Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications.

Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth's status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth's surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.

The Spectral Species Concept in Living Color.

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Integrating remote sensing with ecology and evolution to advance biodiversity conservation.

Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth's biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth's biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration.

Assessment of FSDAF Accuracy on Cotton Yield Estimation Using Different MODIS Products and Landsat Based on the Mixed Degree Index with Different Surroundings.

Research on fusion modeling of high spatial and temporal resolution images typically uses MODIS products at 500 m and 250 m resolution with Landsat images at 30 m, but the effect on results of the date of reference images and the 'mixed pixels' nature of moderate-resolution imaging spectroradiometer (MODIS) images are not often considered. In this study, we evaluated those effects using the flexible spatiotemporal data fusion model (FSDAF) to generate fusion images with both high spatial resolution and frequent coverage over three cotton field plots in the San Joaquin Valley of California, USA. Landsat images of different dates (day-of-year (DOY) 174, 206, and 254, representing early, middle, and end stages of the growing season, respectively) were used as reference images in fusion with two MODIS products (MOD09GA and MOD13Q1) to produce new time-series fusion images with improved temporal sampling over that provided by Landsat alone. The impact on the accuracy of yield estimation of the different Landsat reference dates, as well as the degree of mixing of the two MODIS products, were evaluated. A mixed degree index (MDI) was constructed to evaluate the accuracy and time-series fusion results of the different cotton plots, after which the different yield estimation models were compared. The results show the following: (1) there is a strong correlation (above 0.6) between cotton yield and both the Normalized Difference Vegetation Index (NDVI) from Landsat (NDVIL30) and NDVI from the fusion of Landsat with MOD13Q1 (NDVIF250). (2) Use of a mid-season Landsat image as reference for the fusion of MODIS imagery provides a better yield estimation, 14.73% and 17.26% higher than reference images from early or late in the season, respectively. (3) The accuracy of the yield estimation model of the three plots is different and relates to the MDI of the plots and the types of surrounding crops. These results can be used as a reference for data fusion for vegetation monitoring using remote sensing at the field scale.

Current and near-term advances in Earth observation for ecological applications.

There is an unprecedented array of new satellite technologies with capabilities for advancing our understanding of ecological processes and the changing composition of the Earth's biosphere at scales from local plots to the whole planet. We identified 48 instruments and 13 platforms with multiple instruments that are of broad interest to the environmental sciences that either collected data in the 2000s, were recently launched, or are planned for launch in this decade. We have restricted our review to instruments that primarily observe terrestrial landscapes or coastal margins and are available under free and open data policies. We focused on imagers that passively measure wavelengths in the reflected solar and emitted thermal spectrum. The suite of instruments we describe measure land surface characteristics, including land cover, but provide a more detailed monitoring of ecosystems, plant communities, and even some species then possible from historic sensors. The newer instruments have potential to greatly improve our understanding of ecosystem functional relationships among plant traits like leaf mass area (LMA), total nitrogen content, and leaf area index (LAI). They provide new information on physiological processes related to photosynthesis, transpiration and respiration, and stress detection, including capabilities to measure key plant and soil biophysical properties. These include canopy and soil temperature and emissivity, chlorophyll fluorescence, and biogeochemical contents like photosynthetic pigments (e.g., chlorophylls, carotenoids, and phycobiliproteins from cyanobacteria), water, cellulose, lignin, and nitrogen in foliar proteins. These data will enable us to quantify and characterize various soil properties such as iron content, several types of soil clays, organic matter, and other components. Most of these satellites are in low Earth orbit (LEO), but we include a few in geostationary orbit (GEO) because of their potential to measure plant physiological traits over diurnal periods, improving estimates of water and carbon budgets. We also include a few spaceborne active LiDAR and radar imagers designed for quantifying surface topography, changes in surface structure, and 3-dimensional canopy properties such as height, area, vertical profiles, and gap structure. We provide a description of each instrument and tables to summarize their characteristics. Lastly, we suggest instrument synergies that are likely to yield improved results when data are combined.

Estimation of a New Canopy Structure Parameter for Rice Using Smartphone Photography.

The objective of this study was to develop a low-cost method for rice growth information obtained quickly using digital images taken with smartphone. A new canopy parameter, namely, the canopy volume parameter (CVP), was proposed and developed for rice using the leaf area index (LAI) and plant height (PH). Among these parameters, the CVP was selected as an optimal parameter to characterize rice yields during the growth period. Rice canopy images were acquired with a smartphone. Image feature parameters were extracted, including the canopy cover (CC) and numerous vegetation indices (VIs), before and after image segmentation. A rice CVP prediction model in which the CC and VIs served as independent variables was established using a random forest (RF) regression algorithm. The results revealed the following. The CVP was better than the LAI and PH for predicting the final yield. And a CVP prediction model constructed according to a local modelling method for distinguishing different types of rice varieties was the most accurate (coefficient of determination (R2) = 0.92; root mean square error (RMSE) = 0.44). These findings indicate that digital images can be used to track the growth of crops over time and provide technical support for estimating rice yields.

Leaf reflectance spectra capture the evolutionary history of seed plants.

Leaf reflectance spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to a global dataset of over 16 000 leaf-level spectra (400-2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits. Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales.

Author Correction: Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications.

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Assessing the Impact of Spatial Resolution on the Estimation of Leaf Nitrogen Concentration Over the Full Season of Paddy Rice Using Near-Surface Imaging Spectroscopy Data.

Timely monitoring nitrogen status of rice crops with remote sensing can help us optimize nitrogen fertilizer management and reduce environmental pollution. Recently, the use of near-surface imaging spectroscopy is emerging as a promising technology that can collect hyperspectral images with spatial resolutions ranging from millimeters to decimeters. The spatial resolution is crucial for the efficiency in the image sampling across rice plants and the separation of leaf signals from the background. However, the optimal spatial resolution of such images for monitoring the leaf nitrogen concentration (LNC) in rice crops remains unclear. To assess the impact of spatial resolution on the estimation of rice LNC, we collected ground-based hyperspectral images throughout the entire growing season over 2 consecutive years and generated ten sets of images with spatial resolutions ranging from 1.3 to 450 mm. These images were used to determine the sensitivity of LNC prediction to spatial resolution with three groups of vegetation indices (VIs) and two multivariate methods Gaussian Process regression (GPR) and Partial least squares regression (PLSR). The reflectance spectra of sunlit-, shaded-, and all-leaf leaf pixels separated from background pixels at each spatial resolution were used to predict LNC with VIs, GPR and PLSR, respectively. The results demonstrated all-leaf pixels generally exhibited more stable performance than sunlit- and shaded-leaf pixels regardless of estimation approaches. The predictions of LNC required stage-specific LNC~VI models for each vegetative stage but could be performed with a single model for all the reproductive stages. Specifically, most VIs achieved stable performances from all the resolutions finer than 14 mm for the early tillering stage but from all the resolutions finer than 56 mm for the other stages. In contrast, the global models for the prediction of LNC across the entire growing season were successfully established with the approaches of GPR or PLSR. In particular, GPR generally exhibited the best prediction of LNC with the optimal spatial resolution being found at 28 mm. These findings represent significant advances in the application of ground-based imaging spectroscopy as a promising approach to crop monitoring and understanding the effects of spatial resolution on the estimation of rice LNC.

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact.

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.

Author Correction: ISS observations offer insights into plant function.

In the version of this Comment previously published, in Box 1, the spacing of the GEDI footprints should have read 60 m along the track, not 25 m. Also the second affiliation for Susan Ustin was incorrect, she is only associated with the University of California, Davis. These errors have now been corrected.

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data.

Quantifying biomass consumption and carbon release is critical to understanding the role of fires in the carbon cycle and air quality. We present a methodology to estimate the biomass consumed and the carbon released by the California Rim fire by integrating postfire airborne LiDAR and multitemporal Landsat Operational Land Imager (OLI) imagery. First, a support vector regression (SVR) model was trained to estimate the aboveground biomass (AGB) from LiDAR-derived metrics over the unburned area. The selected model estimated AGB with an R2 of 0.82 and RMSE of 59.98 Mg/ha. Second, LiDAR-based biomass estimates were extrapolated to the entire area before and after the fire, using Landsat OLI reflectance bands, Normalized Difference Infrared Index, and the elevation derived from LiDAR data. The extrapolation was performed using SVR models that resulted in R2 of 0.73 and 0.79 and RMSE of 87.18 (Mg/ha) and 75.43 (Mg/ha) for the postfire and prefire images, respectively. After removing bias from the AGB extrapolations using a linear relationship between estimated and observed values, we estimated the biomass consumption from postfire LiDAR and prefire Landsat maps to be 6.58 ± 0.03 Tg (1012 g), which translate into 12.06 ± 0.06 Tg CO2e released to the atmosphere, equivalent to the annual emissions of 2.57 million cars.

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR.

BACKGROUND: Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them. RESULTS: Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m-2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R2 ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha-1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha-1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R2 ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha-1 [between 0.69-0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha-1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R2 was between 0.58-0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha-1 for the echo-based model, whereas for the CHM R2 was between 0.37-0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha-1. CONCLUSIONS: Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m-2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m-2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m-2.

Assessing the Spectral Properties of Sunlit and Shaded Components in Rice Canopies with Near-Ground Imaging Spectroscopy Data.

Monitoring the components of crop canopies with remote sensing can help us understand the within-canopy variation in spectral properties and resolve the sources of uncertainties in the spectroscopic estimation of crop foliar chemistry. To date, the spectral properties of leaves and panicles in crop canopies and the shadow effects on their spectral variation remain poorly understood due to the insufficient spatial resolution of traditional spectroscopy data. To address this issue, we used a near-ground imaging spectroscopy system with high spatial and spectral resolutions to examine the spectral properties of rice leaves and panicles in sunlit and shaded portions of canopies and evaluate the effect of shadows on the relationships between spectral indices of leaves and foliar chlorophyll content. The results demonstrated that the shaded components exhibited lower reflectance amplitude but stronger absorption features than their sunlit counterparts. Specifically, the reflectance spectra of panicles had unique double-peak absorption features in the blue region. Among the examined vegetation indices (VIs), significant differences were found in the photochemical reflectance index (PRI) between leaves and panicles and further differences in the transformed chlorophyll absorption reflectance index (TCARI) between sunlit and shaded components. After an image-level separation of canopy components with these two indices, statistical analyses revealed much higher correlations between canopy chlorophyll content and both PRI and TCARI of shaded leaves than for those of sunlit leaves. In contrast, the red edge chlorophyll index (CIRed-edge) exhibited the strongest correlations with canopy chlorophyll content among all vegetation indices examined regardless of shadows on leaves. These findings represent significant advances in the understanding of rice leaf and panicle spectral properties under natural light conditions and demonstrate the significance of commonly overlooked shaded leaves in the canopy when correlated to canopy chlorophyll content.

Measuring landscape-scale spread and persistence of an invaded submerged plant community from airborne remote sensing.

Processes of spread and patterns of persistence of invasive species affect species and communities in the new environment. Predicting future rates of spread is of great interest for timely management decisions, but this depends on models that rely on understanding the processes of invasion and historic observations of spread and persistence. Unfortunately, the rates of spread and patterns of persistence are difficult to model or directly observe, especially when multiple rates of spread and diverse persistence patterns may be co-occurring over the geographic distribution of the invaded ecosystem. Remote sensing systematically acquires data over large areas at fine spatial and spectral resolutions over multiple time periods that can be used to quantify spread processes and persistence patterns. We used airborne imaging spectroscopy data acquired once a year for 5 years from 2004 to 2008 to map an invaded submerged aquatic vegetation (SAV) community across 2220 km2 of waterways in the Sacramento-San Joaquin River Delta, California, USA, and measured its spread rate and its persistence. Submerged aquatic vegetation covered 13-23 km2 of the waterways (6-11%) every year. Yearly new growth accounted for 40-60% of the SAV area, ~50% of which survived to following year. Spread rates were overall negative and persistence decreased with time. From this dataset, we were able to identify both radial and saltatorial spread of the invaded SAV in the entire extent of the Delta over time. With both decreasing spread rate and persistence, it is possible that over time the invasion of this SAV community could decrease its ecological impact. A landscape-scale approach allows measurements of all invasion fronts and the spatial anisotropies associated with spread processes and persistence patterns, without spatial interpolation, at locations both proximate and distant to the focus of invasion at multiple points in time.

Food, water, and fault lines: Remote sensing opportunities for earthquake-response management of agricultural water.

Earthquakes often cause destructive and unpredictable changes that can affect local hydrology (e.g. groundwater elevation or reduction) and thus disrupt land uses and human activities. Prolific agricultural regions overlie seismically active areas, emphasizing the importance to improve our understanding and monitoring of hydrologic and agricultural systems following a seismic event. A thorough data collection is necessary for adequate post-earthquake crop management response; however, the large spatial extent of earthquake's impact makes challenging the collection of robust data sets for identifying locations and magnitude of these impacts. Observing hydrologic responses to earthquakes is not a novel concept, yet there is a lack of methods and tools for assessing earthquake's impacts upon the regional hydrology and agricultural systems. The objective of this paper is to describe how remote sensing imagery, methods and tools allow detecting crop responses and damage incurred after earthquakes because a change in the regional hydrology. Many remote sensing datasets are long archived with extensive coverage and with well-documented methods to assess plant-water relations. We thus connect remote sensing of plant water relations to its utility in agriculture using a post-earthquake agrohydrologic remote sensing (PEARS) framework; specifically in agro-hydrologic relationships associated with recent earthquake events that will lead to improved water management.

[Study on the Prediction of Cotton Yield within Field Scale with Time Series Hyperspectral Imagery].

Pixel-based processing method mainly extracts spectral information from hyperspectral remote sensing images, but site specific management zone (SSMZ) delineation and crop yield estimation with images need to take spatiotemporal heterogeneity into account. As the spatial resolution of remote sensing data increases, the so-called "salt-and-pepper" problem of pixel-based classification becomes more serious. The spatiotemporal heterogeneity of soil properties and crop biophysical parameters are mainly delineated with grid sampling and geostatistics interpolation, but the widely used method has some problems: time consuming and high cost. Satellite imageries are introduced to delineate SSMZ, but there are also problems needed to be resolved: (1) single date imagery is used to map SSMZ which is difficult to determine the optimal date for SSMZ delineation; (2) only few SSMZs were mapped, which limited application of site specific fertilizing and management; (3) pixel-based method for SSMZ delineation didn't concern the spatial relationship between pixels and site specific management does not implement at pixel level, but at SSMZ level. To improve the accuracy of crop yield estimation, a time-series of hyperspectral airborne images with high spatial resolution (1 m) of a cotton field, which is located in San Joaquin Valley, California US, were acquired and classified by using object-oriented segmentation, then yield predicting models were built, and the accuracy and stability of yield models were validated with determining coefficients R2 and the root mean square error (RMSE). Results are as follows: (1) object-oriented SSMZ delineating method combines spectral, spatial and temporal information, reduces noises in images and yield data, improves the accuracy of yield prediction; (2) for same SSMZ number, first derivative predicting model is more accurate; (3) for same spectral input, models with fewer SSMZs show higher accuracy, which is due to spatial errors of airborne images and yield data. The results will improve monitoring methods for crop growth and yield while accelerate the application of UAV remote sensing in precision agriculture.