Various Types Of Land Cover Research Articles

Canopy Cover Shapes Bat Diversity across an Urban and Agricultural Landscape Mosaic

SummaryHuman alteration of the planet’s terrestrial landscapes for agriculture, habitation and commerce is reshaping wildlife communities. The threat of land cover change to wildlife is pronounced in regions with rapidly growing human populations. We investigated how species richness and species-specific occurrence of bats changed as a function of land cover and canopy (tree) cover across a rapidly changing region of Florida, USA. Contrary to our predictions, we found negligible effects of agriculture and urban development on the occurrence of all species. In contrast, we found that a remotely sensed metric of canopy cover on a broad scale (25 km2) was a good predictor of the occurrence of eight out of ten species. The occurrence of all smaller bats (vespertilionids) in our study increased with 0–50% increases in canopy cover, while larger bats showed different patterns. Occurrence of Brazilian free-tailed bats (Tadarida brasiliensis) decreased with increasing canopy cover, and Florida bonneted bats (Eumops floridanus) were not influenced by canopy cover. We conclude that remotely sensed measures of canopy cover can provide a more reliable predictor of bat species richness than land-cover types, and efforts to prevent the loss of bat diversity should consider maintaining canopy cover across mosaic landscapes with diverse land-cover types.

Can trail characteristics influence visitor numbers in natural protected areas? A quantitative approach to trail choice assessment

Abstract Planning for recreational activities in protected areas involves an understanding of multiple and complex factors. Trails constitute the main recreation facility in protected areas. They are an important and common infrastructure that concentrates visitor movement. Their sustainable planning and management requires an understanding of how the visitors made their selection. The aim of this study was to identify the effect of trail attributes on visitor numbers in the Krkonoše Mountains National Park in the Czech Republic. The methods used in this study present an analytical approach involving geographic information system analysis, field monitoring and data analyses using generalised linear models. The results showed the preferential tendencies among visitors to certain trail attributes. Marginal significance and a rather strong variability in preferences (over 10%) were identified for five trail characteristics: the amount of local attractions; diversity of land cover types; dominant land cover along a trail; soil erosion; and the type of trail surface. In our study, we illustrate an analytical framework for the assessment of trail characteristics that can help guide trail analyses and management efforts. On the other hand, our findings raise new research questions and point to the requirements for further research in order to better understand how environmental attributes influence visitor choice and to use this knowledge for trail planning and management.

Merging the MODIS and Landsat Terrestrial Latent Heat Flux Products Using the Multiresolution Tree Method

The accurate estimation of the terrestrial latent heat flux (LE) from satellite observations at high spatial and temporal scales plays an important role in the assessment of the water and heat exchange between the earth’s surface and the atmosphere. Although a variety of data fusion methods have been proposed to merge different LE products for more reliable estimates, most of them have ignored the spatiotemporal consistency of LE products across different resolutions. In this paper, we apply the multiresolution tree (MRT) method to improve the accuracy and reduce the inconsistency between the Moderate Resolution Imaging Spectroradiometer (MODIS) LE (MOD16) product and the Landsat-based LE product at different resolutions. Eddy covariance (EC) ground measurements at five sites, MODIS and Landsat images from January 2005 to December 2005 in the north central USA, are used to evaluate the performance of the MRT method. The results show that the MRT method can improve the accuracy of the original LE products (MOD16 and Landsat), and it has the potential to significantly reduce the uncertainty and inconsistency of these products. The bias decreased by 38.3% on average, and the root-mean-square error (RMSE) decreased by approximately 49.2% after the MRT was applied at each scale. Further studies are still required to make the MRT method more universal on a variety of land cover types for long-time periods.

Evaluating the effects of turf-replacement programs in Los Angeles

Water utilities incentivize turf replacement to promote water conservation, but the effects of such programs have received limited evaluations. In 2014, the Metropolitan Water District of Southern California (MWD) undertook an unprecedented investment to incentive turf replacement throughout Southern California in response to a serious Statewide drought. MWD devoted $350 million to the program, resulting in more than 46,000 rebate payments (25,000 in Los Angeles County) to remove 15.3 million square meters of turf. The program implementation provided a unique opportunity to address research gaps on turf replacement implementation. We analyzed socioeconomic and spatial trends of program participants and assessed landscape changes from turf replacement using a random sample of properties (4% of LA County participants in 2014–16). Specifically, we used a novel and cost-effective approach Google Earth Street View to characterize landscapes in front yards and created a typology of land cover types. Results showed: post-replacement landscapes had a diversity of land cover types – diverse yards with several land cover types, as well as more homogenous yards with a single land cover such as woodchips, bare soil, gravel, and artificial turf. Analysis also indicated some evidence of “neighborhood adoption” effects. We describe the need for longitudinal studies to understand long-term effects of turf replacement and associated water use, and suggest that water utilities should also evaluate results in backyards, which requires site visits. This study provides a novel contribution that can be replicated over space and time to further knowledge of turf replacement program implementations and evaluation.

Exploring fine-scale human and livestock movement in western Kenya

Human and livestock mobility are key factors in the transmission of several high-burden zoonoses such as rift valley fever and trypanosomiasis, yet our knowledge of this mobility is relatively poor due to difficulty in quantifying population-level movement patterns. Significant variation in the movement patterns of individual hosts means it is necessary to capture their fine-scale mobility in order to gain useful knowledge that can be extrapolated to a population level. Here we explore how the movements of people and their ruminants, and their exposure to various types of land cover, correlate with ruminant ownership and other demographic factors which could affect individual exposure to zoonoses. The study was conducted in Busia County, western Kenya, where the population are mostly subsistence farmers operating a mixed crop/livestock farming system. We used GPS trackers to collect movement data from 26 people and their ruminants for 1 week per individual in July/August 2016, and the study was repeated at the end of the same year to compare movement patterns between the short rainy and dry seasons respectively. We found that during the dry season, people and their ruminants travelled further on trips outside of the household, and that people spent less time on swampland compared to the short rainy season. Our findings also showed that ruminant owners spent longer and travelled further on trips outside the household than non-ruminant owners, and that people and ruminants from poorer households travelled further than people from relatively wealthier households. These results indicate that some individual-level mobility may be predicted by season and by household characteristics such as ruminant ownership and household wealth, which could have practical uses for assessing individual risk of exposure to some zoonoses and for future modelling studies of zoonosis transmission in similar rural areas.

Characteristics of feeding sites of California Condors (Gymnogyps californianus) in the human-dominated landscape of southern California

Wildlife conservation is often improved by understanding the movement ecology of species and adapting management strategies to dynamic conditions associated with movement. Despite a remarkable recovery over the past 30 years, the establishment of self-sustaining populations of California Condors (Gymnogyps californianus) has been challenging in the human-dominated landscapes of southern California. Among these challenges are those imposed by condor ground-foraging behavior that exposes them to environmental contamination. These include lead poisoning from the ingestion of spent ammunition and micro-trash ingestion and, during takeoff and landing, collisions with human structures. We tracked 28 California Condors for 24 months with patagially mounted GPS telemetry units to investigate the characteristics of ground sites condors visited and to identify spatiotemporal trends that might aid in conservation of this critically endangered species. Ground sites occurred on a wide variety of land cover types, primarily on steep slopes, and those more frequently used were associated with open cover. Condors concentrated their visits to ground sites around a 3 h period near midday, and usage increased from winter to late summer. Our study is the first to use remotely sensed telemetry data to describe fine-scale ecological correlates of condor ground-foraging ecology and therefore has important relevance for ongoing conservation and management strategies for this species. The descriptions of ground sites we provide can be used to target conservation or management actions.

Characterization of elevation and land cover dependent trends of NDVI variations in the Hexi region, northwest China

In the arid Hexi Corridor of northwest China, vegetation cover plays a pivotal role in sustaining the unique terrestrial ecosystem. In this paper, vegetation changes during growth season from April to October were investigated through examining the trends in the Normalized Difference Vegetation Index (NDVI) across the Hexi region. Based on the GMMIS NDVI 3g.v1 dataset, NDVI trend and its dependency on elevation and land cover were analyzed for the period 1982–2015 according to multiple statistical tests. Results showed that NDVI exhibited a significantly increasing trend in ∼70% of the vegetated area, in contrast with a negative trend only in 2.85%. The resulting distinct groups with respect to decreasing, increasing and no trends presented significant differences in elevation and land cover composition, and the correlation between elevation, land cover and NDVI trend magnitude was subjected to precipitation and temperature change. The elevation and grassland cover were found to mainly account for variations in NDVI trend, and increase in elevation and various types of land cover excluding impervious and bare land would facilitate the trend magnitude. The dependency of NDVI trend on elevation and land cover was very vulnerable to increasing air temperature, which triggered an improvement in the vegetable activity to adapt to climate change, especially grass and forest. The contribution of crop and shrub to NDVI change was sensitive to precipitation trend change, but the crop was primarily influenced by human activities. The identified patterns of vegetation change would help to gain insights into the adapting mechanism of the fragile ecosystems in arid areas to changing environmental conditions.

Approaches for land cover monitoring over Europe based on backscatter and coherence properties of Envisat and ERS SAR data

The purpose of this study is the exploitation of Synthetic Aperture Radar (SAR) satellite data for land cover monitoring. The properties of the recorded radar data heavily depend on the type of land cover, season and weather conditions. Therefore, it is possible to utilize the variability of these factors, in order to develop various techniques and methodologies that can be used for classifying land surfaces. In this context, this paper proposes approaches, which include the application of mathematical expressions and application of thresholds on multi-temporal data, for recognizing and classifying various types of land cover, on the basis of ERS and Envisat C-band SAR backscatter and coherence properties. These can be useful for any kind of contemporary SAR data, such as those of the current two Sentinel-1 satellites. Although this study focuses on four main land cover types (urban, mountainous, agricultural-low vegetation and forested areas) over specific areas in Europe, the same principles can be extended worldwide, leading to useful insights for designing future SAR satellite missions or for establishing guidelines for in-depth studies of specific land cover types.

Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

Land cover influences on juvenile Rainbow Trout diet composition and condition in Lake Michigan tributaries

AbstractWatershed development may alter tributaries in ways that influence the growth and development of juvenile fish. For Rainbow Trout Oncorhynchus mykiss, the extent of land cover influence on the diet composition of stream‐residing juveniles is still an open but important question, particularly given their broad global distribution. Our study evaluated the effect of land cover on diet composition and condition metrics of juvenile Rainbow Trout in Lake Michigan tributaries. Juveniles were collected in the fall of 2014 and 2015 from 18 sampling locations across the Lake Michigan basin and included a wide variety of land cover types. Multivariate statistical procedures were used to quantitatively score sites along axes that maximized variation in land cover and diet composition. Subsequently, Spearman's rank correlations were used to test for significance of correlations between (a) land cover and diet composition, (b) land cover and fish condition, and (c) diet composition and fish condition. Our results indicated that land cover had an influence on the diet compositions of juvenile Rainbow Trout, but neither land cover nor diet composition had a significant influence on Rainbow Trout condition. These findings contrast with previously published work focusing on the growth responses of other fish species and provide insight into the plastic nature of Rainbow Trout. Further, we discuss potential ways in which fish may mitigate the effect of land cover changes as they are transmitted through stream food webs; however, the magnitude of the mitigation likely varies among fish species.

Cropland patchiness strongest agricultural predictor of bird diversity for multiple guilds in landscapes of Ontario, Canada

The potential for agricultural landscapes to support biodiversity may vary greatly based on agricultural land use. Current knowledge suggests that agricultural composition and intensity are dominant drivers of biodiversity in agricultural landscapes, with variable effects of agricultural configuration and landscape diversity. The aim of this study was to determine the relative effects of agricultural composition, intensity, configuration, and landscape diversity on the species diversity of six distinct bird guilds on the landscape scale in a large and complex landscape in Ontario, Canada. We found that agricultural configuration, specifically patchiness of croplands, and to a lesser degree forage lands, was the strongest predictor of bird diversity for three of the six bird guilds considered (forest, shrubland, and town). The effects of increased cropland patchiness were variable, with forest and shrubland bird diversity increasing from small to moderate patchiness, and town bird diversity declining from moderate to high patchiness. Grassland birds, a group of considerable conservation concern, increased near linearly with increased agricultural land cover in the landscape, highlighting the need to consider agricultural lands in conservation planning for this species group. Woodland bird diversity declined significantly with all increasing measures of agricultural intensity, including the proportion of high-intensity agriculture and larger patches of agricultural land. Wetland birds were unique from the other guilds, showing primarily a strong association between diversity of land cover types and guild-level bird diversity. Surprisingly, increased cover of agricultural lands, which we predicted to be a dominant driver of guild-level bird diversity declines due to habitat loss, had weak, non-significant effects relative to the other land use variable being tested, except for the positive association with grassland birds. Our findings suggest that a mix of management strategies should be employed to consider the varying effects of agricultural lands on different bird guilds, such as the inclusion of agricultural land in conservation strategies for grassland species and further managing the configuration of agricultural lands to enhance biodiversity of agricultural landscapes.

Implementation of the LandTrendr Algorithm on Google Earth Engine

The LandTrendr (LT) algorithm has been used widely for analysis of change in Landsat spectral time series data, but requires significant pre-processing, data management, and computational resources, and is only accessible to the community in a proprietary programming language (IDL). Here, we introduce LT for the Google Earth Engine (GEE) platform. The GEE platform simplifies pre-processing steps, allowing focus on the translation of the core temporal segmentation algorithm. Temporal segmentation involved a series of repeated random access calls to each pixel’s time series, resulting in a set of breakpoints (“vertices”) that bound straight-line segments. The translation of the algorithm into GEE included both transliteration and code analysis, resulting in improvement and logic error fixes. At six study areas representing diverse land cover types across the U.S., we conducted a direct comparison of the new LT-GEE code against the heritage code (LT-IDL). The algorithms agreed in most cases, and where disagreements occurred, they were largely attributable to logic error fixes in the code translation process. The practical impact of these changes is minimal, as shown by an example of forest disturbance mapping. We conclude that the LT-GEE algorithm represents a faithful translation of the LT code into a platform easily accessible by the broader user community.

Exploring dust emission responses to land cover change using an ecological land classification

Abstract Despite efforts to quantify the impacts of land cover change on wind erosion, assessment uncertainty remains large. We address this uncertainty by evaluating the application of ecological site concepts and state-and-transition models (STMs) for detecting and quantitatively describing the impacts of land cover change on wind erosion. We apply a dust emission model over a rangeland study area in the northern Chihuahuan Desert, New Mexico, USA, and evaluate spatiotemporal patterns of modelled horizontal sediment mass flux and dust emission in the context of ecological sites and their vegetation states; representing a diversity of land cover types. Our results demonstrate how the impacts of land cover change on dust emission can be quantified, compared across land cover classes, and interpreted in the context of an ecological model that encapsulates land management intensity and change. Results also reveal the importance of established weaknesses in the dust model soil characterisation and drag partition scheme, which appeared generally insensitive to the impacts of land cover change. New models that address these weaknesses, coupled with ecological site concepts and field measurements across land cover types, could significantly reduce assessment uncertainties and provide opportunities for identifying land management options.

Hybridization of SLIC and Extra Tree for Object Based Image Analysis in Extracting Shoreline from Medium Resolution Satellite Images

Observation satellites orbiting the Earth provide important surveillance information that helps in identifying various types of land cover. As such, the resolution of satellite images is critical to ensure high accuracy in classifying land cover types. Clearly, high-resolution images are desirable, but such images are prohibitively expensive. Hence, the use of medium-resolution satellite images seems more economical and practical. Several techniques have been developed to monitor the conditions of land covers across the world, such as aerial photography, ground survey, and remote sensing. Among the three techniques, remote sensing is the best, given its ability to monitor vast geographical areas more accurately and much faster compared to those of the other two techniques. In recent decades, many countries have been relying on remote sensing to monitor the conditions of coastal areas by extracting shorelines from satellite images. To date, several pixel-based methods have been proposed for the extraction of shorelines, but most of these methods are fraught with problems. Thus, the object-based approach is proposed using a combination of segmentation algorithms, namely Felzenswalb, Quickshift, and SLIC, together with 15 machine learning classifiers, to classify segmented images of Langkawi Island. The performance of the segmentation algorithms and machine learning classifiers were assessed in terms of segmentation time and overall accuracy in four experimental settings comprising of three different parameters. The research findings showed that the proposed hybridization of SLIC segmentation and Extra Tree classifier was the most efficient and accurate technique compared to other combinations of techniques in extracting the shoreline of the study area. Specifically, SLIC was faster than Felzenswalb and Quickshift by as much as 37 times and 500 times, respectively. Together with Extra Tree classifier, SLIC managed to achieve 100% overall accuracy in the object-based classification in three out of four settings compared to the rest of the techniques tested in the study.

The Role of Vegetation in Controlling Air Temperature Resulting from Urban Heat Island

Urban Heat Island (UHI) is a phenomenon exhibited by many worldwide cities. Cities, which exhibit UHI, possess higher air temperature as compared with air temperature in the surrounding areas. However, existing UHI profiles are those occurring in subtropical areas which are, of course, very much different from those in tropical cities. Therefore, the objectives of this study are to describe the UHI’s profile and the role of tree vegetation in controlling and reducing air temperature in a tropical region’s urban areas and, particularly, in DKI Jakarta. In this study, we carried out a spatial analysis of land cover and the distribution of air temperature. In this regard, we based our analysis of the potency of tree vegetation in reducing air temperature in UHI’s profile on the distribution of air temperature in various types of land cover which extended from north to south and from east to west. The ranges of air temperature in land cover in the form of built-up areas were 29.2-39.5 ⁰C, non-tree vegetation 28.6-35.6 ⁰C, and tree vegetation 27.0-35.7 ⁰C. Accordingly, tree vegetation has the highest potential to reduce air temperature and to overcome the phenomenon of UHI.

Comparative analysis of landscape effects on spatial genetic structure of the big brown bat and one of its cimicid ectoparasites.

Identification of landscape features that correlate with genetic structure permits understanding of factors that may influence gene flow in a species. Comparing effects of the landscape on a parasite and host provides potential insights into parasite‐host ecology. We compared fine‐scale spatial genetic structure between big brown bats (Eptesicus fuscus) and their cimicid ectoparasite (Cimex adjunctus; class Insecta) in the lower Great Lakes region of the United States, in an area of about 160,000 km2. We genotyped 142 big brown bat and 55 C. adjunctus samples at eight and seven microsatellite loci, respectively, and inferred effects of various types of land cover on the genetic structure of each species. We found significant associations between several land cover types and genetic distance in both species, although different land cover types were influential in each. Our results suggest that even in a parasite that is almost entirely reliant on its hosts for dispersal, land cover can affect gene flow differently than in the hosts, depending on key ecological aspects of both species.

Evaluating population expansion of black bears using spatial capture‐recapture

ABSTRACTThe population of American black bears (Ursus americanus) in southern New York, USA has been growing and expanding in range since the 1990s. This has motivated a need to anticipate future patterns of range expansion. We conducted a non‐invasive, genetic, spatial capture‐recapture (SCR) study to estimate black bear density and identify spatial patterns of population density that are potentially associated with range expansion. We collected hair samples in a 2,519‐km2 study area in southern New York with barbed‐wire hair snares and identified individuals and measured genetic diversity using 7 microsatellite loci and 1 sex‐linked marker. We estimated a mean density of black bears in the region of 13.7 bears/100 km2, and detected a slight latitudinal gradient in density consistent with the documented range expansion. However, elevation and the amounts of forest, crop, and developed landcover types did not influence density, suggesting that bears are using a diversity of resources in this heterogeneous landscape outside their previously described distribution. These results provide the first robust baseline estimates for population density and distribution associated with different landcover types in the expanded bear range. Further, genetic diversity was comparable to that of non‐expanding black bear populations in the eastern United States, and in combination with the latitudinal density gradient, suggest that the study area is not at the colonizing front of the range expansion. In addition, the diversity of landcover types used by bears in the study area implies a possible lack of constraints for further northern expansion of the black bear range. Our non‐invasive, genetic, spatial capture‐recapture approach has utility for studying populations of other species that may be expanding in range because SCR allows for the testing of explicit, spatial ecological hypotheses. © 2017 The Wildlife Society.

Land cover diversity increases predator aggregation and consumption of prey.

A lower diversity of land cover types is purported to decrease arthropod diversity in agroecosystems and is dependent on patterns of land use and fragmentation. Ants, important providers of ecosystem services such as biological control, are susceptible to landscape-level changes. We determined the relationships between land cover diversity and fragmentation on the within-field spatial associations of ants to pests and resulting predation events by combining mapping and molecular tools. Increased land cover diversity and decreased fragmentation increased ant abundance, spatial association to pests and predation. Land cover diversity and fragmentation were more explanatory than land cover types. Even so, specific land cover types, such as deciduous forest, influenced ant and pest diversity more so than abundance. These results indicate that geospatial techniques and molecular gut content analysis can be combined to determine the role of land use in influencing predator-prey interactions and resulting predation events in agroecosystems.

Retrieving soil moisture for non-forested areas using PALS radiometer measurements in SMAPVEX12 field campaign

Abstract In this paper, soil moisture retrievals of surface soil moisture was investigated using L-band brightness temperature under diverse conditions and land cover types. The study focused on the PALS (Passive Active L-band System) radiometer data collected during the SMAPVEX12 (Soil Moisture Active Passive Validation Experiment 2012) field experiment which took place in southern Manitoba, Canada in 2012. The experiment domain covers croplands with high clay content as well as croplands and grasslands with sandy soils. A retrieval algorithm was parameterized for these specific land types. The formulation of the retrieval algorithm is based on a traditional surface scattering assumption. Based on this data set we found that for the clayey croplands the surface scattering assumption is inadequate, and that the algorithm needed significant tuning for the sandy soils. Empirically-based parameters for retrieving soil moisture under these conditions were developed. We also applied the parameterized algorithm to the retrieval of soil moisture for the entire experiment domain. We found that the use of sub-grid modeling improves the retrieval performance to a satisfactory level despite the challenging land types encountered.

GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring.

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than −15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than −30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.