Spaceborne Sensors Research Articles

A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset

Vegetation monitoring is important for many applications, e.g., agriculture, food security, or forestry. Optical data from space-borne sensors and spectral indices derived from their data like the normalised difference vegetation index (NDVI) are frequently used in this context because of their simple derivation and interpretation. However, optical sensors have one major drawback: cloud coverage hinders data acquisition, which is especially troublesome for moderate and tropical regions. One solution to this problem is the use of cloud-penetrating synthetic aperture radar (SAR) sensors. Yet, with very different image characteristics of optical and SAR data, an optical sensor cannot be easily replaced by SAR sensors. This paper presents a globally applicable model for the estimation of NDVI values from Sentinel-1 C-band SAR backscatter data. First, the newly created dataset SEN12TP consisting of Sentinel-1 and -2 images is introduced. Its main features are the sophisticated global sampling strategy and that the images of the two sensors are time-paired. Using this dataset, a deep learning model is trained to regress SAR backscatter data to NDVI values. The benefit of auxiliary input information, e.g., digital elevation models, or land-cover maps is evaluated experimentally. After selection of the best model configuration, another experimental evaluation on a carefully selected hold-out test set confirms that high performance, low error, and good level of spatial detail are achieved. Finally, the potential of our approach to create dense NDVI time series of frequently clouded areas is shown. One limit of our approach is the neglect of the temporal characteristics of the SAR and NDVI data, since only data from a single date are used for prediction.

Potential of Optical Spaceborne Sensors for the Differentiation of Plastics in the Environment

Plastics are part of our everyday life, as they are versatile materials and can be produced inexpensively. Approximately 10 Gt of plastics have been produced to date, of which the majority have been accumulated in landfills or have been spread into the terrestrial and aquatic environment in an uncontrolled way. Once in the environment, plastic litter—in its large form or degraded into microplastics—causes several harms to a variety of species. Thus, the detection of plastic waste is a pressing research question in remote sensing. The majority of studies have used Sentinel-2 or WorldView-3 data and empirically explore the usefulness of the given spectral channels for the detection of plastic litter in the environment. On the other hand, laboratory infrared spectroscopy is an established technique for the differentiation of plastic types based on their type-specific absorption bands; the potential of hyperspectral remote sensing for mapping plastics in the environment has not yet been fully explored. In this study, reflectance spectra of the five most commonly used plastic types were used for spectral sensor simulations of ten selected multispectral and hyperspectral sensors. Their signals were classified in order to differentiate between the plastic types as would be measured in nature and to investigate sensor-specific spectral configurations neglecting spatial resolution limitations. Here, we show that most multispectral sensors are not able to differentiate between plastic types, while hyperspectral sensors are. To resolve absorption bands of plastics with multispectral sensors, the number, position, and width of the SWIR channels are decisive for a good classification of plastics. As ASTER and WorldView-3 had/have narrow SWIR channels that match with diagnostic absorption bands of plastics, they yielded outstanding results. Central wavelengths at 1141, 1217, 1697, and 1716 nm, in combination with narrow bandwidths of 10–20 nm, have the highest capability for plastic differentiation.

Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager.

Snow profile reconstruction from tomographic UAV SAR

Snow profiles can provide reliable and detailed snow parameters for global change research, but it is still challenging to reconstruct large-scale spatiotemporally continuous snow profiles. Synthetic aperture radar (SAR) tomography (TomoSAR) has been proven to be a promising method for reconstructing the 3D structure of targets and has been used to reconstruct snow profiles from ground-based SAR. However, the experimental condition of ground-based SAR is ideal and the algorithms developed for ground-based SAR have some problems when applied to airborne and spaceborne sensors, such as large computational load and big angular diversity. In this paper, we first collected the Ku-band tomographic SAR dataset in the snow season of Altay, China using the Unmanned Aerial Vehicle (UAV) platform. Then, we reconstructed the snow profile from the dataset using three spectral estimation-based focusing algorithms. According to the non-stationary characteristic of the UAV platform and the dense medium property of snow, phase calibration and medium correction were performed, respectively. Finally, we assessed the reconstruction accuracy of the snow profile and snow depth using the ground synchronous measurement data. The reconstruction accuracy of the three focusing algorithms from high to low is the MUSIC algorithm, the Capon algorithm, and the Beamforming algorithm. Among them, the average error of the MUSIC algorithm to reconstruct the snow profile is 1.13 cm, and the RMSE and MAE of the snow depth retrieval are 3.71 cm and 3.26 cm, respectively. Although TomoSAR is still widely unexplored for snow using air-based sensors, the results show that it has the ability to reconstruct snow profiles accurately, and the proposed method can provide reliable support for reconstructing the large-scale spatiotemporal continuous snow profiles from subsequent spaceborne SAR.

Domain-incremental learning for fire detection in space-air-ground integrated observation network

Deep learning-based fire detection models are usually trained offline on static datasets. For continuously increasing heterogeneous sensor data, incremental learning is a resolution to enable incremental updates of models. However, it still encounters the challenge of the stability-plasticity dilemma on cross-domain data. In this paper, we propose a Dynamic Equilibrium Network (DENet) to achieve cross-domain fire detection on heterogeneous images captured by spaceborne, airborne, and terrestrial sensors. It can learn more flame and smoke features from cross-domain data in a balanced manner by imposing dynamic equilibrium constraints on the self-knowledge distillation model with deeply-supervised loss. It adopts learnable loss weights to adaptively adjust the importance of the old and new datasets and focuses on rebalancing the weight bias based on dynamic equilibrium constraints guided by the number of samples. Model performance is evaluated on the Flame And Smoke Detection Dataset (FASDD) consisting of computer vision (CV) and satellite remote sensing (RS) images, as well as the FLAME dataset consisting of Unmanned Aerial Vehicle (UAV) images. Extensive experiments demonstrated that DENet performs better generalization and equalization on CV, RS, and UAV data than other classical incremental learning methods, and significantly narrows the performance gap toward joint training. It outperforms the well-performing PODNet model by +7.71% mAP@0.5 on the old dataset while maintaining the same good performance on the new dataset. In the ablation experiments, our model outperforms the No Bias model by +27.13% mAP@0.5 on the old dataset, and by +2.77% mAP@0.5 on the new dataset. Given its excellent equalization capability on heterogeneous data, DENet can provide effective support for cross-domain fire detection. This study will provide an important reference for collaborative fire warning, emergency response, and government decision-making in a space-air-ground integrated observation network.

Simultaneous estimation of fractional cover of photosynthetic and non-photosynthetic vegetation using visible-near infrared satellite imagery

In vegetation-abundant ecosystems, accurate fractional cover estimation of multiple targets, including non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV) and bare soil (BS) is an important indicator of carbon storage and climate change, and a contributing factor to soil and water conservation, wildfire, and drought. Satellite remote sensing began with sensors limited to the visible and near infrared (VNIR, 380 nm to 1000 nm), and advancing to sensors spanning the whole solar domain (350 nm to 2500 nm) over the last five decades. However, NPV is still difficult to distinguish from BS in satellite images covering large spatial areas, since their spectral reflectances are always mixed and have subtly different spectral features. Many spectral indices have been proposed to identify NPV, however, most include a short-wave infrared band which is lacking on many current and historical spaceborne sensors. To fully utilize current and historical airborne/satellite images with only VNIR bands, a novel spectral index – the VNIR spectral shape index (SSI) – is proposed in this paper. Using spectra of PV, NPV, and BS acquired from world-recognized spectral libraries, the SSI was devised using only blue, green, red, and NIR bands. When applied to Sentinel-2 MSI images, the SSI was effective in estimating/mapping the fractional cover of PV, NPV, and BS using the triangular space method. The resulting fractional cover was evaluated in three 3 km × 2 km study areas including one grassland, one woodland, and one cropland. The spectral reflectance collected by spectroradiometer in field work and digital image collect by digital camera mounted on unmanned aerial vehicle (UAV) support that the triangular space method is able to estimate the fractional cover of photosynthetic and non-photosynthetic vegetation simultaneously, and that endmember selection relying solely on satellite images is reasonable. Comparing the abundance of each target in UAV images to corresponding MSI pixels, the SSI index and triangular space method proved effective for fractional cover estimation in varied vegetated ecosystems. For fractional cover evaluation in grassland, woodland, and cropland, the R2 reaching to 0.59, 0.62, and 0.70, respectively. These results demonstrate that the SSI is feasible for multiple sensors, enabling potential large temporal and/or spatial scale applications.

An Assessment of YOLO Architectures for Oil Tank Detection from SPOT Imagery

Since it can be used to manage and estimate oil reserves, the inventory of oil tanks is essential for both the economy and the military applications. Considering oil tanks contain valuable materials required for transportation and industrial production, they are a significant type of target. Oil tank detection techniques have several uses, including monitoring disasters, preventing oil leaks, designing cities, and assessing damage. Huge amount of satellite imagery has recently been available and it is used in both the military and civil applications. The new spaceborne sensors' higher resolution enables the detection of targeted objects. Therefore, remote sensing instruments provide ideal tools for oil tank detection task. Conventional approaches for oil tank detection from high resolution remote sensing imagery generally relies on geometric shape, structure, contract differences and color information of the boundary or hand-crafted features. However, these methods come along with vulnerabilities and hence it can be challenging to obtain accurate detection in the presence of a number of disturbance elements, particularly a wide range of colours, size variations, and the shadows that view angle and illumination create. Therefore, deep learning-based methods can provide a big advantage for solution of this task. In this regard, this study employs four YOLO models namely YOLOv5, YOLOX, YOLOv6 and YOLOv7 for oil tank detection from high-resolution optical imagery. Our results show that YOLOv7 and YOLOv5 architectures provide more accurate detections with mean average precision values of 68.11% and 69.69%, respectively. The experiments and visual inspections reveal efficiency, generalization and transferability of these models.

Machine learning-enabled real-time detection of cloud and aerosol layers using airborne lidar

Lidar profiling of the atmosphere provides information on existence of cloud and aerosol layers and the height and structure of those layers. Knowledge of feature boundaries is a key input to assimilation models. Moreover, identifying feature boundaries with minimal latency is essential to impact operational assimilation and real-time decision making. Using advanced convolution neural network algorithms, we demonstrate real-time determination of atmospheric feature boundaries using an airborne backscatter lidar. Results are shown to agree well with traditional processing methods and are produced with higher horizontal resolution than the traditional method. Demonstrated using airborne lidar, the algorithms and process are extendable to real-time generation of data products from a future spaceborne sensor.

An Adaptive Method for the Estimation of Snow-Covered Fraction with Error Propagation for Applications from Local to Global Scales

Snow can cover over 50% of the landmass in the Northern Hemisphere and has been labelled as an Essential Climate Variable by the World Meteorological Organisation. Currently, continental and global snow cover extent is primarily monitored by optical satellite sensors. There are, however, no large-scale demonstrations for methods that (1) use all the spectral information that is measured by the satellite sensor, (2) estimate fractional snow and (3) provide a pixel-wise quantitative uncertainty estimate. This paper proposes a locally adaptive method for estimating the snow-covered fraction (SCF) per pixel from all the spectral reflective bands available at spaceborne sensors. In addition, a comprehensive procedure for root-mean-square error (RMSE) estimation through error propagation is given. The method adapts the SCF estimates for shaded areas from variable solar illumination conditions and accounts for different snow-free and snow-covered surfaces. To test and evaluate the algorithm, SCF maps were generated from Sentinel-2 MSI and Landsat 8 OLI data covering various mountain regions around the world. Subsequently, the SCF maps were validated with coincidentally acquired very-high-resolution satellite data from WorldView-2/3. This validation revealed a bias of 0.2% and an RMSE of 14.3%. The proposed method was additionally tested with Sentinel-3 SLSTR/OLCI, Suomi NPP VIIRS and Terra MODIS data. The SCF estimations from these satellite data are consistent (bias less than 2.2% SCF) despite their different spatial resolutions.

Experimental and Correction Methods for Responsivity Temperature Dependence in the SWIR Bands of a Spaceborne Scanning Polarimeter

Photodetector spectral responsivity is usually affected by photosensitive surface temperatures. The Photosensitive Surface Temperatures (PSTs) of the long wavelength-type InGaAs detector used in the Polarized Scanning Atmospheric Corrector (PSAC), which adopts an active Constant-Current Cooling (CCC) scheme, can be changed with ambient temperature. To correct this responsivity temperature characteristic, a Responsivity Temperature Dependence Correction (RTDC) model was established with parameters obtained using an instrument-level experiment. Moreover, a detector-level experiment under ambient conditions was also explored to acquire model parameters. The instrument-level and detector-level experimental results show that the responsivity of a PSAC 2250 nm channel operating on orbit (PST ≈ −65 °C) with a 35 °C PST difference is reduced by approximately 1.2% and 1.5% compared with a laboratory-based radiometric test (PST ≈ −30 °C). The 1.08% responsivity change (−61 °C < PST < −38 °C) in the temperature drift simulation experiment is reduced to approximately 0.38% and 0.34%, respectively. The consistency between the two experimental results preliminarily verifies the substitutability of this detector-level experiment for this type of detector. The RTDC experiments are applicable to other spaceborne remote sensors. The detector-level experiment explored herein provides a reference for realizing RTDC under ambient temperature conditions at low costs.

The effects of surface roughness on the calculated, spectral, conical–conical reflectance factor as an alternative to the bidirectional reflectance distribution function of bare sea ice

Abstract. The conical–conical reflectance factor (CCRF) has been calculated as an alternative to the bidirectional reflectance distribution function (BRDF) for three types of bare sea ice with varying surface roughness (σ= 0.1–10) and ice thicknesses (50–2000 cm) over an incident solar irradiance wavelength range of 300–1400 nm. The comprehensive study of the CCRF of sea ice presented here is paramount for interpreting sea ice measurements from satellite imagery and inter-calibrating space-borne sensors that derive albedo from multiple multi-angular measurements. The calculations performed by a radiative-transfer code (PlanarRad) show that the CCRF of sea ice is sensitive to realistic values of surface roughness. The results presented here show that surface roughness cannot be considered independently of sea ice thickness, solar zenith angle and wavelength. A typical CCRF of sea ice has a quasi-isotropic reflectance over the hemisphere, associated with a strong forward-scattering peak of photons. Surface roughness is crucial for the location, size and intensity of the forward-scattering peak. As the surface roughness increases, a spreading of the CCRF peak is observed. The hemisphere was split in to 216 quadrangular regions or quads. The peak remains specular for the smaller surface roughnesses (σ=0.001 to σ=0.01), whereas for larger surface roughness features (above σ=0.05), the peak spreads out over multiple quads with a lower intensity than for smaller roughness features, and the highest value is displaced further out on the solar principal plane. Different types of sea ice have a similar pattern with wavelength: the CCRF increases by 30 % from first-year sea ice to multi-year sea ice at 400 nm and up to 631 % at 1100 nm, 32 % from melting sea ice to multi-year sea ice at 400 nm and a maximum of 98 % at 900 nm, and 11 % from melting sea ice to first-year sea ice at 400 nm and up to 86 % at 800 nm. The CCRF calculations presented in this study form the first set of complete CCRF values as an approximation of the BRDF for bare sea ice with a wide range of configurations.

Calibration of the ESA CCI-Combined Soil Moisture Products on the Qinghai-Tibet Plateau

Soil moisture (SM) retrieved from satellite and spaceborn sensors provides useful parameters for earth system models (ESMs). The Climate Change Initiative (CCI) SM products released by the European Space Agency have been widely used in many humid/semi-humid climatic regions due to their relatively long-term record. However, the performance of these products in cold and arid regions, such as the Qinghai-Tibetan Plateau (QTP), is largely unknown, necessitating urgent evaluation and calibration in these areas. In this work, we evaluated the reliability and improved the accuracy of the active-passive combined CCI products (CCI-C) using in-situ measured SM contents (SMC) under different underlying surface conditions. First, some conventional models were used to investigate the relationship between the CCI-C and the in-situ observed SMC, yielding similar fitting performances. Next, the random forest method and multiple linear regression were used to contrast the conventional models to calibrate and validate the CCI-C SM product based on the in-situ observed SMC, and the random forest method was found to have the highest accuracy. However, calibration of the CCI-C SM data with the best-performed random forest method based on different spatial zonation methods, e.g., by climate, topography, land cover, and vegetation, resulted in distinct spatial patterns of SM. Compared to a widely-used satellite SM product, namely the Soil Moisture Active Passive (SMAP) SM dataset, the calibrated CCI-C SM data based on climatic and vegetation zonation were larger but had similar spatial patterns. This study also points to the value of the calibrated CCI-C SM product to support land surface studies on the QTP.

DPPNet: An Efficient and Robust Deep Learning Network for Land Cover Segmentation From High-Resolution Satellite Images

Visual understanding of land cover is an important task in information extraction from high-resolution satellite images, an operation which is often involved in remote sensing applications. Multi-class semantic segmentation of high-resolution satellite images turned out to be an important research topic because of its wide range of real-life applications. Although scientific literature reports several deep learning methods that can provide good results in segmenting remotely sensed images, these are generally computationally expensive. There still exists an open challenge towards developing a robust deep learning model capable of improving performances while requiring less computational complexity. In this article, we propose a new model termed DPPNet (Depth-wise Pyramid Pooling Network), which uses the newly designed Depth-wise Pyramid Pooling (DPP) block and a dense block with multi-dilated depth-wise residual connections. This proposed DPPNet model is evaluated and compared with the benchmark semantic segmentation models on the Land-cover and WHDLD high-resolution Space-borne Sensor (HRS) datasets. The proposed model provides DC, IoU, OA, Ka scores of (88.81%, 78.29%), (76.35%, 60.92%), (87.15%, 81.02%), (77.86%, 72.73%) on the Land-cover and WHDLD HRS datasets respectively. Results show that the proposed DPPNet model provides better performances, in both quantitative and qualitative terms, on these standard benchmark datasets than current state-of-art methods.

Radiometric calibration of AVIRIS-NG sensor using Indian desert sites

This manuscript describes reflectance-based vicarious calibration exercise of the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) sensor during its second phase of observation campaigns over the Indian subcontinent. Vicarious calibration methods are one of the significant approaches that have been practiced successfully for the absolute radiometric calibration of various spaceborne and airborne sensors to ascertain its desired performance in terms of data quality and its accuracy. Calibration campaigns were performed for AVIRIS-NG sensor during its phase-2 campaign overpass at the Desalpar (25th March 2018) and Amarapur (27th March 2018) calibration sites of Gujarat, India. Results from ground-based measurements of atmospheric conditions and surface reflectance including descriptions of the test sites are summarized in this work. Based on the in-situ data and images from the AVIRIS-NG sensor, the at-sensor apparent radiances are simulated using the 6SV radiative transfer model. The comparison shows good agreement between AVIRIS-NG measured radiance and 6SV simulated apparent radiance at the two calibration sites using desert aerosol model. The gains (spectrally) derived agree to within ∼ 2 % each other. Furthermore, an analysis is performed to determine or identify systematic and random errors, and the overall uncertainty is evaluated for reflectance-based method (total uncertainty of less than 4 % spectrally).

A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China

Ambient fine particulate matter (PM2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM2.5 level has decreased mainly driven by reductions in particulate sulfate as a result of large-scale desulfurization efforts in coal-fired power plants and industrial facilities. Emerging evidence also points to the differential toxicity of particulate sulfate affecting human health. However, estimating the long-term spatiotemporal trend of sulfate is difficult because a ground monitoring network of PM2.5 constituents has not been established in China. Spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol size and type. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR-retrieved aerosol microphysical properties, and atmospheric reanalysis data at a spatial resolution of 0.1°. Our sulfate model performed well with an out-of-bag cross-validationR2 of 0.68 at the daily level and 0.93 at the monthly level. We found that the national mean population-weighted sulfate concentration was relatively stable before the Air Pollution Prevention and Control Action Plan was enforced in 2013, ranging from 10.4 to 11.5 µg m−3. But the sulfate level dramatically decreased to 7.7 µg m−3 in 2018, with a change rate of −28.7 % from 2013 to 2018. Correspondingly, the annual mean total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7 % and 42.3 %, respectively. The long-term, full-coverage sulfate level estimates will support future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.

Physics-Based Fusion of Sentinel-2 and Sentinel-3 for Higher Resolution Vegetation Monitoring

Monitoring vegetation growth, phenology and health in agriculture requires very high spatial and spectral resolution sensors. Sentinel-2 is among the spaceborne sensors that tried to meet these requirements. However, due to physical constraints, few visible bands are present with relatively large spectral responses which limits the leaf pigment content estimation. In this work, we propose to fuse Sentinel-2 bands with a Sentinel-3 image which has a lower spatial resolution but contains several spectral narrower bands in both the visible and near-infrared domains. The fusion procedure consists in sharpening the Sentinel-3 bands to match the Sentinel-2 spatial resolution leading to a higher spatial resolution Sentinel-3 image. The proposed fusion technique follows a physics-based approach based on the use of a radiative transfer model for establishing a correspondence between Sentinel-2 and Sentinel-3 images. In greater details, the main spectrally pure constituent of each high resolution Sentinel-2 pixel is identified for each pixel and it is then related to the corresponding low resolution Sentinel-3 pixel. Pure materials are the barycenters of clusters obtained by an unsupervised classification of the Sentinel-2 image. Sentinel-3 signatures are obtained using coarse resolution pixel matching with the Sentinel-2 image. The latter result is then corrected using radiative transfer modeling allowing the production of more realistic signatures. Validation was done using real Sentinel-2/Sentinel-3 images taken with a delay of only one day or less and by comparing the sharpened Sentinel-3 bands Oa04, Oa06, Oa08, and Oa17 with the corresponding Sentinel-2 bands B2, B3, B4, and B8A, as they are respectively spectrally close. For all the bands, the RMS is lower than 0.007, and compared to the state-of-the-art techniques it shows competitive results and robustness against scene heterogeneity. Besides, our findings prove that the retrieved Sentinel-3 signatures at full resolution are physically consistent and in good agreement with the Sentinel-2 data.

GEDI waveform metrics in vegetation mapping—a case study from a heterogeneous tropical forest landscape

The distribution of different vegetation types is important information for landscape management, especially in the context of tackling global environmental change. Vegetation types can be mapped using satellite and airborne passive remote sensing. However, spectrally similar yet structurally different vegetation types, like different tree-dominated land covers, are often challenging to map using spectral information alone. We examined the potential of vertical vegetation structure acquired in the global ecosystem dynamics investigation (GEDI) mission that harnesses a space-borne waveform lidar sensor in vegetation mapping across a heterogeneous tropical landscape in Cambodia. We extracted 121 waveform metrics from Level-1B and Level-2A data products at 1062 locations across five key vegetation types. After reducing the relative height variables’ dimensionality through simple linear regressions, we developed a Random Forest classifier to predict vegetation classes based on 23 GEDI metrics. We then used this model to classify the vegetation types across more than 77 000 GEDI footprints in the study area. GEDI metrics alone were useful in identifying vegetation types with 81% accuracy. Cropland/grassland class had the highest prediction accuracy (user’s accuracy [UA] = 89%; producer’s accuracy [PA] = 91%), while dry deciduous forest had the lowest accuracy (UA = 73%; PA = 69%). By comparing the GEDI-only classification with an optical-radar map, we found that structural and topographic information from GEDI Level-1B and Level-2A can complement the spectral information in assessing natural habitats that neighbor other vegetation types in a heterogeneous landscape. The highest classification accuracy at the footprint scale was obtained from the combination of GEDI, Sentinel-1, and Sentinel-2 (88.3%). We also demonstrated how wall-to-wall vegetation mapping is possible by combining the three data sources. These findings expand the potential use of GEDI waveform lidar data in supporting the development of policy-relevant maps that depict the distribution of forests together with other vegetation types.

Elevation Model Mosaicking From Coregistration, Adjustment, and Median of Stacks (CAMS)

A large and growing volume of repeat Digital Elevation Models (DEMs) obtained from multiple spaceborne sensors enables high-resolution mapping of the Earth’s surface at continental scales. Mosaicking of individual DEMs to form a continuous surface is challenging due to variability data quality and positional accuracy, all of which can result in artifacts. Presented is a method for efficiently mosaicking sets of repeat, overlapping DEMs using their pairwise, translational offsets to remove poor quality DEMs and optimize their alignment prior to merging. The Coregistration, Adjustment and Median of Stacks (CAMS) approach is tested by mosaicking a set of 2-m resolution DEMs created from WorldView stereoscopic imagery and comparing the result to LiDAR data. CAMS produces a mosaic of substantially higher quality and accuracy than that obtained from the median of all overlapping DEMS, as commonly performed for mosaicking satellite derived DEMs. The method requires no sensor-specific information or ground control, making it applicable for large-area mosaic production using multiple datasets.

Remote Sensing Retrieval of Water Clarity in Clear Oceanic to Extremely Turbid Coastal Waters From Multiple Spaceborne Sensors

Remote Sensing Retrieval of Water Clarity in Clear Oceanic to Extremely Turbid Coastal Waters From Multiple Spaceborne Sensors

Simulating spaceborne imaging to retrieve grassland nitrogen concentration

Spaceborne optical imaging enables continuous monitoring of nitrogen concentration (N%) in grasslands. However, the differences in instrumental setup, image pre-processing, wavelength coverage, and sampling rate pose a challenge when attempting to leverage data from multiple spaceborne instruments. We develop a method that makes use of field spectroscopy and the Soil–Plant–Atmosphere Radiative Transfer (SPART) model to simulate any Top Of Atmosphere (TOA) spaceborne sensor. The purpose of this method is to allow better integration of field spectroscopy data with spaceborne optical imagery. We develop a hybrid model using simulated data and a Random Forest Regressor, which is then independently validated using real Sentinel-2 TOA reflectance collected during 2016 and 2020. Our model achieves an independent validation accuracy of 0.44 (RMSE), MPIW of 0.11 and R2 of 0.55, demonstrating the potential of this methodology for monitoring grassland N% using field spectroscopy.