Image Acquisition Dates Research Articles

Soil class and attribute dynamics and their relationship with natural vegetation based on satellite remote sensing

The development of a strategy to reach soil data through vegetation has the potential to improve soil mapping in large areas covered by forests. This study aimed to develop a strategy to identify the relationship between soil information, such as texture, fertility and pedological classes, through vegetation spectral sensing obtained by a satellite sensor. The study area is located in Angola, Africa, covering 20,000ha. Landsat images corresponding to dry and wet seasons were obtained, atmospheric corrected and transformed into reflectance. We built a mechanism to extract soil information and keep only vegetated areas in images. Afterward, the Normalized Difference Vegetation Index (NDVI) was calculated and its pixel was linked with soil samples collected in the field (450 points at four depths, A: 0–0.2; B: 0.4–0.6; C: 0.8–1.0; D: 1.0–1.2m), and the wet analysis was performed. The non-parametric tests were used to verify the NDVI variation according to soil classes and Landsat image acquisition date. Higher correlation values were obtained in the images from 2008, for Al saturation, Al content, and base saturation. Overall, soil attributes best related with vegetation were the ones directly or indirectly involved with Al. The best correlation occurred in the wet season, because vegetation expressed better its relation with soils and water availability. This relation did not vary much at depth. The higher Al content was associated with the development of vegetation adapted to this condition, i.e. a native savanna-type usually occurring in regions of dystrophic soils such as Ferralsols, which presented the highest NDVI mean and were statistically different from the other soil groups. Pedological classes drive water dynamics, which interferes on vegetation. Thus, we found an important relationship between NDVI and soil classes. Depending on the season, soil class varies on the NDVI projection, due to its morphological and attributes configuration. Thus, we conclude that only one soil attribute is not sufficient to drive the NDVI result, but it has to be combined with the soil class. Wet season is indicated as the best period to analyze vegetation in order to reach soil information. The results indicate an important strategy to assist digital soil mapping in vegetated areas.

Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery

This paper presents a novel method of multi-temporal land cover classification based on the polarization signature (PS). Additionally, the temporal change of the scattering mechanism of crops and other land cover classes is analyzed based on the PS. Firstly, the scattering mechanisms of corn, soybean, wheat, forest and urban over time are analyzed. The correlation coefficient is computed between their PS and the PS of canonical targets to study their relationships with the completely polarized surface, dihedral, dipole and helix scattering targets. The pedestal height (PH) of their PS is also calculated to investigate their un-polarized components such as multiple scattering. Then, a novel multi-temporal supervised binary-tree classification scheme (MTSBTCS) with a criterion that maximizes the power difference of PS (MDPS) is developed (MTSBTCS-MDPS), in which the MDPS is proposed to find the optimum orientation angle, ellipticity angle and image acquisition date to maximize the power difference of two different land cover classes and a binary-tree classifier is constructed for the final multi-temporal classification. Seven fully polarimetric C-band RADARASAT-2 images in Fine Quad (FQ) 21 mode acquired in southwestern Ontario, Canada during the entire growing season from May to September in 2012 are used for the purpose of the scattering mechanism analysis and validation of the MTSBTCS-MDPS. The experiment demonstrates that, the scattering mechanism varies with crop type and also changes greatly for each crop when they are in different growing stages. However, the scattering mechanisms of urban and forest areas remain almost the same. In terms of the classification, comparison of the MDPS with the traditional support vector machine (SVM), random forest (RF), and the Wishart distance (WD), shows that for the single-date image classification, both MDPS and WD achieve much higher accuracy on each date than that of the SVM and RF, but the MDPS consumes more time than that of the WD and its accuracy is also lower. However, when they are applied to the multi-temporal images, both SVM and RF suffer a severe misclassification between forest and urban with their accuracy lower than that of the MTSBTCS-MDPS. In addition, the MTSBTCS-MDPS developed in this paper is much less time-consuming approximately 2.5 times less than that of the MTSBTCS-WD, meanwhile, it also achieves the highest accuracy with its overall accuracy of 87.5% and kappa coefficient of 0.85 among all methods applied.

Sensitivity of Pansharpening Methods to Temporal and Instrumental Changes Between Multispectral and Panchromatic Data Sets

In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred.

The utilization of Depth Invariant Index and Principle Component Analysis for mapping seagrass ecosystem of Kotok Island and Karang Bongkok, Indonesia

Seagrass perform a variety of functions within ecosystems, and have both economic and ecological values, therefore it has to be kept sustainable. One of the stages to preserve seagrass ecosystems is monitoring by utilizing thespatial data accurately. The purpose of the study was to assess and compare the accuracy of DII and PCA transformationsfor mapping of seagrass ecosystems. Fieldstudy was carried out in Karang Bongkok and Kotok Island waters, in Agustus 2014 and in March 2015. A WorldView-2 image acquisition date of 5 October 2013 was used in the study. The transformations for image processing data were Depth Invariant Index (DII) and Principle Component Analysis (PCA) using Support Vector Machine (SVM) classification. The result shows that benthic habitat mapping of Karang Bongkok using DII and PCA transformations were 72%and 81% overall’s accuracy respectively, whereas of Kotok Island were 83% and 84% overall’s accuracy respectively. There were seven benthic habitat types found in karang Bongkok waters and in Kotok Island namely seagrass, sand, rubble, coral, logoon, sand mix seagrass, and sand mix rubble. PCA transformation was effectively to improve mapping accuracy of sea grass mapping in Kotok Island and Karang Bongkok.

Updating the Grassland Vegetation Inventory Using Change Vector Analysis and Functionally-Based Vegetation Indices

The Grassland Vegetation Inventory (GVI), which represents a comprehensive biophysical, anthropogenic, and land-use inventory of grasslands in Alberta, is widely used as a baseline for grassland conditions. An up-to-date GVI is essential for understanding grassland changes and for planning management or conservation actions on grasslands. In this study, a hybrid change detection method is proposed that incorporates change vector analysis and a set of vegetation indices (VIs) measuring different vegetation attributes for mapping the conversion of native grassland to cultivated agriculture, and ultimately to update the GVI based on multiseasonal and multiyear Landsat images. Vegetation indices that contribute significantly to differentiation between existing native grassland and land recently converted from native grassland to cultivated cropland were identified by using stepwise regression analyses and were used as inputs for mapping the conversion between 2006 and 2011 or 2015. The results showed that land conversion can be detected using a single image acquired during the growing season, but that the accuracy of identification is affected by the date of image collection and the nature of the VIs used. The greatest accuracy in detecting land conversion between 2006 and 2011 was achieved using the difference in VI between years (dVI) for the Shortwave Infrared Reflectance 3/2 Ratio (SWIR32) and the Enhanced Vegetation Difference Index (EVI) derived from July imagery (accuracy = 95.2 %; Kappa = 0.86). The same combination of SWIR32 and EVI was also effective, although with lower accuracy (accuracy = 86.0 %; Kappa = 0.64) when tested on a larger geographical area and for detecting land use change between 2006 and 2015. The method proposed here could be applied to detect the land cover conversion in other grassland regions, although the optimal VIs and image acquisition date may need to be modified depending on the type of land use activities implemented in each region.

Mapping Prosopis juliflora in West Somaliland with Landsat 8 Satellite Imagery and Ground Information

AbstractProsopis juliflora is a drought‐tolerant fast‐growing tree species originating from South and Central America with a high invasion potential in arid and semi‐arid areas in Africa. It was introduced in Somaliland in the 1980s and is reported to have spread vigorously since. Despite being recognized as a serious issue in the country, the actual scale of the problem is unknown. In this study, we mapped the species in a study area that includes the capital, Hargeisa, using Landsat 8 satellite imagery. During a field campaign in 2015, we collected canopy‐level spectral signatures of P. juliflora and native trees to analyse the potential use of spectral data in discriminating the invasive species. P. juliflora was found to be generally distinguishable because of its greater vigour during the dry season. We tested the accuracy of the random forest classifier and different classification set‐ups, varying the spatial resolution (original 30 m vs pan‐sharpened 15 m) and image acquisition dates (during the wet season, the dry season and a combination of the two). Best overall accuracy (84%) was achieved by using pan‐sharpened data from the two seasons. About 30 years since its introduction, the invasive species was detected in 9% of the total investigated area with highest occurrence in the proximity of human settlements and along seasonal water courses. © 2016 The Authors. Land Degradation and Development published by John Wiley & Sons, Ltd.

Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR

This paper examines the value of very high-resolution multispectral satellite imagery and LiDAR-derived digital elevation information for classifying estuarine vegetation types. Satellite images used are from the WorldView-2, RapidEye, and SPOT-6 sensors in 2m and 5m resolution, respectively, acquired between 2010 and 2014. Ground truthing reference is a GIS-derived vegetation map based on field data from 2008. Supervised maximum likelihood classification produced satisfactory overall accuracies between 64.3% and 77.9% for the SPOT-6 and the WorldView-2 image, respectively, while the RapidEye-based classifications produced overall accuracies between 55.0% and 66.8%. The reasons for the misclassifications are mainly based on the highly dynamic environmental conditions causing discrepancies between the field data and satellite acquisition dates rather than technical issues. Dynamics in water levels and salinity caused rapid change in vegetation communities. Further, weather impacts such as floods and wind events caused water turbidity and led to bias in the reflective properties of the satellite images and thus misclassifications. These results show, however, that the spatial and spectral resolution of modern very high-resolution imagery is sufficient to satisfactory map estuarine vegetation and to monitor small-scale change. They emphasise, however, the importance of synchronisation of ground truthing data with actual image acquisition dates in these highly dynamic environments in order to achieve high classification accuracies. The results also highlight the importance of ancillary data for accurate interpretation of observed classification discrepancies and vegetation dynamics.

Water residence time affecting phytoplankton blooms: study case in Ibitinga Reservoir (São Paulo, Brazil) using Landsat/TM images.

Satellite images are an effective tool for the detection of phytoplankton blooms, since they cause striking changes in water color. Bloom intensity can be expressed in terms of chlorophyll-a concentration. Previous studies suggest the use of Landsat TM4/TM3 reflectance ratio to retrieve surface chlorophyll-a concentration from aquatic systems. In this study we assumed that a remote sensing trophic state index can be applied to investigate how changes in HRT along the hydrologic year affect the spatial distribution of the phytoplankton blooms at Ibitinga's reservoir surface. For that, we formulated two objectives: (1) apply a semi-empirical model which uses this reflectance ratio to map chlorophyll-a concentration at Ibitinga reservoir along the 2005 hydrologic year and (2) assess how changes in hydraulic residence time (HRT) affect the spatial distribution of phytoplankton blooms at Ibitinga Reservoir. The study site was chosen because previous studies reported seasonal changes in the reservoir limnology which might be related to the reservoir seasonality and hydrodynamics. Six Landsat/TM images were acquired over Ibitinga reservoir during 2005 and water flow measurements provided by the Brazilian Electric System National Operator - ONS were used to compute the reservoir´s residence time, which varied from 5.37 to 52.39 days during 2005. The HRT in the date of image acquisition was then compared to the distribution of chlorophyll-a in the reservoir. The results showed that the HRT increasing implies the increasing of the reservoir surface occupied by phytoplankton blooms.

An investigation into the factors influencing the detectability of oil spills using spectral indices in an oil-polluted environment

ABSTRACTThe aim of this article is to investigate and test the influence of oil spill volume and time gap (number of days between oil spill events and image acquisition date) on normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). This was carried out to determine the effect of these factors on vegetation condition affected by the oil spill. Based on regression analysis, it was shown that increase in the volume of oil spill resulted in increased deterioration of vegetation condition (estimated using NDVI and NDWI) in the study site. The study also tested how the length of time gap between the oil spill and image acquisition date influences the detectability of impacts of oil spill on vegetation. The results showed that the length of time between image acquisition and oil spill influenced the detectability of impacts of oil spill on vegetation condition. The longer the time between the date of image acquisition and the oil spill event, the lower the detectability of impacts of oil spill on vegetation condition. The NDVI seemed to produce better results than the NDWI. In conclusion, time and volume of oil spill can be important factors influencing the detection of pollution using vegetation indices (VIs) in an oil-polluted environment.

Detection of Drought-Induced Hickory Disturbances in Western Lin An County, China, Using Multitemporal Landsat Imagery

Hickory plantations play an important role in improving local farmers’ economic conditions, but extreme drought in July–August 2013 seriously influenced hickory nut production. It is necessary to understand the extent and magnitude of this drought-induced hickory disturbance through mapping its spatial distribution using remote sensing data. This paper proposes a new approach to examine hickory disturbance based on multitemporal Landsat imagery. Ratios of green vegetation to soil fractions were calculated, in which the green vegetation and soil fractions were extracted from Landsat multispectral imagery using the linear spectral mixture analysis approach. We used the differences between before-drought and after-drought ratios to detect hickory disturbances. Four disturbance levels—non-disturbance, light, medium, and severe—were grouped according to the field survey data. The spatial distribution of these four levels was developed using the ratio-based approach. The result indicates that this approach is effective to detect drought-induced hickory disturbance and may be transferred to detect other kinds of disturbances, such as forest disease and selective logging. Cautions should be taken to properly select image acquisition dates and the change detection period, in addition to the approach itself.

Temporal dependence of burn severity assessment in Siberian larch (Larix sibirica) forest of northern Mongolia using remotely sensed data

Assessing burn severity is critical for understanding both the short- and long-term effects of fire disturbance on forest ecosystems. This study proposed a methodology to reconstruct burn severity from the Landsat imagery at different time lags after a fire (≤18 years) in Siberian larch (Larix sibirica) forest. The estimated accuracy of the burn severity models we developed indicated strong effects of forest recovery, image acquisition date and remote sensing predictors on the burn severity assessment. In the first several years after the fire, the dNBR (differenced Normalized Burn Ratio) was the most important remotely sensed index for assessing burn severity, followed by the dNDMI (differenced Normalized Difference Moisture Index) and dNDVI (differenced Normalized Difference Vegetation Index). However, the dNDMI was more important than the dNBR and dNDVI in explaining burn severity when larch forest regrowth dominated. The overall accuracy of the classification and regression tree models showed a decrease in accuracy from 83% to 62% depending on the lag times of burn severity assessment. The high severity class had the lowest omission and commission errors, followed by the low and moderate classes among lag times. Our evaluation of model transferability and thresholds of burn severity index demonstrates the advantage of the proposed methodology for rapid assessment of fire effects in boreal larch forest that will assist in understanding the complex relationships among forest fires and ecological processes in Eurasian boreal ecosystems.

Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment

Digital aerial photogrammetry (DAP) is emerging as an alternate data source to airborne laser scanning (ALS) data for three-dimensional characterization of forest structure. In this study we compare point cloud metrics and plot-level model estimates derived from ALS data and an image-based point cloud generated using semi-global matching (SGM) for a complex, coastal forest in western Canada. Plot-level estimates of Lorey’s mean height (H), basal area (G), and gross volume (V) were modelled using an area-based approach. Metrics and model outcomes were evaluated across a series of strata defined by slope and canopy cover, as well as by image acquisition date. We found statistically significant differences between ALS and SGM metrics for all strata for five of the eight metrics we used for model development. We also found that the similarity between metrics from the two data sources generally increased with increasing canopy cover, particularly for upper canopy metrics, whereas trends across slope classes were less consistent. Model outcomes from ALS and SGM were comparable. We found the greatest difference in model outcomes was for H (ΔRMSE% = 5.04%). By comparison, ΔRMSE% was 2.33% for G and 3.63% for V. We did not discern any corresponding trends in model outcomes across slope and canopy cover strata, or associated with different image acquisition dates.

Ancient Stone Tidal Weirs in Penghu Archipelago: Distribution, Category, Structure and Function, a Google Earth and GIS Approach

Abstract. The aim of this study was to give a comprehensive archaeological investigation for Penghu’s stone tidal weirs (STWs) based on both Google Earth and GIS. Firstly, this study uses GoogleEarth Pro tools to clip a GeoEye-1 image (acquisition date: 22/07/2013) and a WorldView-2 image (acquisition date: 25/01/2014) for Chipei Island and Husi Island, respectively, and save them at a “premium resolution” of 4800 dpi. More, using 15 m panchromatic orthorectified Landsat images as a base, two clips were geo-referenced in ENVI 5.1 with minimal root mean square error. Furthermore, the STWs were manual extracted from the two GoogleEarth images in ArcGIS 10.1. Category and size statistics are presented; construction structure and weir function are discussed. Lastly, by using GIS analyses, STWs characteristics of intertidal flats across Penghu archipelago have been mapped and related to key geographical environmental variables. From spring to summer of 2015 our research team conducted investigations into Penghu’s STWs based on different seasons and time periods of GoogleEarth historic images. Our results showed that, distributed amongst Penghu’s coastline, there are 503 STWs. Compared with the official survey results (around 592 STWs), the counts are similar but the GoogleEarth-based method is more time-saving and efficient.

Estimating woody above-ground biomass in an arid zone of central Australia using Landsat imagery

Woody cover was found to be an easily measurable and significant variable for estimating woody above-ground biomass (AGB) based on in situ measurements in an arid zone of central Australia. In addition, the potential of woody cover to estimate woody AGB based on Landsat imagery was further tested. Linear spectral mixture analysis (LSMA) using multitemporal endmembers was employed to estimate the woody cover for Landsat imagery and the LSMA method was tested for different Landsat images under various drought conditions. The results show that the accuracy of the woody cover estimation increased as the accumulated rainfall prior to the image acquisition date decreased. Woody AGB across the study area was finally retrieved using the combination of plot-level woody AGB model and woody cover derived from dry-period Landsat images closest to the acquisition time of the field data, with an root mean square error of 0.798 t/ha . It is concluded that woody cover is able to replace tree basal area to estimate woody AGB at plot-level scale, and it is much more suitable for estimating woody AGB than the normalized difference vegetation index-AGB model for this arid zone covered with low-cover shrubs; however, woody AGB estimated using the proposed method is underestimated for some areas.

A scalable satellite-based crop yield mapper

New advances in satellite data acquisition and processing offer promise for monitoring agricultural lands globally. Using these data to estimate crop yields for individual fields would benefit both crop management and scientific research, especially for areas where reliable ground-based estimates are not currently made. Here we introduce a generalized approach for mapping crop yields with satellite data and test its predictions for yields across more than 17,000 maize fields and 11,000 soybean fields spanning multiple states and years in the Midwestern United States. The method, termed SCYM (a scalable satellite-based crop yield mapper), uses crop model simulations to train statistical models for different combinations of possible image acquisition dates, and these are then applied to Landsat and gridded weather data within the Google Earth Engine platform, where the Landsat is composited to find the “best” dates of observations on a pixel-by-pixel basis. SCYM estimates successfully captured a significant fraction of maize yield variation in all state-years, with a range of 14–58% and an average of 35% for this particular study region and crop. Similar results were observed for soybean, with an average of 32% of yield variation captured. The multi-year yield estimates were also used to examine the temporal persistence of yield advantages for the top yielding fields in different counties, which is one measure of how important factors such as farmer skill are in explaining yield gaps. The strength of the SCYM approach lies in its ability to leverage physiological knowledge embedded in crop models to interpret satellite observations in a scalable way, as it can be readily applied to new crops, regions, and types and timing of remote sensing observations without the need for ground calibration.

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km2 of inland water globally – nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal ‘inland water’ biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds – effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org).

Remote Sensing Image Fusion at the Segment Level Using a Spatially-Weighted Approach: Applications for Land Cover Spectral Analysis and Mapping

Segment-level image fusion involves segmenting a higher spatial resolution (HSR) image to derive boundaries of land cover objects, and then extracting additional descriptors of image segments (polygons) from a lower spatial resolution (LSR) image. In past research, an unweighted segment-level fusion (USF) approach, which extracts information from a resampled LSR image, resulted in more accurate land cover classification than the use of HSR imagery alone. However, simply fusing the LSR image with segment polygons may lead to significant errors due to the high level of noise in pixels along the segment boundaries (i.e., pixels containing multiple land cover types). To mitigate this, a spatially-weighted segment-level fusion (SWSF) method was proposed for extracting descriptors (mean spectral values) of segments from LSR images. SWSF reduces the weights of LSR pixels located on or near segment boundaries to reduce errors in the fusion process. Compared to the USF approach, SWSF extracted more accurate spectral properties of land cover objects when the ratio of the LSR image resolution to the HSR image resolution was greater than 2:1, and SWSF was also shown to increase classification accuracy. SWSF can be used to fuse any type of imagery at the segment level since it is insensitive to spectral differences between the LSR and HSR images (e.g., different spectral ranges of the images or different image acquisition dates).

Use of Landsat images for yield evaluation within a small plot

Many factors can influence crop yield. One of the most important factors is topography, which can play a crucial role especially in dry years. Plant variability can be monitored by many methods. This paper evaluates the suitability of vegetation indices derived from satellite Landsat 5 TM data in comparison with yield, curvature and topography wetness index over a relatively small field (11.5 ha). Imageries were chosen from the years 2006 and 2010, when oat was grown and from 2005 and 2011, when winter wheat was grown. These images were taken in June in the same growth stage for every crop. It was confirmed that derived indices from Landsat images can be used for comparison with yield and selected topographic attributes and it can explain yield variability, which can be influenced by water distribution during growth stages. Correlation coefficient between moisture stress index and winter wheat yield was &amp;ndash;0.816 in the image acquisition date of 4. 6. 2011.

Evaluating an Intra-Annual Time Series for Grassland Classification—How Many Acquisitions and What Seasonal Origin Are Optimal?

The amount of images used in multitemporal classification studies has greatly increased along with enhanced temporal sensor capacities. Handling large intra-annual time series leads to the question of how the selection of image acquisition dates could be optimized. In this study, an empirical approach for evaluating the relative classification power of single acquisition dates is introduced for the differentiation of seminatural grassland vegetation. The main question is how many acquisitions from which phenological origins are preferable to achieve a certain classification accuracy target. The tested time series contains 24 single RapidEye scenes from 2009 to 2011. The vegetation index composites of these images were iteratively classified into different combinations of acquisition dates using the support vector machine (SVM) algorithm. The subsequent results were tested for significant accuracy improvements over single acquisition dates. These acquisition dates are subsumed under phenological seasons to evaluate adequate temporal acquisition windows. The results show that a three-scene composite reaches more than 0.8 overall accuracy (OAA). The best tradeoff amount between number of acquisition dates and classification accuracy is achieved using a seven-scene NDVI composite. The most important season for the differentiation of seminatural grassland is early summer (ESu). Full spring (FuS), late summer (LSu), and midsummer (MSu) can also be identified as influential temporal windows for data acquisition.

Factors affecting the measurement of CDOM by remote sensing of optically complex inland waters

A combination of new measurements and analysis of historical data from several geographic regions was used to address four issues that affect the reliability and interpretation of colored dissolved organic matter (CDOM) measured by remote sensing of inland waters. First, high variability of CDOM levels in lakes and rivers was found at seasonal and multi-year time scales and at shorter intervals in some rivers and lakes. Coefficients of variation (CVs) of 30%–50% for absorptivity at 440nm (a440) were common in historical and new data sets we examined. CDOM values used to calibrate imagery thus should be measured close to the image acquisition date, preferably within 1–2 months in lakes and a few days in large rivers, unless it can be shown that CDOM levels are temporally stable over longer (or shorter) time periods in a given aquatic system. Second, spectral slopes (S) for CDOM in the visible range vary little over time (even over multi-year periods) within sites. Substantial variation was found between sites, however, and most spectra showed a change in slope near 460nm. Values of S400–460 for waters with moderate to high CDOM levels generally were within a narrow range (~0.014–0.018) and similar to reported S values in the near UV. Values of S400–460 for waters with low CDOM generally were smaller and more variable, as were values for S460–650 for all waters. Overall, the variability of spectral slopes in the visible range should not have a large effect on the reliability of a440 estimates made from remote sensing, which in many models involve reflectance measurements at wavelengths>500nm. Third, although a strong correlation (r2=0.925) was found between CDOM levels and DOC concentrations in 34 surface waters sampled in 2013, the standard error of estimate suggests an uncertainty of~±20% in predicting DOC at a440=5m−1 (a moderate CDOM level). Moreover, CDOM–DOC relationships for unpublished data sets we analyzed and those reported in the literature indicate that both the fraction of DOC that is colored and slopes of regressions between CDOM and DOC are highly variable in space and time. Prediction of DOC concentrations in water bodies from CDOM levels (whether measured in the laboratory or by remote sensing) thus is associated with considerable uncertainty. For the present, this implies that field sampling is required to verify DOC concentrations predicted from remotely sensed CDOM measurements until we have a better understanding of variations in DOC–CDOM relationships. Fourth, shapes of reflectance spectra for CDOM-rich waters varied greatly depending on the concentrations of other constituents (suspended solids and chlorophyll) that affect the optical properties of water. Nonetheless, it is not obvious from our results for several predictive models that different remote sensing algorithms are needed to calculate CDOM levels accurately for waters where CDOM is the only variable affecting reflectance versus waters where other constituents also affect the spectra. The best band or band ratio models for simulated Landsat 8, Sentinel-2 and Sentinel-3 bands from field measured reflectance spectra yielded high r2 values (0.84–0.86) for a440. The broader Landsat 8 bands worked nearly as well for a440 as the narrower Sentinel band sets and hyperspectral bands, probably because CDOM is characterized by a broad exponential increase in absorbance with decreasing wavelength rather than specific peaks or troughs in absorbance or reflectance.