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Crop yield prediction is essential for tasks like determining the optimal profile of crops to be planted, allocating government resources, effectively planning and preparing for aid distribution, making decisions about imports, and so on. Crop yield prediction using remote sensing data during the growing season is helpful to farm planning and management, which has received increasing attention. Information mining from multichannel geo-spatiotemporal data brings many benefits to crop yield prediction. However, most of the existing methods have not fully utilized the dimension reduction technology and the spatiotemporal feature of the data. In this paper, a new approach is proposed to predict the yield from multispatial images by using the dimension reduction method and a 3D convolutional neural network. In addition, regions with similar crop yields should have similar features learned by the network. Thus, metric learning and multitask learning are used to learn more discriminative features. We evaluated the proposed method on county-level soybean yield prediction in the United States, and the experimental results show the effectiveness of the proposed method. The proposed method provides new ideas for crop yield estimation and effectively improves the accuracy of crop yield estimation. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system's capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management. [ABSTRACT FROM AUTHOR]

To date, no method utilizing satellite imagery exists for detailing the locations and functions of critical infrastructure across the United States, making response to natural disasters and other events challenging due to complex infrastructural interdependencies. This paper presents a repeatable, transferable, and explainable method for critical infrastructure analysis and implementation of a robust model for critical infrastructure detection in satellite imagery. This model consists of a DenseNet-161 convolutional neural network, pretrained with the ImageNet database. The model was provided additional training with a custom dataset, containing nine infrastructure classes. The resultant analysis achieved an overall accuracy of 90%, with the highest accuracy for airports (97%), hydroelectric dams (96%), solar farms (94%), substations (91%), potable water tanks (93%), and hospitals (93%). Critical infrastructure types with relatively low accuracy are likely influenced by data commonality between similar infrastructure components for petroleum terminals (86%), water treatment plants (78%), and natural gas generation (78%). Local interpretable model-agnostic explanations (LIME) was integrated into the overall modeling pipeline to establish trust for users in critical infrastructure applications. The results demonstrate the effectiveness of a convolutional neural network approach for critical infrastructure identification, with higher than 90% accuracy in identifying six of the critical infrastructure facility types. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which "Environmental Science and Ecology" ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field. [ABSTRACT FROM AUTHOR]

The remote sensing satellite cluster system, as an important component of the next generation of space architecture in the United States, has important application prospects in the military field. In order to improve the effects of time, with regard to the effectiveness evaluation of the remote sensing satellite cluster system, neural network methods are generally used to satisfy the requirements of real-time decision-making assistance in the military field. However, there are two problems that emerge when applying the existing neural network methods to an effectiveness evaluation of the remote sensing satellite cluster. On the one hand, the neural network model architecture needs to be designed specifically for the remote sensing satellite cluster system. On the other hand, there is still a lack of hyperparameter optimization methods that consume less time and have good optimization effects for the established neural network model. In this regard, two main modifications were made to the back-propagation neural network, to which an effectiveness evaluation was applied. The first comprised a new architecture named BPS, which was designed for the back-propagation neural network so as to improve its prediction accuracy. In BP architecture, one back-propagation neural network is established for each indicator involved in the effectiveness evaluation indicator system of the remote sensing satellite cluster; the output of each back-propagation neural network model is modified to the residual value between the corresponding indicator value and the value that is predicted through a multiple linear regression analysis of the corresponding indicator. The second modification involved the multi-round traversal method, which is based on the three-way decision theory, and it was proposed in order to significantly improve the model's training time, which is a new type of hyperparameter optimization method. The results show that compared with the traditional simulation model, the modified back-propagation neural network model based on three-way decision theory can quickly and effectively provide stable and accurate evaluation results; this can assist with and meet the requirements for real-time decision-making in the military field. [ABSTRACT FROM AUTHOR]

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. "Oceanography," "Environmental Sciences," and "Remote Sensing" are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that "phytoplankton," "chlorophyll," and "ocean" were the most frequently occurring keywords, while "eutrophication management and monitoring," "climate change," "lakes," and "remote-sensing algorithms" were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field. [ABSTRACT FROM AUTHOR]

Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m) spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery, visually classified locations and a softmax neural network model. The model efficiently produced classification surfaces at 1mresolution across roughly 11.6 million hectares (28.8 million acres) with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery. [ABSTRACT FROM AUTHOR]

The rising severity and frequency of wildfires in recent years in the United States have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellite systems provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite fire products have high temporal (1–5 min) but low spatial resolution (≥2 km), and VIIRS polar orbiter satellite fire products have low temporal (~12 h) but high spatial resolution (375 m). This work aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with deep learning (DL) advances to achieve an operational high-resolution, both spatially and temporarily, wildfire monitoring tool. Specifically, this study considers the problem of increasing the spatial resolution of high temporal but low spatial resolution GOES-17 data products using low temporal but high spatial resolution VIIRS data products. The main idea is using an Autoencoder DL model to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and DL architectures are implemented and tested to predict both the fire area and the corresponding brightness temperature. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps. [ABSTRACT FROM AUTHOR]

In the United States (US), salt marshes are especially vulnerable to the effects of projected sea level rise, increased storm frequency, and climatic changes. Sentinel-2 data offer the opportunity to observe the land surface at high spatial resolutions (10 m). The Sentinel-2 data, encompassing Cumberland Island National Seashore, Fort Pulaski National Monument, and Canaveral National Seashore, were analyzed to identify temporal changes in salt marsh presence from 2016 to 2020. ENVI-derived unsupervised and supervised classification algorithms were applied to determine the most appropriate procedure to measure distant areas of salt marsh increases and decreases. The Normalized Difference Vegetation Index (NDVI) was applied to describe the varied vegetation biomass spatially. The results from this approach indicate that the ENVI-derived maximum likelihood classification provides a statistical distribution and calculation of the probability (>90%) that the given pixels represented both water and salt marsh environments. The salt marshes captured by the maximum likelihood classification indicated an overall decrease in salt marsh area presence. The NDVI results displayed how the varied vegetation biomass was analogous to the occurrence of salt marsh changes. Areas representing the lowest NDVI values (−0.1 to 0.1) corresponded to bare soil areas where a salt marsh decrease was detected. [ABSTRACT FROM AUTHOR]

Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations. [ABSTRACT FROM AUTHOR]

Regression algorithms are regularly used for improving the accuracy of satellite precipitation products. In this context, satellite precipitation and topography data are the predictor variables, and gauged-measured precipitation data are the dependent variables. Alongside this, it is increasingly recognised in many fields that combinations of algorithms through ensemble learning can lead to substantial predictive performance improvements. Still, a sufficient number of ensemble learners for improving the accuracy of satellite precipitation products and their large-scale comparison are currently missing from the literature. In this study, we work towards filling in this specific gap by proposing 11 new ensemble learners in the field and by extensively comparing them. We apply the ensemble learners to monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets that span over a 15-year period and over the entire contiguous United States (CONUS). We also use gauge-measured precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The ensemble learners combine the predictions of six machine learning regression algorithms (base learners), namely the multivariate adaptive regression splines (MARS), multivariate adaptive polynomial splines (poly-MARS), random forests (RF), gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and Bayesian regularized neural networks (BRNN), and each of them is based on a different combiner. The combiners include the equal-weight combiner, the median combiner, two best learners and seven variants of a sophisticated stacking method. The latter stacks a regression algorithm on top of the base learners to combine their independent predictions. Its seven variants are defined by seven different regression algorithms, specifically the linear regression (LR) algorithm and the six algorithms also used as base learners. The results suggest that sophisticated stacking performs significantly better than the base learners, especially when applied using the LR algorithm. It also beats the simpler combination methods. [ABSTRACT FROM AUTHOR]

Using remote sensing images to identify crop plots and estimate crop planting area is an important part of agricultural remote sensing monitoring. High-resolution remote sensing images can provide rich information regarding texture, tone, shape, and spectrum of ground objects. With the advancement of sensor and information technologies, it is now possible to categorize crops with pinpoint accuracy. This study defines crop mapping as a semantic segmentation problem; therefore, a deep learning method is proposed to identify the distribution of corn and soybean using the differences in the spatial and spectral features of crops. The study area is located in the southwest of the Great Lakes in the United States, where corn and soybean cultivation is concentrated. The proposed attention mechanism deep learning model, A2SegNet, was trained and evaluated using three years of Sentinel-2 data, collected between 2019 and 2021. The experimental results show that this method is able to fully extract the spatial and spectral characteristics of crops, and its classification effect is significantly better than that of the baseline method, and it has better classification performance than other deep learning models. We cross verified the trained model on the test sets of different years through transfer learning in both spatiotemporal and spatial dimensions. Proving the effectiveness of the attention mechanism in the process of knowledge transfer, A2SegNet showed better adaptability. [ABSTRACT FROM AUTHOR]

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans. [ABSTRACT FROM AUTHOR]

Wildfires are one of the major disasters among many and are responsible for more than 6 million acres burned in the United States alone every year. Accurate, insightful, and timely wildfire detection is needed to help authorities mitigate and prevent further destruction. Uncertainty quantification is always a crucial part of the detection of natural disasters, such as wildfires, and modeling products can be misinterpreted without proper uncertainty quantification. In this study, we propose a supervised deep generative machine-learning model that generates stochastic wildfire detection, allowing fast and comprehensive uncertainty quantification for individual and collective events. In the proposed approach, we also aim to address the patchy and discontinuous Moderate Resolution Imaging Spectroradiometer (MODIS) wildfire product by training the proposed model with MODIS raw and combined bands to detect fire. This approach allows us to generate diverse but plausible segmentations to represent the disagreements regarding the delineation of wildfire boundaries by subject matter experts. The proposed approach generates stochastic segmentation via two model streams in which one learns meaningful stochastic latent distributions, and the other learns the visual features. Two model branches join eventually to become a supervised stochastic image-to-image wildfire detection model. The model is compared to two baseline stochastic machine-learning models: (1) with permanent dropout in training and test phases and (2) with Stochastic ReLU activations. The visual and statistical metrics demonstrate better agreements between the ground truth and the proposed model segmentations. Furthermore, we used multiple scenarios to evaluate the model comprehension, and the proposed Probabilistic U-Net model demonstrates a better understanding of the underlying physical dynamics of wildfires compared to the baselines. [ABSTRACT FROM AUTHOR]

Monitoring of rice planting areas plays an important role in maintaining food security. With powerful automatic feature extraction capability, crop mapping based on deep learning methods has become one of the most important research directions of crop remote sensing recognition. However, the training of deep learning models often requires a large number of samples, which restricts the application of these models in areas with a lack of samples. To address this problem, based on time-series Sentinel-1 SAR data, this study pre-trained the temporal feature-based segmentation (TFBS) model with an attention mechanism (attTFBS) using abundant samples from the United States and then performed an inter-continental transfer of the pre-trained model based on a very small number of samples to obtain rice maps in areas with a lack of samples. The results showed that an inter-continental transferred rice mapping model was feasible to achieve accurate rice maps in Northeast China (F-score, kappa coefficient, recall, and precision were 0.8502, 0.8439, 0.8345, and 0.8669, respectively). The study found that the transferred model exhibited a strong spatiotemporal generalization capability, achieving high accuracy in rice mapping in the three main rice-producing regions of Northeast China. The phenological differences of rice significantly affected the generalization capability of the transferred model, particularly the significant differences in transplanting periods, which could have resulted in a decrease in the generalization capability of the model. Furthermore, the study found that the model transferred based on an extremely limited number of samples could attain a rice recognition accuracy equivalent to that of the model trained from scratch with a substantial number of samples, indicating that the proposed method possessed strong practicality, which could dramatically reduce the sample requirements for crop mapping based on deep learning models, thereby decreasing costs, increasing efficiency, and facilitating large-scale crop mapping in areas with limited samples. [ABSTRACT FROM AUTHOR]

Gridded satellite precipitation datasets are useful in hydrological applications as they cover large regions with high density. However, they are not accurate in the sense that they do not agree with ground-based measurements. An established means for improving their accuracy is to correct them by adopting machine learning algorithms. This correction takes the form of a regression problem, in which the ground-based measurements have the role of the dependent variable and the satellite data are the predictor variables, together with topography factors (e.g., elevation). Most studies of this kind involve a limited number of machine learning algorithms and are conducted for a small region and for a limited time period. Thus, the results obtained through them are of local importance and do not provide more general guidance and best practices. To provide results that are generalizable and to contribute to the delivery of best practices, we here compare eight state-of-the-art machine learning algorithms in correcting satellite precipitation data for the entire contiguous United States and for a 15-year period. We use monthly data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) gridded dataset, together with monthly earth-observed precipitation data from the Global Historical Climatology Network monthly database, version 2 (GHCNm). The results suggest that extreme gradient boosting (XGBoost) and random forests are the most accurate in terms of the squared error scoring function. The remaining algorithms can be ordered as follows, from the best to the worst: Bayesian regularized feed-forward neural networks, multivariate adaptive polynomial splines (poly-MARS), gradient boosting machines (gbm), multivariate adaptive regression splines (MARS), feed-forward neural networks and linear regression. [ABSTRACT FROM AUTHOR]

Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavor. At the same time, tree-based ensemble algorithms are adopted in various fields for solving regression problems with high accuracy and low computational costs. Still, information on which tree-based ensemble algorithm to select for correcting satellite precipitation products for the contiguous United States (US) at the daily time scale is missing from the literature. In this study, we worked towards filling this methodological gap by conducting an extensive comparison between three algorithms of the category of interest, specifically between random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost). We used daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also used earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database. The experiments referred to the entire contiguous US and additionally included the application of the linear regression algorithm for benchmarking purposes. The results suggest that XGBoost is the best-performing tree-based ensemble algorithm among those compared. Indeed, the mean relative improvements that it provided with respect to linear regression (for the case that the latter algorithm was run with the same predictors as XGBoost) are equal to 52.66%, 56.26% and 64.55% (for three different predictor sets), while the respective values are 37.57%, 53.99% and 54.39% for random forests, and 34.72%, 47.99% and 62.61% for gbm. Lastly, the results suggest that IMERG is more useful than PERSIANN in the context investigated. [ABSTRACT FROM AUTHOR]

Monitoring land surface phenology plays a fundamental role in quantifying the impact of climate change on terrestrial ecosystems. Shifts in land surface spring phenology have become a hot spot in the field of global climate change research. While numerous studies have used satellite data to capture the interannual variation of the start of the growing season (SOS), the understanding of spatiotemporal performances of SOS needs to be enhanced. In this study, we retrieved the annual SOS from the Moderate Resolution Imaging Spectroradiometer (MODIS) two-band enhanced vegetation index (EVI2) time series in the conterminous United States from 2001 to 2021, and explored the spatial and temporal patterns of SOS and its trend characteristics in different land cover types. The performance of the satellite-derived SOS was evaluated using the USA National Phenology Network (USA-NPN) and Harvard Forest data. The results revealed that SOS exhibited a significantly delayed trend of 1.537 days/degree (p < 0.01) with increasing latitude. The timing of the satellite-derived SOS was significantly and positively correlated with the in-situ data. Despite the fact that the overall trends were not significant from 2001 to 2021, the SOS and its interannual variability exhibited a wide range of variation across land cover types. The earliest SOS occurred in urban and built-up land areas, while the latest occurred in cropland areas. In addition, mixed trends in SOS were observed in sporadic areas of different land cover types. Our results found that (1) warming hiatus slows the advance of land surface spring phenology across the conterminous United States under climate change, and (2) large-scale land surface spring phenology trends extraction should consider the potential effects of different land cover types. To improve our understanding of climate change, we need to continuously monitor and analyze the dynamics of the land surface spring phenology. [ABSTRACT FROM AUTHOR]

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but requires capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service's (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA's experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs. [ABSTRACT FROM AUTHOR]

Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes. [ABSTRACT FROM AUTHOR]

Remote sensing has been heralded as the silver bullet in water quality modeling and watershed management, and yet a quantitative mapping of where its applicability is likely and most useful has not been undertaken so far. Here, we combine geospatial models of cloud cover as a proxy for the likelihood of acquiring remote scenes and the shortest time of travel to population centers as a proxy for accessibility to ground-truth remote sensing data for water quality monitoring and produce maps of the potential of remote sensing in watershed management in the United States. We generate several maps with different cost-payoff relationships to help stakeholders plan and incentivize remote sensing-based monitoring campaigns. Additionally, we combine these remote sensing potential maps with spatial indices of population, water demand, ecosystem services, pollution risk, and monitoring coverage deficits to identify where remote sensing likely has the greatest role to play. We find that the Southwestern United States and the Central plains regions are generally suitable for remote sensing for watershed management even under the most stringent costing projections, but that the potential for using remote sensing can extend further North and East as constraints are relaxed. We also find large areas in the Southern United States and sporadic watersheds in the Northeast and Northwest seaboards and the Midwest would likely benefit most from using remote sensing for watershed monitoring. Although developed herein for watershed decision support in the United States, our approach is readily generalizable to other environmental domains and across the world. [ABSTRACT FROM AUTHOR]

The advances of remote sensing techniques allow for the generation of dense point clouds to detect detailed surface changes up to centimeter/millimeter levels. However, there is still a need for an easy method to derive such surface changes based on digital elevation models generated from dense point clouds while taking into consideration spatial varied uncertainty. We present a straightforward method, Las2DoD, to quantify surface change directly from point clouds with spatially varied uncertainty. This method uses a cell-based Welch's t-test to determine whether each cell of a surface experienced a significant elevation change based on the points measured within the cell. Las2DoD is coded in Python with a simple graphic user interface. It was applied in a case study to quantify hillslope erosion on two plots: one dominated by rill erosion, and the other by sheet erosion, in southeastern United States. The results from the rilled plot indicate that Las2DoD can estimate 90% of the total measured sediment, in comparison to 58% and 70% from two other commonly used methods. The Las2DOD-derived result is less accurate (65%) but still outperforms the other two methods (30% and 48%) for the plot dominated by sheet erosion. Las2DoD captures more low-magnitude changes and is particularly useful where surface changes are small but contribute significantly to the total surface change when summed. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the extent to which Sentinel-2 Normalised Difference Vegetation Index (NDVI) reflects soil moisture conditions, and whether this product offers an improvement over Landsat-8. Based on drought exposure, cloud-free imagery availability, and measured soil moisture, five sites in the Southwestern United States were selected. These sites, normally dry to arid, were in various states of drought. A secondary focus was therefore the performance of the NDVI under extreme conditions. Following supervised classification, the NDVI values for one-kilometre radius areas were calculated. Sentinel-2 NDVI variants using Spectral Bands 8 (10 m spatial resolution), 5, 6, 7, and 8A (20 m spatial resolution) were calculated. Landsat-8 NDVI was calculated at 30 m spatial resolution. Pearson correlation analysis was undertaken for NDVI against moisture at various depths. To assess the difference in correlation strength, a principal component analysis was performed on the combination of all bands and the combination of the new red-edge bands. Performance of the red-edge NDVI against the standard near infrared (NIR) was then evaluated using a Steiger comparison. No significant correlations between Landsat-8 NDVI and soil moisture were found. Significant correlations at depths of less than 30 cm were present between Sentinel-2 NDVI and soil moisture at three sites. The remaining two sites were characterised by low vegetation cover, suggesting a cover threshold of approximately 30–40% is required for a correlation to be present. At all sites of significant positive moisture to NDVI correlation, the linear combination of the red-edge bands produced stronger correlations than the poorer spectral but higher spatial resolution band. NDVI calculated using the higher spectral resolution bands may therefore be of greater use in this context than the higher spatial resolution option. Results suggest potential for the application of Sentinel-2 NDVI in soil moisture monitoring, even in extreme environments. To the best of our knowledge, this paper represents the first study of this kind using Sentinel-2. [ABSTRACT FROM AUTHOR]

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest. [ABSTRACT FROM AUTHOR]

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g., climate related-changes) versus direct and rapid changes (e.g., fire, land development) is challenging as changes can be confounded by time-dependent patterns, and variation associated with climatic factors. In the present study, we leveraged a method that we previously developed for a pilot study to address these confounding factors by evaluating NDVI change using autoregression techniques that compare results from univariate (NDVI vs. time) and multivariate analyses (NDVI vs. time and climatic factors) for 7,660,636 1 km x 1 km pixels comprising the 48 contiguous states of the USA, over a 25-year period (1989–2013). NDVI changed significantly for 48% of the nation over the 25-year period in the univariate analyses where most significant trends (85%) indicated an increase in greenness over time. By including climatic factors in the multivariate analyses of NDVI over time, the detection of significant NDVI trends increased to 53% (an increase of 5%). Comparisons of univariate and multivariate analyses for each pixel showed that less than 4% of the pixels had a significant NDVI trend attributable to gradual climatic changes while the remainder of pixels with a significant NDVI trend indicated that changes were due to direct factors. While most NDVI changes were attributable to direct factors like wildfires, drought or flooding of agriculture, and tree mortality associated with insect infestation, these conditions may be indirectly influenced by changes in climatic factors. [ABSTRACT FROM AUTHOR]

Satellite-retrieved aerosol optical depth (AOD) has become an important predictor of ground-level particulate matter (PM) and greatly empowered air pollution research worldwide. We evaluated the AOD parameters included in the Collection 6 aerosol product of the Moderate Resolution Imaging Spectroradiometer (MODIS) for two key factors affecting their applications in air quality research—coverage and accuracy—over the continental US. For the high confidence retrievals (QAC 3), the 10 km DB-DT combined AOD has the best coverage nationwide (29.7% of the days in a year in any given 12 km grid cell). While the Eastern US generally had more successful AOD retrievals, the highest spatial coverage of AOD parameters were found in California (>55%) and other vegetated parts of the Western US. If lower QAC retrievals were included, the coverage of the 10 km DB AOD was dramatically increased to 49.6%. In the Eastern US, the QAC 3 retrievals of all four AOD parameters are highly correlated with AERONET observations (correlation coefficients between 0.80 and 0.92). In the Western US, positive retrieval errors existed in all MODIS AOD parameters, resulting in lower correlations with AERONET. AOD retrieval errors showed significant dependence on flight geometry, land cover type, and weather conditions. To ensure appropriate use of these AOD values, air quality researchers should carefully balance the needs for coverage and accuracy, and develop additional data screening criteria based on their study design. [ABSTRACT FROM AUTHOR]

In this U.S. Geological Survey study of forest land cover across the conterminous U.S. (CONUS), specific proportions and rates of forest conversion to developed (urban) land were assessed on an ecoregional basis. The study period was divided into six time intervals between 1973 and 2011. Forest land cover was the source of 40% or more of the new urban land in 35 of the 84 ecoregions located within the CONUS. In 11 of these ecoregions this threshold exceeded in every time interval. When the percent of change, forest to urban, was compared to the percent of forest in each ecoregion, 58 ecoregions had a greater percent of change and, in six of those, change occurred in every time interval. Annual rates of forest to urban land cover change of 0.2% or higher occurred in 12 ecoregions at least once and in one ecoregion in all intervals. There were three ecoregions where the above conditions were met for nearly every time interval. Even though only a small number of the ecoregions were heavily impacted by forest loss to urban development within the CONUS, the ecosystemservices provided by undeveloped forest land cover need to be quantified more completely to better inform future regional land management. [ABSTRACT FROM AUTHOR]

As the frequency and size of wildfires increase, accurate assessment of burn severity is essential for understanding fire effects and evaluating post-fire vegetation impacts. Remotely-sensed imagery allows for rapid assessment of burn severity, but it also needs to be field validated. Permanent forest inventory plots can provide burn severity information for the field validation of remotely-sensed burn severity metrics, although there is often a mismatch between the size and shape of the inventory plot and the resolution of the rasterized images. For this study, we used two distinct datasets: (1) ground-based inventory data from the United States national forest inventory to calculate ground-based burn severity; and (2) remotely-sensed data from the Monitoring Trends in Burn Severity (MTBS) database to calculate different remotely-sensed burn severity metrics based on six weighting scenarios. Our goals were to test which MTBS metric would best align with the burn severity of national inventory plots observed on the ground, and to identify the superior weighting scenarios to extract pixel values from a raster image in order to match burn severity of the national inventory plots. We fitted logistic and ordinal regression models to predict the ground-based burn severity from the remotely-sensed burn severity averaged from six weighting scenarios. Among the weighting scenarios, two scenarios assigned weights to pixels based on the area of a pixel that intersected any parts of a national inventory plot. Based on our analysis, 9-pixel weighted averages of the Relative differenced Normalized Burn Ratio (RdNBR) values best predicted the ground-based burn severity of national inventory plots. Finally, the pixel specific weights that we present can be used to link other Landsat-derived remote sensing metrics with United States forest inventory plots. [ABSTRACT FROM AUTHOR]

The launch of the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S-5P) satellite has revolutionized pollution observations from space. The purpose of this study was to link spatiotemporal variations in TROPOMI methane (CH4) columns to meteorological flow patterns over the Permian Basin, the largest oil and second-largest natural gas producing region in the United States. Over a two-year period (1 December 2018–1 December 2020), the largest average CH4 enhancements were observed near and to the north and west of the primary emission regions. Four case study periods—two with moderate westerly winds associated with passing weather disturbances (8–15 March 2019 and 1 April–10 May 2019) and two other periods dominated by high pressure and low wind speeds (16–23 March 2019 and 24 September–9 October 2020)—were analyzed to better understand meteorological drivers of the variability in CH4. Meteorological observations and analyses combined with TROPOMI observations suggest that weakened transport out of the Basin during low wind speed periods contributes to CH4 enhancements throughout the Basin, while valley and slope flows may explain the observed western expansion of the Permian Basin CH4 anomaly. [ABSTRACT FROM AUTHOR]

Tall fescue (Schedonorus arundinaceus) is a common perennial forage in cattle pastures of the southeastern United States. A mutualistic fungal endophyte normally infects the grass and produces ergot alkaloids toxic to livestock, but fungal biotypes that have no ergot alkaloid production have been developed. Here remote sensing methods were used to assess plant health in 1 ha grazed paddocks with application amongst different combinations of fertilizer sources (inorganic and broiler litter) and endophyte associations (wild, novel–tall fescue MaxQ type with novel endophyte, and free). Broiler litter fertilization is common in the region due to the presence of many chicken farms. Moreover, broiler litter costs are comparable to inorganic fertilizer depending on distance from source to application. Incorporating remote sensing, we tested the sensitivity of three indices: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI) to assess fescue plant health. Indices were obtained from satellite imagery provided by Landsat 7 ETM+ between the years 2005 and 2009. Sensitivity analytics suggested that LSWI was the optimum index to determine fescue plant health. The year experiencing drought (determined by annual precipitation) showed significant difference between fertilizer types (p = 0.05) and a nearly significant difference between endophyte associations (p = 0.08). There was no significant difference in years with normal or wet precipitation rates due to tall fescue endophyte association or type of fertilization. Limited availability of satellite imagery during parts of the five years of study might have influenced outcomes of statistical analyses. Nevertheless, the data and findings point to the potential use of satellite imagery in assessing grazingland tall fescue health and advancing the concept of poultry manureshed in the region or elsewhere where poultry manure production is extensive. [ABSTRACT FROM AUTHOR]

Using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Bidirectional Reflectance Distribution Function (BRDF) with the U.S. Department of Agriculture's National Agricultural Statistics Service's Cropland Data Layer (CDL), the daily albedo of homogenous agricultural fields was derived for 51 common United States field crops by wavelength, sky-type, day of year, crop, and hardiness zone from 2015–2018. This study suggests that crop growth can result in changes in reflectivity up to a factor of 2 at most wavelengths and is unique per crop type in timing and range. Additionally, broadband impacts were studied and found to be less conspicuous than the individual wavelengths, but still significant. The results were used to evaluate a common method of cropland albedo estimation, normalized difference vegetation index (NDVI) as a proxy for albedo, and this method was found to have some significant limitations dependent on wavelength and date. Finally, a database of surface albedo variations as a function of growing period is constructed for common field crops to the United States (as well as additional land-cover types). This database can be used to aid both satellite remote-sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors. [ABSTRACT FROM AUTHOR]

Assessing fire behavior in shrubland/grassland ecosystems of the western United States has proven especially problematic, in part due to the complex nature of the vegetation and its relationships with prior fire history events. Our goals in this study were (1) to determine if we can effectively leverage the high temporal resolution capabilities of current remote sensing systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve upon shrub and grassland mapping and (2) to determine if these improvements alter and improve fire behavior model results in these grass- and shrub-dominated systems. The study focused on the shrublands and grasslands of the Owyhee Basin, which is located primarily in southern Idaho. Shrubland and grassland fuel load dynamics were characterized using Normalized Difference Vegetation Index (NDVI) and Net Primary Production (NPP) datasets (both derived from MODIS). NDVI shrub and grassland values were converted to biomass, and custom fire behavior fuel models were then developed to evaluate the impacts of surface fuel changes on fire behaviors. Results from the study include the following: (1) high intra- and interannual spectral variability characterized these shrubland/grassland ecosystems, and this spectral variability was highly correlated with climate variables, most notably precipitation; (2) fire activity had a higher likelihood of occurring in areas where the NDVI (and biomass) differential between spring and summer values was especially high; (3) the annual fuel loads estimated from MODIS NPP showed that live herbaceous fuel loads were closely correlated with annual precipitation; (4) estimated fuel load accumulation was higher on shrublands than grasslands with the same vegetation productivity; (5) the total fuel load on shrublands was impacted by shrubland age, and live woody fuel load was over 66% of the total fuel load; and (6) comparisons of simulated fire behavior and spread between dynamic and static fuel loads, the latter estimates being obtained from the operational and nationwide LANDFIRE program, showed clear differences in fire indices and fire burn areas between the dynamic fuel loads and the static fuel loads. Current standard fuel models appear to have bias in underestimating the fire spread and total burnable area. [ABSTRACT FROM AUTHOR]

Land surface soil moisture (SM) monitoring is crucial for global water cycle and agricultural dryness research. The FengYun-3C Microwave Radiation Imager (FY-3C/MWRI) collects various Earth geophysical parameters, and the FY-3C/MWRI SM product (FY-3C VSM) has been widely applied to determine regional-scale surface SM contents. The FY-3C VSM retrieval accuracy in different seasons was evaluated by calculating the root mean square error (RMSE), unbiased RMSE (ubRMSE), mean absolute error (MAE), and correlation coefficient (R) values between the retrieved and measured SM. A lower accuracy in July (RMSE = 0.164 cm3/cm3, ubRMSE = 0.130 cm3/cm3, and MAE = 0.120 cm3/cm3) than in the other months was found due to the impacts of vegetation and climate variations. To show a detailed relationship between SM and multiple factors, including vegetation coverage, location, and elevation, quantile regression (QR) models were used to calculate the correlations at different quantiles. Except for the elevation at the 0.9 quantile, the QR models of the measured SM with the FY-3C VSM, MODIS NDVI, latitude, and longitude at each quantile all passed the significance test at the 0.005 level. Thus, the MODIS NDVI, latitude, and longitude were selected for error correction during the surface SM retrieval process using FY-3C VSM. Multivariate linear regression (MLR) and multivariate back-propagation neural network (MBPNN) models with different numbers of input variables were built to improve the SM monitoring results. The MBPNN model with three inputs (MBPNN-3) achieved the highest R (0.871) and lowest RMSE (0.034 cm3/cm3), MAE (0.026 cm3/cm3), and mean relative error (MRE) (20.7%) values, which were better than those of the MLR models with one, two, or three independent variables (MLR-1, -2, -3) and those of the MBPNN models with one or two inputs (MBPNN-1, -2). Then, the MBPNN-3 model was applied to generate the regional SM in the United States from January 2019 to October 2019. The estimated SM images were more consistent with the measured SM than the FY-3C VSM. This work indicated that combining FY-3C VSM data with the MBPNN-3 model could provide precise and reliable SM monitoring results. [ABSTRACT FROM AUTHOR]

Remote sensing of ice phenology for small lakes is hindered by a lack of satellite observations with both high temporal and spatial resolutions. By merging multi-source satellite data over individual lakes, we present a new algorithm that successfully estimates ice freeze and thaw timing for lakes with surface areas as small as 0.13 km2 and obtains consistent results across a range of lake sizes. We have developed an approach for classifying ice pixels based on the red reflectance band of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, with a threshold calibrated against ice fraction from Landsat Fmask over each lake. Using a filter derived from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) surface air temperature product, we removed outliers in the time series of lake ice fraction. The time series of lake ice fraction was then applied to identify lake ice breakup and freezeup dates. Validation results from over 296 lakes in Maine indicate that the satellite-based lake ice timing detection algorithm perform well, with mean absolute error (MAE) of 5.54 days for breakup dates and 7.31 days for freezeup dates. This algorithm can be applied to lakes worldwide, including the nearly two million lakes with surface area between 0.1 and 1 km2. [ABSTRACT FROM AUTHOR]

The evaluation of historical water use in the Upper Rio Grande Basin (URGB), United States and Mexico, using Landsat-derived actual evapotranspiration (ETa) from 1986 to 2015 is presented here as the first study of its kind to apply satellite observations to quantify long-term, basin-wide crop consumptive use in a large basin. The rich archive of Landsat imagery combined with the Operational Simplified Surface Energy Balance (SSEBop) model was used to estimate and map ETa across the basin and over irrigated fields for historical characterization of water-use dynamics. Monthly ETa estimates were evaluated using six eddy-covariance (EC) flux towers showing strong correspondence (r2 > 0.80) with reasonable error rates (root mean square error between 6 and 19 mm/month). Detailed spatiotemporal analysis using peak growing season (June–August) ETa over irrigated areas revealed declining regional crop water-use patterns throughout the basin, a trend reinforced through comparisons with gridded ETa from the Max Planck Institute (MPI). The interrelationships among seven agro-hydroclimatic variables (ETa, Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), maximum air temperature (Ta), potential ET (ETo), precipitation, and runoff) are all summarized to support the assessment and context of historical water-use dynamics over 30 years in the URGB. [ABSTRACT FROM AUTHOR]

In this study an initial validation of the Landsat 8 (L8) Operational Land Imager (OLI) Surface Reflectance (SR) product was performed. The OLI SR product is derived from the L8 Top-of-Atmosphere product via the Landsat Surface Reflectance Code (LaSRC) software and generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of this study is to develop and evaluate proper validation methodology for the OLI L2 SR product. Validation was performed using near-simultaneous ground truth SR measurements during Landsat 8 overpasses at 13 sites located in the U.S., Brazil, Chile and France. The ground truth measurements consisted of field spectrometer measurements, automated hyperspectral ground measurements operated by the Radiometric Calibration Network (RadCalNet) and derived SR measurements from Airborne Observation Platforms (AOP) operated by the National Ecological Observatory Network (NEON). The 13 sites cover a broad range of 0–0.5 surface reflectance units across the reflective solar spectrum. Results show that the mean reflectance difference between OLI L2 SR products and ground truth measurements for the 13 validation sites and all bands was under 2.5%. The largest uncertainties of 11% and 8% were found in the CA and Blue bands, respectively; whereas, the longer wavelength bands were within 4% or less. Results consistently indicated similarity between the OLI L2 SR product and ground truth data, especially in longer wavelengths over dark and bright targets, while less reliable performance was observed in shorter wavelengths and sparsely vegetated targets. [ABSTRACT FROM AUTHOR]

Understanding and monitoring the dynamics of rangeland heterogeneity through time and across space is critical for the effective management and conservation of rangeland systems and the sustained supply of the ecosystem goods and services they provide. Conventional approaches (both field-based and remote sensing) to monitoring rangeland productivity fail to effectively capture important aspects of this heterogeneity. While field methods can effectively capture high levels of detail at fine spatial and temporal resolutions, they are limited in their applicability and scalability to larger spatial extents and longer time periods. Alternatively, remote sensing based approaches that scale broad spatiotemporal extents simplify important heterogeneity occurring at fine scales. We address these limitations to monitoring rangeland productivity by combining a continuous plant functional type (PFT) fractional cover dataset with a Landsat derived gross primary production (GPP) and net primary production (NPP) model. Integrating the annual PFT dataset with a 16-day Landsat normalized difference vegetation (NDVI) composite dataset enabled us to disaggregate the pixel level NDVI values to the sub-pixel PFTs. These values were incorporated into the productivity algorithm, enabling refined estimations of 16-day GPP and annual NPP for the PFTs that composed each pixel. We demonstrated the results of these methods on a set of representative rangeland sites across the western United States. Partitioning rangeland productivity to sub-pixel PFTs revealed new dynamics and insights to aid the sustainable management of rangelands. [ABSTRACT FROM AUTHOR]

Research in different agricultural sectors, including in crop loss estimation during flood and yield estimation, substantially rely on inundation information. Spaceborne remote sensing has widely been used in the mapping and monitoring of floods. However, the inability of optical remote sensing to cloud penetration and the scarcity of fine temporal resolution SAR data hinder the application of flood mapping in many cases. Soil Moisture Active Passive (SMAP) level 4 products, which are model-driven soil moisture data derived from SMAP observations and are available at 3-h intervals, can offer an intermediate but effective solution. This study maps flood progress in croplands by incorporating SMAP surface soil moisture, soil physical properties, and national floodplain information. Soil moisture above the effective soil porosity is a direct indication of soil saturation. Soil moisture also increases considerably during a flood event. Therefore, this approach took into account three conditions to map the flooded pixels: a minimum of 0.05 m3m−3 increment in soil moisture from pre-flood to post-flood condition, soil moisture above the effective soil porosity, and the holding of saturation condition for the 72 consecutive hours. Results indicated that the SMAP-derived maps were able to successfully map most of the flooded areas in the reference maps in the majority of the cases, though with some degree of overestimation (due to the coarse spatial resolution of SMAP). Finally, the inundated croplands are extracted from saturated areas by Spatial Hazard Zone areas (SHFA) of Federal Emergency Management Agency (FEMA) and cropland data layer (CDL). The flood maps extracted from SMAP data are validated with FEMA-declared affected counties as well as with flood maps from other sources. [ABSTRACT FROM AUTHOR]

Studying the effects of drought on forest ecosystems is important in developing a better understanding of forest phenology and productivity. Many previous studies were based on single drought events, whereas effects of recurrent droughts have not been yet fully investigated. This study jointly analyzed the spatial–temporal change of drought patterns with forest phenology and productivity between 2000–2015 in the western Central Hardwood Forests at Missouri, Arkansas Illinois, Oklahoma, and Kansas of the US. Characteristics of forest phenology and productivity were captured by utilizing the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing 16-day MOD13Q1 data and Savitsky–Golay (S-G) filtering method. Spatial-temporal drought patterns were assessed by empirical orthogonal function (EOF) on self-calibrating Palmer Drought Severity Index (scPDSI) time series. Our results revealed four drought zones: sporadic severe drought zone, cyclic light drought zone, minor drought zone, and moderate drought zone. The results showed that at the regional scale, drought effects on forest phenology and productivity depended on forest type and drought intensity. The cyclic light drought did not result in a notable decline of growing season length and productivity, while both minor drought and severe drought were followed by a significant decrease of forest growing season length and productivity. This research presents an alternative method to analyze the impacts of drought on regional forest dynamics. [ABSTRACT FROM AUTHOR]

Modeling vegetation gross primary productivity (GPP) is crucial to understanding the land–atmosphere interactions and, hence, the global carbon cycle. While studies have demonstrated that satellite-based models could well simulate intra-annual variation of vegetation GPP, there is a need to understand our ability to capture interannual GPP variability. This study compares the spatiotemporal performance of six satellite-based models in regional modeling of annual GPP for deciduous broadleaf forests across the eastern United States. The 2001–2012 average annual gross primary productivities (AAGPPs) derived from different models have mismatched spatial patterns with divergent changing trends along both latitude and longitude. Evaluation using flux tower data indicates that some models could have considerable biases on a yearly basis. All tested models, despite performing well on the 8-day basis because of the underlying strong seasonality in vegetation productivity, fail to capture interannual variation of GPP across sites and years. Our study identifies considerable modeling uncertainties on a yearly basis even for an extensively studied biome of deciduous broadleaf forest at both site and large scales. Improvements to the current satellite-based models have to be made to capture interannual GPP variation in addition to intra-annual variation. [ABSTRACT FROM AUTHOR]

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30–210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling. [ABSTRACT FROM AUTHOR]