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Harmful algal blooms (HABs) threaten lake ecosystems and public health. Early HAB detection is possible by monitoring chlorophyll-a (Chl-a) concentration. Ground-based Chl-a data have limited spatial and temporal coverage but can be geo-registered with temporally coincident satellite imagery to calibrate a remote sensing-based predictive model for regional mapping over time. When matching ground and satellite data, positional and temporal discrepancies are unavoidable due particularly to dynamic lake surfaces, thereby biasing the model calibration. This limitation has long been recognized but so far has not been addressed explicitly. To mitigate such effects of data mismatching, we proposed an Akaike Information Criterion (AIC)-like weighted regression algorithm that relies on an error-based heuristic to automatically favor “good” data points and downplay “bad” points. We evaluated the algorithm for estimating Chl-a over inland lakes in Ohio using Harmonized Landsat Sentinel-2. The AIC-like weighted regression estimates showed superior performance with an R2 of 0.91 and an error variance (σE2) of 0.29 μg/L, outperforming linear regression (R2 = 0.34, σE2 = 2.34 μg/L) and random forest (R2 = 0.82, σE2 = 0.92 μg/L). We also noticed the poorest performance occurred in the spring due to low reflectance variation in clear water and low Chl-a concentration. Our weighted regression scheme is adaptive and generically applicable. Future studies may adopt our scheme to tackle other remote sensing estimation problems (e.g., terrestrial applications) for alleviating the adverse effects of geolocation errors and temporal discrepancies.

Cyanobacterial harmful algal blooms release toxins and form thick blanket layers on the water surface causing widespread problems, including serious threats to human health, water ecosystem, economics, and recreation. To identify the potential drivers for the bloom, there is a need for extensive observations of the water sources with bloom occurrences. However, the traditional methods for monitoring water sources, such as collection of point ground samples, have proven limited due to spatial and temporal variability of water resources, and the cost associated with collecting samples that accurately represent this variability. These limitations can be addressed through the use of high-frequency satellite data. In this study, we explored the use of Random Forest (RF), which is one of the widely used machine learning architectures, to evaluate the performance of Sentinel-3 OLCI (Ocean and Land Color Imager) images in predicting bloom proxies in the western region of Lake Erie. The sixteen available bands of Sentinel-3 images were used as the predictor variables, while four proxies of the cyanobacterial masses, including Chlorophyll-a, Microcystin, Phycocyanin, and Secchi-depth, were considered as response variables in the RF models, with one RF model per proxy. Each of the proxies comes with a unique set of traits that can help with bloom detection. Among four RF models, the model for Chlorophyll-a performed the best with R2 = 0.55 and RMSE = 20.84 µg/L, while R2 performance for the rest of the other proxies was less than 0.5. This is because Chlorophyll-a is the most dominant and optically active pigment in water, while Phycocyanin, which is a strong indicator of harmful bloom, is present in low concentrations. Additionally, Microcystin, responsible for bloom toxicity, has limited spectral sensitivity, and Secchi-depth could be influenced by various factors besides blooms, such as colored dissolved organic and inorganic matter. On further examining the relationship between the proxies, Microcystin and Secchi-depth were significantly correlated with Chlorophyll-a, which enhances the usefulness of Chlorophyll-a in accurately identifying the presence of algal blooms.

Multi-angle optical reflectance measurements such as those from the NASA moderate resolution imaging spectroradiometer (MODIS) are sensitive to forest 3D structures, potentially serving as a useful proxy to estimate forest structural variables such as aboveground biomass (AGB)—a potential theoretically recognized but rarely explored. In this paper, we examined the effectiveness of the reconstructed MODIS typical-angle reflectances—reflectances observed from the hotspot, darkspot, and nadir directions—for estimating forest AGB from both theoretical and practical perspectives. To gain theoretical insights, we first tested the sensitivities of typical-angle reflectances to forest AGB through simulations using the 4-scale bidirectional reflectance distribution function (BRDF) model. We then built statistical models to fit the relationship between MODIS multi-angle observations and field-measured deciduous-broadleaf/mixed-temperate forest AGB at five sites in the eastern USA, assisted by a semivariogram analysis to determine the effect of pixel heterogeneity on the MODIS–AGB relationship. We also determined the effects of terrain and season on the predictive relationships. Our results indicated that multi-angle reflectances with fewer visible shadows yielded better AGB estimates (hotspot: R2 = 0.63, RMSE = 54.28 Mg/ha; nadir: R2 = 0.55, RMSE = 59.95 Mg/ha; darkspot: R2 = 0.46, RMSE = 65.66 Mg/ha) after filtering out the effects of complex terrain and pixel heterogeneity; the MODIS typical-angle reflectances in the NIR band were the most sensitive to forest AGB. We also found strong sensitivities of estimated accuracies to MODIS image acquisition dates or season. Overall, our results suggest that the current practice of leveraging only single-angle MODIS data can be a suboptimal strategy for AGB estimation. We advocate the use of MODIS multi-angle reflectances for optical remote sensing of forest AGB or potentially other ecological applications requiring forest structure information.

Vegetation indices are widely used to derive land surface phenology (LSP). However, due to inconsistent illumination geometries, reflectance varies with solar zenith angles (SZA), which in turn affects the vegetation indices, and thus the derived LSP. To examine the SZA effect on LSP, the MODIS bidirectional reflectance distribution function (BRDF) product and a BRDF model were employed to derive LSPs under several constant SZAs (i.e., 0°, 15°, 30°, 45°, and 60°) in the Harvard Forest, Massachusetts, USA. The LSPs derived under varying SZAs from the MODIS nadir BRDF-adjusted reflectance (NBAR) and MODIS vegetation index products were used as baselines. The results show that with increasing SZA, NDVI increases but EVI decreases. The magnitude of SZA-induced NDVI/EVI changes suggests that EVI is more sensitive to varying SZAs than NDVI. NDVI and EVI are comparable in deriving the start of season (SOS), but EVI is more accurate when deriving the end of season (EOS). Specifically, NDVI/EVI-derived SOSs are relatively close to those derived from ground measurements, with an absolute mean difference of 8.01 days for NDVI-derived SOSs and 9.07 days for EVI-derived SOSs over ten years. However, a considerable lag exists for EOSs derived from vegetation indices, especially from the NDVI time series, with an absolute mean difference of 14.67 days relative to that derived from ground measurements. The SOSs derived from NDVI time series are generally earlier, while those from EVI time series are delayed. In contrast, the EOSs derived from NDVI time series are delayed; those derived from the simulated EVI time series under a fixed illumination geometry are also delayed, but those derived from the products with varying illumination geometries (i.e., MODIS NBAR product and MODIS vegetation index product) are advanced. LSPs derived from varying illumination geometries could lead to a difference spanning from a few days to a month in this case study, which highlights the importance of normalizing the illumination geometry when deriving LSP from NDVI/EVI time series.

Cover cropping is a conservation practice that helps to alleviate soil health problems and reduce nutrient losses. Understanding the spatial variability in historic and current adoption of cover cropping practices and their impacts on soil, water, and nutrient dynamics at a landscape scale is an important step in determining and prioritizing areas in a watershed to effectively utilize this practice. However, such data are lacking. Our objective was to develop a spatial and temporal inventory of winter cover cropping practices in the Maumee River watershed using images collected by Landsat satellites (Landsat 5, 7 and 8) from 2008 to 2019 in Google Earth Engine (GEE) platform. Each year, satellite images collected during cover crop growing season (i.e., between October and April) were converted into two seasonal composites based on cover crop phenology. Using these composites, various image-based covariates were extracted for 628 ground-truth (field) data. By integrating ground-truth and image-based covariates, a cover crop classification model based on a random forest (RF) algorithm was developed, trained and validated in GEE platform. Our classification scheme differentiated four cover crop categories: Winter Hardy, Winter Kill, Spring Emergent, and No Cover. The overall classification accuracy was 75%, with a kappa coefficient of 0.63. The results showed that more than 50% of the corn-soybean areas in the Maumee River watershed were without winter crops during 2008–2019 period. It was found that 2019/2020 and 2009/2010 were the years with the largest and lowest cover crop areas, with 34% and 10% in the watershed, respectively. The total cover cropping area was then assessed in relation to fall precipitation and cumulative growing degree days (GDD). There was no apparent increasing trend in cover crop areas between 2008 and 2019, but the variability in cover crops areas was found to be related to higher accumulated GDD and fall precipitation. A detailed understanding of the spatial and temporal distribution of cover crops using GEE could help in promoting site-specific management practices to enhance their environmental benefits. This also has significance to policy makers and funding agencies as they could use the information to localize areas in need of interventions for supporting adoption of cover cropping practice.

Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable. The majority of regional or global CI products available so far were generated from multiangle optical reflectance data. However, these reflectance-based estimates have well-known limitations, such as the mere use of a linear relationship between the normalized difference hotspot and darkspot (NDHD) and CI, uncertainties in bidirectional reflectance distribution function (BRDF) models used to calculate the NDHD, and coarse spatial resolutions (e.g., hundreds of meters to several kilometers). To remedy these limitations and develop alternative methods for large-scale CI mapping, here we explored the use of spaceborne lidar—the Geoscience Laser Altimeter System (GLAS)—and proposed a semi-physical algorithm to estimate CI at the footprint level. Our algorithm was formulated to leverage the full vertical canopy profile information of the GLAS full-waveform data; it converted raw waveforms to forest canopy gap distributions and gap fractions of random canopies, which was used to estimate CI based on the radiative transfer theory and a revised Beer–Lambert model. We tested our algorithm over two areas in China—the Saihanba National Forest Park and Heilongjiang Province—and assessed its relative accuracies against field-measured CI and MODIS CI products. We found that reliable estimation of CI was possible only for GLAS waveforms with high signal-to-noise ratios (e.g., >65) and at gentle slopes (e.g., 2 = 0.72, RMSE = 0.07, and bias = 0.02), but they showed less correlation to MODIS CI (e.g., R2 = 0.26, RMSE = 0.12, and bias = 0.04). The difference highlights the impact of the scale effect in conducting comparisons of products with huge differences resolution. Overall, our analyses represent the first attempt to use spaceborne lidar to retrieve high-resolution forest CI and our algorithm holds promise for mapping CI globally.

The vertical foliage profile (VFP) and leaf area index (LAI) are critical descriptors in terrestrial ecosystem modeling. Although light detection and ranging (lidar) observations have been proven to have potential for deriving the VFP and LAI, existing methods depend only on the received waveform information and are sensitive to additional input parameters, such as the ratio of canopy to ground reflectance. In this study, we proposed a new method for retrieving forest VFP and LAI from Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data over two sites similar in their biophysical parameters. Our method utilized the information from not only the interaction between the laser and the forest but also the sensor configuration, which brought the benefit that our method was free from an empirical input parameter. Specifically, we first derived the transmitted energy profile (TEP) through the lidar 1-D radiative transfer model. Then, the obtained TEP was utilized to calculate the vertical gap distribution. Finally, the vertical gap distribution was taken as input to derive the VFP based on the Beer–Lambert law, and the LAI was calculated by integrating the VFP. Extensive validations of our method were carried out based on the discrete anisotropic radiative transfer (DART) simulation data, ground-based measurements, and the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The validation based on the DART simulation data showed that our method could effectively characterize the VFP and LAI under various canopy architecture scenarios, including homogeneous turbid and discrete individual-tree scenes. The ground-based validation also proved the feasibility of our method: the VFP retrieved from the GLAS data showed a similar trend with the foliage distribution density in the GLAS footprints; the GLAS LAI had a high correlation with the field measurements, with a determination coefficient (R2) of 0.79, root mean square error (RMSE) of 0.49, and bias of 0.17. Once the outliers caused by low data quality and large slope were identified and removed, the accuracy was further improved, with R2 = 0.85, RMSE = 0.35, and bias = 0.10. However, the MODIS LAI product did not present a good relationship with the GLAS LAI. Relative to the GLAS LAI, the MODIS LAI showed an overestimation in the low and middle ranges of the LAI and a saturation at high values of approximately LAI = 5.5. Overall, this method has the potential to produce continental- and global-scale VFP and LAI datasets from the spaceborne lidar system.

Figuring out the effect of the built-up environment on artificial light at night is essential for better understanding nighttime luminosity in both socioeconomic and ecological perspectives. However, there are few studies linking artificial surface properties to nighttime light (NTL). This study uses a statistical method to investigate effects of construction region environments on nighttime brightness and its variation with building height and regional economic development level. First, we extracted footprint-level target heights from Geoscience Laser Altimeter System (GLAS) waveform light detection and ranging (LiDAR) data. Then, we proposed a set of built-up environment properties, including building coverage, vegetation fraction, building height, and surface-area index, and then extracted these properties from GLAS-derived height, GlobeLand30 land-cover data, and DMSP/OLS radiance-calibrated NTL data. Next, the effects of non-building areas on NTL data were removed based on a supervised method. Finally, linear regression analyses were conducted to analyze the relationships between nighttime lights and built-up environment properties. Results showed that building coverage and vegetation fraction have weak correlations with nighttime lights (R2 < 0.2), building height has a moderate correlation with nighttime lights (R2 = 0.48), and surface-area index has a significant correlation with nighttime lights (R2 = 0.64). The results suggest that surface-area index is a more reasonable measure for estimating light number and intensity of NTL because it takes into account both building coverage and height, i.e., building surface area. Meanwhile, building height contributed to nighttime lights greater than building coverage. Further analysis showed the correlation between NTL and surface-area index becomes stronger with the increase of building height, while it is the weakest when the regional economic development level is the highest. In conclusion, these results can help us better understand the determinants of nighttime lights.

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.

Transition of freeze/thaw (F/T) affects land-atmospheric interactions and other biospheric dynamics. Global F/T statuses are normally monitored using microwave remote sensing, but at coarse resolutions (e.g., 25 km). Integration of coarse microwave remote sensing data with finer satellite products represents an opportunity to further enhance our ability to map F/T statuses regionally and globally. Here, we implemented and tested an approach to generate daily F/T status maps at a 5-km spatial resolution through the fusion of passive microwave data from AMSR2 and land surface temperature products from MODIS, using China as our study area for the year 2013 and 2014. Moreover, possible influences from elevation, vegetation, seasonality, etc., were also analyzed, as such analysis provides a direction to improve the approach. Overall, our freeze/thaw maps agreed well with ground reference observations, with an accuracy of ~86.6%. The new F/T maps helped to identify regions subject to frequent F/T transitions through the year, such as the Qinghai-Tibetan Plateau, Xinjiang, Gansu, Heilongjiang, Jilin, and Liaoning Province. This study indicates that the combination of AMSR2 and MODIS observations provides an effective method to obtain finer F/T maps (5-km or lower) for extensive regions. The finer F/T maps improve our knowledge of the F/T state detected by satellite remote sensing, and have a wide range of applications in regional studies considering land surface heterogeneity and models (e.g., community land models).

Photogrammetric UAV sees a surge in use for high-resolution mapping, but its use to map terrain under dense vegetation cover remains challenging due to a lack of exposed ground surfaces. This paper presents a novel object-oriented classification ensemble algorithm to leverage height, texture and contextual information of UAV data to improve landscape classification and terrain estimation. Its implementation incorporates multiple heuristics, such as multi-input machine learning-based classification, object-oriented ensemble, and integration of UAV and GPS surveys for terrain correction. Experiments based on a densely vegetated wetland restoration site showed classification improvement from 83.98% to 96.12% in overall accuracy and from 0.7806 to 0.947 in kappa value. Use of standard and existing UAV terrain mapping algorithms and software produced reliable digital terrain model only over exposed bare grounds (mean error = −0.019 m and RMSE = 0.035 m) but severely overestimated the terrain by ~80% of mean vegetation height in vegetated areas. The terrain correction method successfully reduced the mean error from 0.302 m to −0.002 m (RMSE from 0.342 m to 0.177 m) in low vegetation and from 1.305 m to 0.057 m (RMSE from 1.399 m to 0.550 m) in tall vegetation. Overall, this research validated a feasible solution to integrate UAV and RTK GPS for terrain mapping in densely vegetated environments.