Special Issue "Land Surface Phenology and Seasonality: Novel Approaches and Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (28 February 2017).

Special Issue Editors

Dr. Geoffrey M. Henebry
Website
Guest Editor
Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007-3510, USA
Interests: land surface phenology; ecological remote sensing; grasslands; croplands; urban areas; land cover/land use change
Dr. Forrest M. Hoffman
Website
Guest Editor
Climate Change Science Institute, Computational Earth Sciences Group, Oak Ridge National Laboratory Oak Ridge, TN 37831-6301, USA
Interests: vegetation and soil dynamics; global Earth system modeling; global carbon cycle modeling; terrestrial and marine biogeochemistry; model benchmarking and model–data integration; high performance computational science; large scale Earth system data analytics and machine learning; remote sensing; ecological modeling
Dr. Jitendra Kumar
Website
Guest Editor
Climate Change Science Institute, Environmental Sciences Division, Oak Ridge National Laboratory Oak Ridge, TN 37831-6301, USA
Interests: land surface phenology; remote sensing; geospatial analysis; landscape ecology; subsurface flow and reactive transport modeling
Dr. Xiaoyang Zhang
Website
Guest Editor
Geospatial Sciences Center, South Dakota State University, Brookings, SD 57007, USA
Interests: biomass burning emissions; burned area; fire seasonality; climate change; real-time monitoring; remote sensing
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Special Issue Information

Dear Colleagues,

The rapid pace of land surface phenology (LSP) monitoring and modeling has positioned the field to make significant advances in the coming decade. The primary lesson to be learned from the past 15 years of LSP research is that there is no single approach to LSP that fits all situations. The community is starting to explore approaches to LSP monitoring and modeling that embrace suites of sensors and algorithms toward developing biome-tuned LSP models. Land surface seasonality (LSS) is a recent concept that could be used in tandem with LSPs to tackle biome-specific monitoring and modeling. For example, the seasonality of soil freeze/thaw is a key transition in ecosystem processes and one that can be monitored effectively using microwave frequencies with passive and active sensors.

Although most of the LSP literature has focused on green-up dynamics, it is necessary to move beyond a focus on spring. Recent work on the dynamics of autumnal senescence has demonstrated some novel approaches, but there is much more to explore in monitoring and modeling the processes of canopy coloring, nutrient retranslocation, drying, and foliage abscission.

Much of the LSP literature has focused on optical imagery and on very few vegetation indices. It is time to explore the possibilities of incorporating multiple remote sensing modalities beyond the visible to near infrared end of the spectrum.

Many LSP studies have focused on natural landscapes and ecosystems, but we should also leverage our understanding of human-managed systems, whether in croplands or urbanized areas, to advance LSP monitoring and modeling.

Very little research has been done to date on the influence of LSPs and LSSs on the spatial structure of surface characteristics and vice versa. A few field studies have shown how the spatial pattern of reflectance changes during the growing season. Spatial analyses of image time series have revealed characteristic seasonal patterns in reflectance, emittance, and backscattering that can enable the detection and evaluation of change. With the increased accessibility of the Landsat archive, this avenue of LSP research could be very fruitful area in the coming decade.

Cross-calibration of LSP metrics with other indicators of phenology has been studied since the launch of ERTS (Landsat-1) in 1972. More recent efforts to cross-calibrate estimates of phenophases have found a tendency for LSP timings to be early relative to a suite of bioclimatic. Which sorts of data constitute appropriate reference sets for ground-level phenological observations remains an open question with multiple regional solutions tuned to specific vegetation assemblages the most likely answer. However, it is clear that the community needs coordinated observations across multiple scales to link landscape heterogeneity to pixel variability. The use of flux tower observations and "phenocams" for cross-calibration are critical, but there is another source of finer spatial resolution remote sensing data that promises a rich source for cross-calibration efforts, viz., the global Landsat data record.

Validation of land surface products is the proverbial "elephant in the room". Note that we say land surface products and not land surface phenology products. The challenge facing the remote sensing community is larger than validation of just LSPs. The Land Product Validation Subgroup (LPVS) of the Committee on Earth Observation Systems (CEOS) Working Group on Calibration and Validation (WGCV) has been active in a number of areas (http://lpvs.gsfc.nasa.gov), including land surface phenology (http://lpvs.gsfc.nasa.gov/pheno_home.html). Despite an effort to self-organize, progress in bringing the LSP community together to engage in validation exercises has been slow, compared to what has been accomplished for leaf area index (LAI) retrievals. This situation is due, in large part, to a lack of funding for a validation campaign, but it is also attributable to (a) the relative scale-invariance of intensive variables like vegetation indices, (b) the sensitivity of vegetation indices to sensor band centers and bandwidths, (c) the lack of sharply defined phenometrics, and (d) the various ways to generate phenometrics from image time series.

We invite you to submit articles concerning your recent research in modeling and/or measuring and monitoring land surface phenologies and seasonalities with respect to the following topics:

  • Beyond NDVI and EVI: using narrowband spectral indices to capture phenophase transitions
  • Beyond VNIR: using longwave sensors (active and passive) to capture phenophase transitions
  • LSPs from solar induced fluorescence (SIF)
  • LSPs in croplands
  • LSPs in grasslands, savannas, and shrublands
  • LSPs in tropical ecosystems, including croplands
  • LSPs in and around cities
  • LSPs in mountain ecosystems
  • LSPs at high latitudes
  • LSPs in arid ecosystems
  • LSPs in lotic, lentic, estuarine, and marine ecosystems
  • LSPs and spatial and spatio-temporal patterning
  • Cross-calibration of LSPs
  • Validation of LSPs

Authors are required to check and follow the specific Instructions to Authors, https://www.mdpi.com/journal/remotesensing/instructions.

Dr. Geoffrey M. Henebry
Dr. Forrest M. Hoffman
Dr. Jitendra Kumar
Dr. Xiaoyang Zhang
Guest Editors

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Published Papers (14 papers)

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Research

Open AccessArticle
Response of Land Surface Phenology to Variation in Tree Cover during Green-Up and Senescence Periods in the Semi-Arid Savanna of Southern Africa
Remote Sens. 2017, 9(7), 689; https://doi.org/10.3390/rs9070689 - 04 Jul 2017
Cited by 7
Abstract
Understanding the spatio-temporal dynamics of land surface phenology is important to understanding changes in landscape ecological processes of semi-arid savannas in Southern Africa. The aim of the study was to determine the influence of variation in tree cover percentage on land surface [...] Read more.
Understanding the spatio-temporal dynamics of land surface phenology is important to understanding changes in landscape ecological processes of semi-arid savannas in Southern Africa. The aim of the study was to determine the influence of variation in tree cover percentage on land surface phenological response in the semi-arid savanna of Southern Africa. Various land surface phenological metrics for the green-up and senescing periods of the vegetation were retrieved from leaf index area (LAI) seasonal time series (2001 to 2015) maps for a study region in South Africa. Tree cover (%) data for 100 randomly selected polygons grouped into three tree cover classes, low (<20%, n = 44), medium (20–40%, n = 22) and high (>40%, n = 34), were used to determine the influence of varying tree cover (%) on the phenological metrics by means of the t-test. The differences in the means between tree cover classes were statistically significant (t-test p < 0.05) for the senescence period metrics but not for the green-up period metrics. The categorical data results were supported by regression results involving tree cover and the various phenological metrics, where tree cover (%) explained 40% of the variance in day of the year at end of growing season compared to 3% for the start of the growing season. An analysis of the impact of rainfall on the land surface phenological metrics showed that rainfall influences the green-up period metrics but not the senescence period metrics. Quantifying the contribution of tree cover to the day of the year at end of growing season could be important in the assessment of the spatial variability of a savanna ecological process such as the risk of fire spread with time. Full article
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Open AccessFeature PaperArticle
Comparing Passive Microwave with Visible-To-Near-Infrared Phenometrics in Croplands of Northern Eurasia
Remote Sens. 2017, 9(6), 613; https://doi.org/10.3390/rs9060613 - 15 Jun 2017
Cited by 6
Abstract
Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and [...] Read more.
Planting and harvesting times drive cropland phenology. There are few datasets that derive explicit phenological metrics, and these datasets use the visible to near infrared (VNIR) spectrum. Many different methods have been used to derive phenometrics such as Start of Season (SOS) and End of Season (EOS), leading to differing results. This discrepancy is partly due to spatial and temporal compositing of the VNIR satellite data to minimize data gaps resulting from cloud cover, atmospheric aerosols, and solar illumination constraints. Phenometrics derived from the downward Convex Quadratic model (CxQ) include Peak Height (PH) and Thermal Time to Peak (TTP), which are more consistent than SOS and EOS because they are minimally affected by snow and frost and other non-vegetation related issues. Here, we have determined PH using the vegetation optical depth (VOD) in three microwave frequencies (6.925, 10.65 and 18.7 GHz) and accumulated growing degree-days derived from AMSR-E (Advanced Microwave Scanning Radiometer on EOS) data at a spatial resolution of 25 km. We focus on 50 AMSR-E cropland pixels in the major grain production areas of Northern Eurasia (Ukraine, southwestern Russia, and northern Kazakhstan) for 2003–2010. We compared the land surface phenologies of AMSR-E VOD and MODIS NDVI data. VOD time series tracked cropland seasonal dynamics similar to that recorded by the NDVI. The coefficients of determination for the CxQ model fit of the NDVI data were high for all sites (0.78 < R2 < 0.99). The 10.65 GHz VOD (VOD1065GHz) achieved the best linear regression fit (R2 = 0.84) with lowest standard error (SEE = 0.128); it is therefore recommended for microwave VOD studies of cropland land surface phenology. Based on an Analysis of Covariance (ANCOVA) analysis, the slopes from the linear regression fit were not significantly different by microwave frequency, whereas the intercepts were significantly different, given the different magnitudes of the VODs. PHs for NDVI and VOD were highly correlated. Despite their strong correspondence, there was generally a lag of AMSR-E PH VOD10.65GHz by about two weeks compared to MODIS peak greenness. To evaluate the utility of the PH determination based on maximum value, we correlated the CxQ derived and maximum value determined PHs of NDVI and found that they were highly correlated with R2 of 0.87, but with a one-week bias. Considering the one-week bias between the two methods, we find that PH of VOD10.65GHz lags PH of NDVI by three weeks. We conclude, therefore, that maximum-value based PH of VOD can be a complementary phenometric for the CxQ model derived PH NDVI, especially in cloud and aerosol obscured regions of the world. Full article
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Open AccessArticle
Impacts of Thermal Time on Land Surface Phenology in Urban Areas
Remote Sens. 2017, 9(5), 499; https://doi.org/10.3390/rs9050499 - 18 May 2017
Cited by 13
Abstract
Urban areas alter local atmospheric conditions by modifying surface albedo and consequently the surface radiation and energy balances, releasing waste heat from anthropogenic uses, and increasing atmospheric aerosols, all of which combine to increase temperatures in cities, especially overnight, compared with surrounding rural [...] Read more.
Urban areas alter local atmospheric conditions by modifying surface albedo and consequently the surface radiation and energy balances, releasing waste heat from anthropogenic uses, and increasing atmospheric aerosols, all of which combine to increase temperatures in cities, especially overnight, compared with surrounding rural areas, resulting in a phenomenon called the “urban heat island” effect. Recent rapid urbanization of the planet has generated calls for remote sensing research related to the impacts of urban areas and urbanization on the natural environment. Spatially extensive, high spatial resolution data products are needed to capture phenological patterns in regions with heterogeneous land cover and external drivers such as cities, which are comprised of a mixture of land cover/land uses and experience microclimatic influences. Here we use the 30 m normalized difference vegetation index (NDVI) product from the Web-Enabled Landsat Data (WELD) project to analyze the impacts of urban areas and their surface heat islands on the seasonal development of the vegetated land surface along an urban–rural gradient for 19 cities located in the Upper Midwest of the United States. We fit NDVI observations from 2003–2012 as a quadratic function of thermal time as accumulated growing degree-days (AGDD) calculated from the Moderate-resolution Imaging Spectroradiometer (MODIS) 1 km land surface temperature product to model decadal land surface phenology metrics at 30 m spatial resolution. In general, duration of growing season (measured in AGDD) in green core areas is equivalent to duration of growing season in urban extent areas, but significantly longer than duration of growing season in areas outside of the urban extent. We found an exponential relationship in the difference of duration of growing season between urban and surrounding rural areas as a function of distance from urban core areas for perennial vegetation, with an average magnitude of 669 AGDD (base 0 °C) and the influence of urban areas extending greater than 11 km from urban core areas. At the regional scale, relative change in duration of growing season does not appear to be significantly related to total area of urban extent, population, or latitude. The distance and magnitude that urban areas exert influence on vegetation in and near cities is relatively uniform. Full article
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Open AccessArticle
Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone
Remote Sens. 2017, 9(5), 485; https://doi.org/10.3390/rs9050485 - 16 May 2017
Cited by 21
Abstract
Satellite remote sensing of plant phenology provides an important indicator of climate change. However, start of the growing season (SOS) estimates in Northern Hemisphere boreal forest areas are known to be challenged by the presence of seasonal snow cover and limited seasonality in [...] Read more.
Satellite remote sensing of plant phenology provides an important indicator of climate change. However, start of the growing season (SOS) estimates in Northern Hemisphere boreal forest areas are known to be challenged by the presence of seasonal snow cover and limited seasonality in the greenness signal for evergreen needleleaf forests, which can both bias and impede trend estimates of SOS. The newly developed Plant Phenology Index (PPI) was specifically designed to overcome both problems. Here we use Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000–2014) to analyze the ability of PPI for estimating start of season (SOS) in boreal regions of the Northern Hemisphere, in comparison to two other widely applied indices for SOS retrieval: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). Satellite-based SOS is evaluated against gross primary production (GPP)-retrieved SOS derived from a network of flux tower observations in boreal areas (a total of 81 site-years analyzed). Spatiotemporal relationships between SOS derived from PPI, EVI and NDVI are furthermore studied for different boreal land cover types and regions. The overall correlation between SOS derived from VIs and ground measurements was rather low, but PPI performed significantly better (r = 0.50, p < 0.01) than EVI and NDVI which both showed a very poor correlation (r = 0.11, p = 0. 16 and r = 0.08, p = 0.24). PPI, EVI and NDVI overall produce similar trends in SOS for the Northern Hemisphere showing an advance in SOS towards earlier dates (0.28, 0.23 and 0.26 days/year), but a pronounced difference in trend estimates between PPI and EVI/NDVI is observed for different land cover types. Deciduous needleleaf forest is characterized by the largest advance in SOS when considering all indices, yet PPI showed less dramatic changes as compared to EVI/NDVI (0.47 days/year as compared to 0.62 and 0.74). PPI SOS trends were found to be higher for deciduous broadleaf forests and savannas (0.54 and 0.56 days/year). Taken together, the findings of this study suggest improved performance of PPI over NDVI and EVI in retrieval of SOS in boreal regions and precautions must be taken when interpreting spatio-temporal patterns of SOS from the latter two indices. Full article
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Open AccessArticle
Characterizing Land Cover Impacts on the Responses of Land Surface Phenology to the Rainy Season in the Congo Basin
Remote Sens. 2017, 9(5), 461; https://doi.org/10.3390/rs9050461 - 09 May 2017
Cited by 7
Abstract
Knowledge of how rainfall seasonality affects land surface phenology has important implications on understanding ecosystem resilience to future climate change in the Congo Basin. We studied the impacts of land cover on the response of the canopy greenness cycle (CGC) to the rainy [...] Read more.
Knowledge of how rainfall seasonality affects land surface phenology has important implications on understanding ecosystem resilience to future climate change in the Congo Basin. We studied the impacts of land cover on the response of the canopy greenness cycle (CGC) to the rainy season in the Congo Basin on a yearly basis during 2006–2013. Specifically, we retrieved CGC from the time series of two-band enhanced vegetation index (EVI2) acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI). We then detected yearly onset (ORS) and end (ERS) of the rainy season using a modified Climatological Anomalous Accumulation (CAA) method based on the daily rainfall time series provided by the Tropical Rainfall Measurement Mission. We further examined the timing differences between CGC and the rainy season across different types of land cover, and investigated the relationship between spatial variations in CGC and rainy season timing. Results show that the rainy season in the equatorial Congo Basin was regulated by a distinct bimodal rainfall regime. The spatial variation in the rainy season timing presented distinct latitudinal gradients whereas the variation in CGC timing was relatively small. Moreover, the inter-annual variation in the rainy season timing could exceed 40 days whereas it was predominantly less than 20 days for CGC timing. The response of CGC to the rainy season varied with land cover. The lead time of CGC onset prior to ORS was longer in tropical woodlands and forests, whereas it became relatively short in grasslands and shrublands. Further, the spatial variation in CGC onset had a stronger correlation with that of ORS in grasslands and shrublands than in tropical woodlands and forests. In contrast, the lag of CGC end behind ERS was widespread across the Congo Basin, which was longer in grasslands and shrublands than that in tropical woodlands and forests. However, no significant relationship was identified between spatial variations in ERS and CGC end. Full article
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Open AccessArticle
Spring and Autumn Phenological Variability across Environmental Gradients of Great Smoky Mountains National Park, USA
Remote Sens. 2017, 9(5), 407; https://doi.org/10.3390/rs9050407 - 26 Apr 2017
Cited by 6
Abstract
Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, [...] Read more.
Mountainous regions experience complex phenological behavior along climatic, vegetational and topographic gradients. In this paper, we use a MODIS time series of the Normalized Difference Vegetation Index (NDVI) to understand the causes of variations in spring and autumn timing from 2000 to 2015, for a landscape renowned for its biological diversity. By filtering for cover type, topography and disturbance history, we achieved an improved understanding of the effects of seasonal weather variation on land surface phenology (LSP). Elevational effects were greatest in spring and were more important than site moisture effects. The spring and autumn NDVI of deciduous forests were found to increase in response to antecedent warm temperatures, with evidence of possible cross-seasonal lag effects, including possible accelerated green-up after cold Januarys and early brown-down following warm springs. Areas that were disturbed by the hemlock woolly adelgid and a severe tornado showed a weaker sensitivity to cross-year temperature and precipitation variation, while low severity wildland fire had no discernable effect. Use of ancillary datasets to filter for disturbance and vegetation type improves our understanding of vegetation’s phenological responsiveness to climate dynamics across complex environmental gradients. Full article
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Open AccessArticle
Investigation of Urbanization Effects on Land Surface Phenology in Northeast China during 2001–2015
Remote Sens. 2017, 9(1), 66; https://doi.org/10.3390/rs9010066 - 12 Jan 2017
Cited by 16
Abstract
The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS [...] Read more.
The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS Land Surface Temperature (LST) data and China’s Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity (UHII) and urbanization effects on LSP in Northeast China during 2001–2015. LST and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. A Mann–Kendall nonparametric test and Sen’s slope were used to evaluate the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average LSP during 2001–2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban areas had become earlier and earlier compared to rural areas, and the differences in SOS between urbanized areas and rural areas changed greatly during 2001–2015 (−0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural areas, especially in urbanized areas (0.92 days/year, p < 0.01), but the differences in the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China. Full article
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Open AccessArticle
Spatiotemporal Variability of Land Surface Phenology in China from 2001–2014
Remote Sens. 2017, 9(1), 65; https://doi.org/10.3390/rs9010065 - 12 Jan 2017
Cited by 19
Abstract
Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a [...] Read more.
Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a period during which China has experienced remarkably strong El Niño events. In addition, even fewer studies have focused on changes of the end of season (EOS) and length of season (LOS) despite their importance. In this study, we used four methods to reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) dataset and chose the best smoothing result to estimate land surface phenology. Then, the phenophase trends were analyzed via the Mann-Kendall method. We aimed to assess whether trends in land surface phenology have continued since 2000 in China at both national and regional levels. We also sought to determine whether trends in land surface phenology in subtropical or high altitude areas are the same as those observed in high latitude areas and whether those trends are uniform among different vegetation types. The result indicated that the start of season (SOS) was progressively delayed with increasing latitude and altitude. In contrast, EOS exhibited an opposite trend in its spatial distribution, and LOS showed clear spatial patterns over this region that decreased from south to north and from east to west at a national scale. The trend of SOS was advanced at a national level, while the trend in Southern China and the Tibetan Plateau was opposite to that in Northern China. The transaction zone of the SOS within Northern China and Southern China occurred approximately between 31.4°N and 35.2°N. The trend in EOS and LOS were delayed and extended, respectively, at both national and regional levels except that of LOS in the Tibetan Plateau, which was shortened by delayed SOS onset more than by delayed EOS onset. The absolute magnitude of SOS was decreased after 2000 compared with previous studies, and the phenophase trends are species specific. Full article
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Open AccessArticle
Evaluation of a Phenology-Dependent Response Method for Estimating Leaf Area Index of Rice Across Climate Gradients
Remote Sens. 2017, 9(1), 20; https://doi.org/10.3390/rs9010020 - 29 Dec 2016
Cited by 4
Abstract
Accurate estimate of the seasonal leaf area index (LAI) in croplands is required for understanding not only intra- and inter-annual crop development, but also crop management. Lack of consideration in different growth phases in the relationship between LAI and vegetation indices (VI) often [...] Read more.
Accurate estimate of the seasonal leaf area index (LAI) in croplands is required for understanding not only intra- and inter-annual crop development, but also crop management. Lack of consideration in different growth phases in the relationship between LAI and vegetation indices (VI) often results in unsatisfactory estimation in the seasonal course of LAI. In this study, we partitioned the growing season into two phases separated by maximum VI ( VI max ) and applied the general regression model to the data gained from two phases. As an alternative method to capture the influence of seasonal phenological development on the LAI-VI relationship, we developed a consistent development curve method and compared its performance with the general regression approaches. We used the Normalized Difference VI (NDVI) and the Enhanced VI (EVI) from the rice paddy sites in Asia (South Korea and Japan) and Europe (Spain) to examine its applicability across different climate conditions and management cycles. When the general regression method was used, separating the season into two phases resulted in no better estimation than the estimation obtained with the entire season observation due to an abrupt change in seasonal LAI occurring during the transition between the before and after VI max . The consistent development curve method reproduced the seasonal patterns of LAI from both NDVI and EVI across all sites better than the general regression method. Despite less than satisfactory estimation of a local LAI max , the consistent development curve method demonstrates improvement in estimating the seasonal course of LAI. The method can aid in providing accurate seasonal LAI as an input into ecological process-based models. Full article
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Open AccessFeature PaperArticle
Characterizing Cropland Phenology in Major Grain Production Areas of Russia, Ukraine, and Kazakhstan by the Synergistic Use of Passive Microwave and Visible to Near Infrared Data
Remote Sens. 2016, 8(12), 1016; https://doi.org/10.3390/rs8121016 - 11 Dec 2016
Cited by 5
Abstract
We demonstrate the synergistic use of surface air temperature retrieved from AMSR-E (Advanced Microwave Scanning Radiometer on Earth observing satellite) and two vegetation indices (VIs) from the shorter wavelengths of MODIS (MODerate resolution Imaging Spectroradiometer) to characterize cropland phenology in the major grain [...] Read more.
We demonstrate the synergistic use of surface air temperature retrieved from AMSR-E (Advanced Microwave Scanning Radiometer on Earth observing satellite) and two vegetation indices (VIs) from the shorter wavelengths of MODIS (MODerate resolution Imaging Spectroradiometer) to characterize cropland phenology in the major grain production areas of Northern Eurasia from 2003–2010. We selected 49 AMSR-E pixels across Ukraine, Russia, and Kazakhstan, based on MODIS land cover percentage data. AMSR-E air temperature growing degree-days (GDD) captures the weekly, monthly, and seasonal oscillations, and well correlated with station GDD. A convex quadratic (CxQ) model that linked thermal time measured as growing degree-days to accumulated growing degree-days (AGDD) was fitted to each pixel’s time series yielding high coefficients of determination (0.88 ≤ r2 ≤ 0.98). Deviations of observed GDD from the CxQ model predicted GDD by site corresponded to peak VI for negative residuals (period of higher latent heat flux) and low VI at beginning and end of growing season for positive residuals (periods of higher sensible heat flux). Modeled thermal time to peak, i.e., AGDD at peak GDD, showed a strong inverse linear trend with respect to latitude with r2 of 0.92 for Russia and Kazakhstan and 0.81 for Ukraine. MODIS VIs tracked similar seasonal responses in time and space and were highly correlated across the growing season with r2 > 0.95. Sites at lower latitude (≤49°N) that grow winter and spring grains showed either a bimodal growing season or a shorter unimodal winter growing season with substantial inter-annual variability, whereas sites at higher latitude (≥56°N) where spring grains are cultivated exhibited shorter, unimodal growing seasons. Sites between these extremes exhibited longer unimodal growing seasons. At some sites there were shifts between unimodal and bimodal patterns over the study period. Regional heat waves that devastated grain production in 2007 in Ukraine and in 2010 in Russia and Kazakhstan appear clearly anomalous. Microwave based surface air temperature data holds great promise to extend to parts of the planet where the land surface is frequently obscured by clouds, smoke, or aerosols, and where routine meteorological observations are sparse or absent. Full article
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Open AccessArticle
Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest
Remote Sens. 2016, 8(12), 997; https://doi.org/10.3390/rs8120997 - 03 Dec 2016
Cited by 12
Abstract
Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify [...] Read more.
Phenology-based multi-index with the random forest (RF) algorithm can be used to overcome the shortcomings of traditional deforestation mapping that involves pixel-based classification, such as ISODATA or decision trees, and single images. The purpose of this study was to investigate methods to identify specific types of deforestation in North Korea, and to increase the accuracy of classification, using phenological characteristics extracted with multi-index and random forest algorithms. The mapping of deforestation area based on RF was carried out by merging phenology-based multi-indices (i.e., normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference soil index (NDSI)) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) products and topographical variables. Our results showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87. In particular, for forest and farm land categories with similar phenological characteristic (e.g., paddy, plateau vegetation, unstocked forest, hillside field), this approach improved the classification accuracy in comparison with pixel-based methods and other classes. The deforestation types were identified by incorporating point data from high-resolution imagery, outcomes of image classification, and slope data. Our study demonstrated that the proposed methodology could be used for deciding on the restoration priority and monitoring the expansion of deforestation areas. Full article
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Open AccessArticle
Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation
Remote Sens. 2016, 8(10), 847; https://doi.org/10.3390/rs8100847 - 17 Oct 2016
Cited by 22Correction
Abstract
To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but [...] Read more.
To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, only GRVI showed significant correlations with NDVI in all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (GDN − RDN)/(GDN + RDN) where GDN is Green digital number and RDN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic. Full article
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Open AccessArticle
Effects of Different Methods on the Comparison between Land Surface and Ground Phenology—A Methodological Case Study from South-Western Germany
Remote Sens. 2016, 8(9), 753; https://doi.org/10.3390/rs8090753 - 13 Sep 2016
Cited by 10
Abstract
Several methods exist for extracting plant phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite observations (Land Surface Phenology or LSP) with ground based phenological observations (Ground Phenology or GP). The [...] Read more.
Several methods exist for extracting plant phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite observations (Land Surface Phenology or LSP) with ground based phenological observations (Ground Phenology or GP). The classical pixel to point matching problem along with the temporal and spatial resolution of remote sensing data are some of the many issues encountered. In this study, MODIS-sensor’s Normalised Differenced Vegetation Index (NDVI) time series data were smoothed using two filtering techniques for comparison. Several start of season (SOS) methods established in the literature, namely thresholds of amplitude, derivatives and delayed moving average, were tested for determination of LSP-SOS for broadleaf forests at a site in southwestern Germany using 2001–2013 time series of NDVI data. The different LSP-SOS estimates when compared with species-rich GP dataset revealed that different LSP-SOS extraction methods agree better with specific phases of GP, and the choice of data processing or smoothing strongly affects the LSP-SOS extracted. LSP methods mirroring late SOS dates, i.e., 75% amplitude and 1st derivative, indicated a better match in means and trends, and high, significant correlations of up to 0.7 with leaf unfolding and greening of late understory and broadleaf tree species. GP-SOS of early understory leaf unfolding partly were significantly correlated with earlier detecting LSP-SOS, i.e., 20% amplitude and 3rd derivative. Early understory SOS were, however, more difficult to detect from NDVI due to the lack of a high resolution land cover information. Full article
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Open AccessArticle
Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series
Remote Sens. 2016, 8(9), 741; https://doi.org/10.3390/rs8090741 - 08 Sep 2016
Cited by 15
Abstract
Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated [...] Read more.
Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase. Full article
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