265209 (Alonso-González and Fernández-García, 2020).

Mapping and Monitoring the Multi-Decadal Dynamics of Australia’s Open Waterbodies Using Landsat

Water detection algorithms are now being routinely applied to continental and global archives of satellite imagery. However, water resource management decisions typically take place at the waterbody rather than pixel scale. Here, we present a workflow for generating polygons of persistent waterbodies from Landsat observations, enabling improved monitoring and management of water assets across Australia. We use Digital Earth Australia’s (DEA) Water Observations from Space (WOfS) product, which provides a water classified output for every available Landsat scene, to determine the spatial locations and extents of waterbodies across Australia. We generated a polygon set of waterbodies that identified 295,906 waterbodies ranging in size from 3125 m2 to 4820 km2. Each polygon was used to generate a time series of WOfS, providing a history of the change in surface area of each waterbody every ~16 days since 1987. We demonstrate the applications of this new dataset, DEA Waterbodies, to understanding local through to national-scale surface water spatio-temporal dynamics. DEA Waterbodies provides new insights into Australia’s water availability and enables the monitoring of important landscape features such as lakes and dams, improving our ability to use earth observation data to make meaningful decisions.

Changes in the End-of-Summer Snow Line Altitude of Summer-Accumulation-Type Glaciers in the Eastern Tien Shan Mountains from 1994 to 2016

For summer-accumulation-type glaciers, the glaciological literature is lacking studies on determining the snow line altitude (SLA) from optical images at the end of the summer as an indicator of the equilibrium line altitude (ELA). This paper presents a workflow for extracting the SLA from Landsat images based on the variation in the albedo with the altitude in the central line area of glaciers. The correlation of &gt;0.8 at the 99% confidence level between the retrieved SLAs with ELAs derived from the interpolation of ground-based, mass balance measurements indicated that the workflow can be applied to derive the SLA from end-of-summer satellite data as an indicator of ELA. The ELA was under-estimated by the calculated SLA. The relationship between the end-of-summer SLA and the ELA depends on the intensity of glacier melting. Subsequently, the workflow was applied to the seven glaciers in the Eastern Tien Shan Mountains, and a time series of the SLA was obtained using 12 end-of-summer Landsat scenes from 1994 to 2016. Over the whole study period, a mean SLA of 4011.6 ± 20.7 m above sea level (a.s.l.) was derived for the seven investigated glaciers, and an increasing SLA was demonstrated. The increase in SLAs was consistent for the seven glaciers from 1994 to 2016. Concerning the spatial variability, the east–west difference was prominent, and these differences exhibited a decreasing trend. The average SLA of each glacier is more influenced by its morpho-topographic variables. The interannual variations in the average SLA are mainly driven by the increasing summer air temperature, and the high correlation with the cumulative summer solid precipitation reflects the characteristics of the summer-accumulation-type glaciers.

Multiple Endmember Spectral Mixture Analysis (MESMA) Applied to the Study of Habitat Diversity in the Fine-Grained Landscapes of the Cantabrian Mountains

Heterogeneous and patchy landscapes where vegetation and abiotic factors vary at small spatial scale (fine-grained landscapes) represent a challenge for habitat diversity mapping using remote sensing imagery. In this context, techniques of spectral mixture analysis may have an advantage over traditional methods of land cover classification because they allow to decompose the spectral signature of a mixed pixel into several endmembers and their respective abundances. In this work, we present the application of Multiple Endmember Spectral Mixture Analysis (MESMA) to quantify habitat diversity and assess the compositional turnover at different spatial scales in the fine-grained landscapes of the Cantabrian Mountains (northwestern Iberian Peninsula). A Landsat-8 OLI scene and high-resolution orthophotographs (25 cm) were used to build a region-specific spectral library of the main types of habitats in this region (arboreal vegetation; shrubby vegetation; herbaceous vegetation; rocks–soil and water bodies). We optimized the spectral library with the Iterative Endmember Selection (IES) method and we applied MESMA to unmix the Landsat scene into five fraction images representing the five defined habitats (root mean square error, RMSE ≤ 0.025 in 99.45% of the pixels). The fraction images were validated by linear regressions using 250 reference plots from the orthophotographs and then used to calculate habitat diversity at the pixel (α-diversity: 30 × 30 m), landscape (γ-diversity: 1 × 1 km) and regional (ε-diversity: 110 × 33 km) scales and the compositional turnover (β- and δ-diversity) according to Simpson’s diversity index. Richness and evenness were also computed. Results showed that fraction images were highly related to reference data (R2 ≥ 0.73 and RMSE ≤ 0.18). In general, our findings indicated that habitat diversity was highly dependent on the spatial scale, with values for the Simpson index ranging from 0.20 ± 0.22 for α-diversity to 0.60 ± 0.09 for γ-diversity and 0.72 ± 0.11 for ε-diversity. Accordingly, we found β-diversity to be higher than δ-diversity. This work contributes to advance in the estimation of ecological diversity in complex landscapes, showing the potential of MESMA to quantify habitat diversity in a comprehensive way using Landsat imagery.

Combining Optical Remote Sensing, McFLI Discharge Estimation, Global Hydrologic Modeling, and Data Assimilation to Improve Daily Discharge Estimates Across an Entire Large Watershed

AbstractRemote sensing has gained attention as a novel source of primary information for estimating river discharge, and the Mass‐conserved Flow Law Inversion (McFLI) approach has successfully estimated river discharge in ungauged basins solely from optical satellite data. However, McFLI currently suffers from two major drawbacks: (1) existing optical satellites lead to temporally and spatially sparse discharge estimates and (2) because of the assumptions required, McFLI cannot guarantee downstream flow continuity. Hydrological modeling has neither drawback, yet model accuracy is frequently limited by a lack of discharge observations. We therefore combine McFLI and models in a data assimilation framework applicable globally. We establish a daily “ungauged” baseline model for 28,998 reaches of the Missouri river basin forced by recently published global runoff data, which we do not calibrate. We estimate discharge via McFLI using ∼1 million width measurements made from 12,000 Landsat scenes and assimilate McFLI into the model before validating at 403 USGS gauges. Results show that assimilated discharges did not impair already accurate baseline flows and achieved median improvements of 28% normalized root mean square error, 0.50 Nash–Sutcliffe efficiency (NSE), and 0.23 Kling–Gupta efficiency where baseline performance was poor (defined as baseline negative NSE, 225/403 reaches). We ultimately improved flows at 92% of these originally poorly modeled gauges, even though Landsat images only provide McFLI discharges at 1.5% of reaches and 26% of simulated days. Our results suggest that the combination of McFLI and state‐of‐the‐art hydrology models can improve flow estimations in ungauged basins globally.

Daily Actual Evapotranspiration Estimation in a Mediterranean Ecosystem from Landsat Observations Using SEBAL Approach

Quantifying actual evapotranspiration (ETa) over natural vegetation is crucial in evaluating the water status of ecosystems and the water-use patterns in local or regional hydrological basins. Remote sensing-based surface energy balance models have been used extensively for estimating ETa in agro-environments; however, the application of these models to natural ecosystems is still limited. The surface energy balance algorithm for land (SEBAL) physical-based surface energy balance model was applied to estimate the actual evapotranspiration over a heterogeneous coverage of Mediterranean maquis in a natural reserve in Sardinia, Italy. The model was applied on 19 Landsat 5 and 8 images from 2009 to 2014, and the results were compared to the data of a micrometeorological station with eddy covariance flux measurements. Comparing the SEBAL-based evaporative fraction (ΛS) to the corresponding tower-derived evaporative fractions (ΛT) showed good flux estimations in the Landsat overpass time (Coefficient of determination R2 = 0.77, root mean square error RMSE = 0.05 and mean absolute error MAE = 0.076). Three methods were evaluated for upscaling instantaneous latent heat flux (λE) to daily actual evapotranspiration (ETa,D). The upscaling methods use the evaporative fraction (Λ), the reference evapotranspiration fraction (EFr) and the ratio of daily to instantaneous incoming shortwave radiation (Rs24/Rsi) as upscaling factors under the hypothesis of diurnal self-preservation. A preliminary analysis performed using only in-situ measured data demonstrated that the three factors were relatively self-preserved during the daytime, and can yield good ETa,D estimations, particularly when obtained at near the Landsat scene acquisition time (≈10:00 UTC). The upscaling factors obtained from SEBAL retrieved instantaneous fluxes, and some ancillary measured meteorological data were used to upscale SEBAL-estimated instantaneous actual λ to daily ET. The Λ EFr and Rs24/Rsi methods on average overestimated the measured ETa,D by nearly 20, 61 and 18%, respectively. The performance of the Λ and Rs24/Rsi methods was considered satisfactory, bearing in mind the high variable ground cover and the inherent variability of the biome composition, which cannot be properly represented in the Landsat moderate spatial resolution. In this study, we tested the potential of the SEBAL model application in a complex natural ecosystem. This modeling approach will be used to represent the spatial dynamics of ET, which will be integrated into further environmental and hydrological applications.

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps.

A Semiphysical Approach of Haze Removal for Landsat Image

The presence of haze could seriously contaminate the observations of optical satellite imagery. Haze not only significantly affects the visual interpretation but also reduces the accuracy of map products. In this article, a semiphysical approach is proposed to reduce the haze effects for Landsat image. The proposed approach is based on the physical model of radiative transfer theory and the presence of dark objects. As the depth map of satellite remotely sensed image is almost a constant value, the coarse transmission map of atmosphere is estimated by the haze thickness, other than the scene depth map. The derived coarse transmission is utilized to correct the color shift induced by airlight. For haze veiled textural information, the guided-filter based approach is adopted to refine the coarse transmission map to restore the textural information. Experiments are conducted upon the images acquired by Landsat at different dates and spatial locations. The visual interpretation upon the dehazed results suggests that the proposed approach could generate visually promising results and preserve spectral properties of land surfaces. Moreover, it also performs favorably against several state-of-the-art deep learning based methods and a classic algorithm. The results of quantitative assessments demonstrate that the developed approach could enhance the information of Landsat scene with minimum spectrum changes. With the visual and quantitative assessments, we can conclude that the proposed approach is a promising solution for Landsat image dehazing. It is expected to improve the data quality of hazy scene and expand the usability of Landsat data.

Glacier albedo reduction and drought effects in the extratropical Andes, 1986–2020

AbstractSurface albedo typically dominates the mass balance of mountain glaciers, though long-term trends and patterns of glacier albedo are seldom explored. We calculated broadband shortwave albedo for glaciers in the central Chilean Andes (33–34°S) using end-of-summer Landsat scenes between 1986 and 2020. We found a high inter-annual variability of glacier-wide albedo that is largely a function of the glacier fractional snow-covered area and the total precipitation of the preceding hydrological year (up to 69% of the inter-annual variance explained). Under the 2010–2020 ‘Mega Drought’ period, the mean albedo, regionally averaged ranging from ~0.25–0.5, decreased by −0.05 on average relative to 1986–2009, with the greatest reduction occurring 3500–5000 m a.s.l. In 2020, differences relative to 1986–2009 were −0.14 on average as a result of near-complete absence of late summer snow cover and the driest hydrological year since the Landsat observation period began (~90% reduction of annual precipitation relative to the 1986–2009 period). We found statistically significant, negative trends in glacier ice albedo of up to −0.03 per decade, a trend that would have serious implications for the future water security of the region, because glacier ice melt acts to buffer streamflow shortages under severe drought conditions.

Combined Effects of Impervious Surface Change and Large-Scale Afforestation on the Surface Urban Heat Island Intensity of Beijing, China Based on Remote Sensing Analysis

Urban heat island (UHI) attenuation is an essential aspect for maintaining environmental sustainability at a local, regional, and global scale. Although impervious surfaces (IS) and green spaces have been confirmed to have a dominant effect on the spatial differentiation of the urban land surface temperature (LST), comprehensive temporal and quantitative analysis of their combined effects on LST and surface urban heat island intensity (SUHII) changes is still partly lacking. This study took the plain area of Beijing, China as an example. Here, rapid urbanization and a large-scale afforestation project have caused distinct IS and vegetation cover changes within a small range of years. Based on 8 scenes of Landsat 5 TM/7ETM/8OLI images (30 m × 30 m spatial resolution), 920 scenes of EOS-Aqua-MODIS LST images (1 km × 1 km spatial resolution), and other data/information collected by different approaches, this study characterized the interrelationship of the impervious surface area (ISA) dynamic, forest cover increase, and LST and SUHII changes in Beijing’s plain area during 2009–2018. An innovative controlled regression analysis and scenario prediction method was used to identify the contribution of ISA change and afforestation to SUHII changes. The results showed that percent ISA and forest cover increased by 6.6 and 10.0, respectively, during 2009–2018. SUHIIs had significant rising tendencies during the decade, according to the time division of warm season days (summer days included) and cold season nights (winter nights included). LST changes during warm season days responded positively to a regionalized ISA increase and negatively to a regionalized forest cover increase. However, during cold season nights, LST changes responded negatively to a slight regionalized ISA increase, but positively to an extensive regionalized ISA increase, and LST variations responded negatively to a regionalized forest cover increase. The effect of vegetation cooling was weaker than ISA warming on warm season days, but the effect of vegetation cooling was similar to that of ISA during cold season nights. When it was assumed that LST variations were only caused by the combined effects of ISA changes and the planting project, it was found that 82.9% of the SUHII rise on warm season days (and 73.6% on summer days) was induced by the planting project, while 80.6% of the SUHII increase during cold season nights (and 78.9% during winter nights) was caused by ISA change. The study presents novel insights on UHI alleviation concerning IS and green space planning, e.g., the importance of the joint planning of IS and green spaces, season-oriented UHI mitigation, and considering the thresholds of regional IS expansion in relation to LST changes.