M Moderate Resolution Imaging Spectroradiometer Research Articles

A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery

Land surface phenology (LSP) is beneficial to understand ecosystem response to climate change, vegetation and crop type discrimination, and ecological modeling. However, the existing efforts based on coarse resolution data (≥500 m) cannot perform well in regions with higher spatial heterogeneity and multi-cropping system, such as China. Given that the majority of 10 m/30 m-based phenological research has focused on North America and Europe, developing spatiotemporally explicit LSP data in China is imperative. More importantly, the existing 30 m LSP products are mainly suitable for vegetation types with a single vegetation cycle, but cannot work well for biomes with complex seasonality (e.g., multiple growth cycles). Here we first harmonized three vegetation indices, i.e., the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2), and land surface water index (LSWI) from Landsat-7/8 and Sentinel-2 imagery on the Google Earth Engine (GEE) platform. We then developed a new 30 m LSP algorithm that unified different phenological cycle-seeking processes per vegetation type and improved the existing algorithm. Furthermore, we used the algorithm to estimate the LSP product (LSP30CHN) for 2016–2019 across China, suitable for all vegetation types. The validation results showed a reasonably high accuracy (R2 > 0.6, RMSE < 15 days, mostly) of the LSP30CHN data against multi-sources in-situ observational (e.g., PhenoCam) and satellite-retrieved vegetation phenology data. Moreover, LSP30CHN data showed a consistent pattern but finer spatial details with the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) phenology product (MCD12Q2) at the homogenous area. We also found that phenological differences between LSP30CHN and MCD12Q2 increased with surface fragmentation, suggesting the potential of LSP30CHN to delineate phenological information on more fragmented landscapes. In contrast, the 500 m LSP data cannot provide such details in the regions with mixed cropping structures (e.g., corn, rice, and soybean) and multiple cropping index (e.g., single- and double-cropping systems). This study offers high accuracy of the LSP map for China, valuable for finer phenology-based services such as field-level crop management and agricultural phenology monitoring. It opens up new insights about exploring large-scale refined agricultural management and ecological assessment for other regions with complicated, fragmented landscapes and vegetation seasonality.

Developing global annual land surface phenology datasets (1982–2018) from the AVHRR data using multiple phenology retrieval methods

Land surface phenology that reflects periodical changes in terrestrial vegetation plays an important role in influencing the functioning and services of ecosystems. Satellite-derived metrics of land surface phenology have been widely used to assess the patterns in land cover dynamics. There are still limited satellite-based datasets of global land surface phenology available to date due to uneven qualities of satellite data. Here, we developed a global annual land surface phenology dataset based on Advanced Very High Resolution Radiometer (AVHRR) data from 1982 to 2018. The number of global vegetation growing seasons for each individual pixel was counted for each year from 1982 to 2018. We implemented four phenology retrieval methods, including the amplitude threshold method, the second-order derivative method, the third-order derivative method, and the curvature change rate method, to retrieve four key phenological metrics for each vegetation growth cycle, including the start of the growing season (SOS), maturity, senescence, and the end of the growing season (EOS). The results show that the phenological metrics retrieved using different phenology retrieval methods have similar patterns of global spatial distribution. From 1982 to 2018, the timing of both maturity and senescence within a year advanced in the Northern Hemisphere and the timing of SOS delayed in the Southern Hemisphere. The phenological metrics extracted from in-situ data such as the USA National Phenology Network (USA-NPN), the Pan European Phenology Database (PEP725), and flux tower measurements were positively correlated with satellite-derived phenological metrics. Despite the effects of mismatching spatial scales, the retrieved phenological metrics based on 5 km AVHRR data were consistent with those based on 500 m Moderate-resolution Imaging Spectroradiometer (MODIS) data and 30 m fused satellite data at both site-scale and regional-scale. The developed dataset released for usages and applications on global land surface dynamics offers potential end users options for choosing desirable phenological metrics derived from different methods.

Digital Soil Mapping Based on Fine Temporal Resolution Landsat Data Produced by Spatiotemporal Fusion

Multi-temporal Landsat-8 satellite images with fine spatial resolution (i.e., 30 m) are crucial for modern digital soil mapping (DSM). Generally, cloud-free images covering bare topsoil are common choices for DSM. However, the number of effective Landsat-8 data is greatly limited due to cloud contamination coupled with the coarse temporal resolution, and interference of material covering topsoil in most of the months, hindering the development of accurate DSM. To address this issue, temporally dense Landsat images were predicted using a spatio-temporal fusion method to improve DSM. Specifically, the recently developed virtual image pair-based spatio-temporal fusion (VIPSTF) method was adopted to produce simulated Landsat-8 time-series, by fusing with 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) time-series with frequent observations. Subsequently, the simulated Landsat-8 data were used for distinguishing different soil classes via a random forest (RF) model. Training and validation samples of soil classes were collected from legacy soil data. Our results indicate that the simulated data were beneficial for improving DSM owing to the increase in class separability. More precisely, after combining the observed and simulated data, the overall accuracy (OA) and kappa coefficient (Kappa) were increased by 3.099% and 0.047, respectively. This research explored the potential of the spatio-temporal fusion method for DSM, providing a new solution for remote sensing-based DSM.

A physical method for downscaling land surface temperatures using surface energy balance theory

Fine-resolution land surface temperature (LST) derived from thermal infrared remote sensing images is a good indicator of surface water status and plays an essential role in the exchange of energy and water between land and atmosphere. A physical surface energy balance (SEB)-based LST downscaling method (DTsEB) is developed to downscale coarse remotely sensed thermal infrared LST products with fine-resolution visible and near-infrared data. The DTsEB method is advantageous for its ability to mechanically interrelate surface variables contributing to the spatial variation of LST, to quantitatively weigh the contributions of each related variable within a physical framework, and to efficaciously avoid the subjective selection of scaling factors and the establishment of statistical regression relationships. The applicability of the DTsEB method was tested by downscaling 12 scenes of 990 m Moderate Resolution Imaging Spectroradiometer (MODIS) and aggregated Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST products to 90 m resolution at six overpass times between 2005 and 2015 over three 9.9 km by 9.9 km cropland (mixed by grass, tree, and built-up land) study areas. Three typical LST downscaling methods, namely the widely applied TsHARP, the later developed least median square regression downscaling (LMS) and the geographically weighted regression (GWR), were introduced for intercomparison. The results showed that the DTsEB method could more effectively reconstruct the subpixel spatial variations in LST within the coarse-resolution pixels and achieve a better downscaling accuracy than the TsHARP, LMS and GWR methods. The DTsEB method yielded, on average, root mean square errors (RMSEs) of 2.01 K and 1.42 K when applied to the MODIS datasets and aggregated ASTER datasets, respectively, which were lower than those obtained with the TsHARP method, with average RMSEs of 2.41 K and 1.71 K, the LMS method, with average RMSEs of 2.35 K and 1.63 K, and the GWR method, with average RMSEs of 2.38 K and 1.64 K, respectively. The contributions of the related surface variables to the subpixel spatial variation in the LST varied both spatially and temporally and were different from each other. In summary, the DTsEB method was demonstrated to outperform the TsHARP, LMS, and GWR methods and could be used as a good alternative for downscaling LST products from coarse to fine resolution with high robustness and accuracy.

Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification.

Influence of two management practices in the Canadian Prairies on radiative forcing

Surface albedo and soil carbon sequestration are influenced by agricultural management practices which impact the Earth's radiation budget and climate change. In this study we investigate the impact of reduced summer fallowing and reduced tillage in the Canadian Prairies on climate change by estimating the change in radiative forcing due to albedo and soil carbon sequestration. Seasonal variations of albedo, which are dependent on agricultural management practices and soil colour in three soil zones, were derived from 10-day composite 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data. Using this information, we found an overall increase of surface albedo due to the conversion from summer fallowing to continuous cropping and from conventional tillage (CT) to either no-tillage (NT) or reduced tillage (RT). The increase was dependent on soil brightness, type of vegetation and snow cover. Using data from the Census of Agriculture and taking into consideration both albedo and soil carbon changes, we estimated that from 1981 to 2016, the total radiative forcing for the cropland area in the Canadian Prairies was −405 μW m−2 due to the conversion of CT to either NT or RT and about 70% was due to the change in albedo. During the same period, the total radiative forcing was −410 μW m−2 due to a reduction in the area under summer fallow and about 62% was due to the change in albedo. The equivalent atmospheric CO2 drawdown from these two management changes from albedo change was about 7.8 and 8.7 Tg CO2 yr−1, respectively. These results demonstrate that it is important to consider both the changes of soil carbon and surface albedo in evaluating climate change impacts due to agricultural management practices.

Improved mapping and change detection of the start of the crop growing season in the US Corn Belt from long-term AVHRR NDVI

Long-term monitoring of crop phenology is a critical process to understand changes in cropland vegetation dynamics related to climate and human management. During the last four decades, seasonal changes in vegetation have been measured by satellite instruments, the Advanced Very High Resolution Radiometers (AVHRRs). However, an acceptable detection of crop phenology has been prevented due to its coarse spatial resolution (~8 km) pixels in which croplands are frequently mixed with non-cropland. This study introduces a novel method for detecting the start of the growing season in cropland (SOSC) from the AVHRR Normalized Difference Vegetation Index (NDVI) to examine the long-term changes in SOSC at the 8-km pixel level across the core cropping regions of the Midwestern United States (the so-called ‘the Corn Belt’) for 1982–2015. The AVHRR-based SOSC was compared with the SOSC retrieved from the pixels covering mostly croplands of the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. These two satellite-based SOSC shows reasonable correspondence in spatial patterns so that the annual values of the root mean square difference ranges from 3.6 to 7.4 days during the overlapping period of 2008–2015. Also, an acceptable correspondence was found between the interannual variations in the AVHRR SOSCs and in the state-level survey of crop stages. For sowing stages, correlation coefficients ranged from 0.43 to 0.68; for flowering stages those ranged from 0.51 to 0.83. By analyzing long-term SOSC changes with climate factors, we found a strong tendency that warmer pre-season condition is accompanied with earlier SOSC. In addition, larger pre-season precipitation tends to postpone SOSC as strongly as the pre-season temperature affects SOSC in a considerable portion of the core cropland. This study suggests that the postponing of sowing due to larger precipitation, which has rarely been highlighted in previous studies, requires more attentions in order for better understanding of vegetation dynamics in agriculture-climate studies.

The utility of fusing multi-sensor data spatio-temporally in estimating grassland aboveground biomass in the three-river headwaters region of China

ABSTRACT Accurate grassland aboveground biomass (AGB) estimation is crucial for effective grassland utilization. However, most current satellites cannot provide data with high spatial and temporal resolutions simultaneously. Spatiotemporal fusion models can combine the resolution advantages of different remote sensing data and support high-precision vegetation monitoring. In order to obtain accurate grassland AGB maps with high resolution in the Three-River Headwaters Region (TRHR) of China, we developed an estimation method based on the synthetic 30 m growing season averaged normalized difference vegetation index (GS-NDVI), which was fused from 30 m Landsat 8 Operational Land Imager (OLI) and 250 m Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data. To choose the optimal fusion model, we investigated the performances of three spatiotemporal fusion models for NDVI fusion, the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the rule-based piecewise regression tree model (RPRTM). The three models all produced reasonable NDVI predictions, with the coefficient of determination (R 2) ranging from 0.58 to 0.86. RPRTM had the highest efficiency and was more suitable for large-scale spatiotemporal data fusion. Compared with the models generated from 250 m MODIS GS-NDVI, the AGB estimation models based on 30 m synthetic GS-NDVI were more accurate, demonstrating the effectiveness of our methods. The resulting AGB map of 30 m resolution provides spatially detailed AGB information that will be useful for regional ecosystem studies and local land management decisions.

Sustainable Development Through Green-Themed Environmental Impact Assessment: Lessons for Developing Countries

In this study, we used Geographic Information System and remote sensing to explore the stimulation of sustainable development through “Green-Conscious” Environmental Impact Assessment of industrial projects. Despite the global clamour for sustainable development as panacea for recent climatic concerns; sustainable development initiatives like Nano and green technologies are not highlighted in key Sections of the Nigerian EIA Act. The study adopted time series of 16-day composite Moderate Resolution Imaging Spectroradiometer 250m resolution NDVI data (MOD13Q1) covering South West, Nigeria between 2002–2017. We conclude that Nigeria cannot attain sustainable development if relevant laws are not amended to include sustainability initiatives. Keywords: Sustainable Development; Going-Green; Environmental Impact Assessment; EIA; Industrial Projects, Nanotechnology DOI: 10.7176/JEES/10-5-06 Publication date: May 31 st 2020

Field-Scale Crop Seeding Date Estimation from MODIS Data and Growing Degree Days in Manitoba, Canada

Information on crop seeding date is required in many applications; such as crop management and yield forecasting. This study presents a novel method to estimate crop seeding date at the field level from time-series 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data and growing degree days (GDD; base 5 °C; °C-days). The start of growing season (SOS) was first derived from time-series EVI2 (two-band Enhanced Vegetation Index) calculated from a MODIS 8-day composite surface reflectance product (MOD09Q1; Collection 6). Based on GDD; calculated from the Daymet gridded estimates of daily weather parameters; a simple model was developed to establish a linkage between the observed seeding date and the SOS. Calibration and validation of the model was conducted on three major crops; spring wheat; canola and oats; in the Province of Manitoba; Canada. The estimated SOS had a strong linear correlation with the observed seeding date; with a deviation of a few days depending on the year. The seeding date of the three crops can be calculated from the SOS by adjusting the number of days needed to accumulate GDD (AGDD) for emergence. The overall root-mean-square-difference (RMSD) of the estimated seeding date was less than 10 days. Validation showed that the accuracy of the estimated seeding date was crop-type independent. The developed method is useful for estimating the historical crop seeding date from remote sensing data in Canada; to support studies of the interactions among seeding date; crop management and crop yield under climate change. It is anticipated that this method can be adapted to other crops in other locations using the same or different satellite data.

Drought hazard in Kazakhstan in 2000-2016: a remote sensing perspective.

Droughts have significant negative impacts on livelihoods and economy of Kazakhstan. In this study, we assessed and characterized drought hazard events in Kazakhstan using satellite Remote Sensing time series for the period between 2000 and 2016. First, we calculated Vegetation Condition Index (VCI) and Standardized Enhanced Vegetation Index anomalies (ZEVI) based on 250m Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series. Second, we assessed vegetation cover changes for the observation period. Third, we analyzed different characteristics of the drought hazard as well as spatial distribution of the drought-affected areas within the country. The results confirmed that drought was one of the environmental challenges for Kazakhstan in 2000-2016. The obtained maps showed that drought hazard conditions were observed every year, though the areal coverage of the drought conditions largely varied between the analyzed years. The calculated drought indices indicated that in years 2000, 2008, 2010, 2011, 2012, and 2014, more than 50% of the area of the country were affected by drought conditions of different severity with the largest droughts in terms of the areal spread occurring in 2012 and 2014. We concluded that the pre-requisite of successful implementation of drought hazard and risk mitigation strategies is availability of spatially explicit, timely, and reliable information on drought hazard. This suggests the necessity of incorporation of remote sensing-based drought information, as was demonstrated in this paper, in the national drought monitoring system of Kazakhstan.

Changes in vegetation cover and snowmelt timing in the Noatak National Preserve of Northwestern Alaska estimated from MODIS and Landsat satellite image analysis

ABSTRACT Trends and transitions in the growing season Normalized Difference Vegetation Index (NDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite sensor at 250-m resolution were analyzed for the period from 2000 to 2018 to understand recent patterns of vegetation change in ecosystems of the Noatak National Preserve in northwestern Alaska. Statistical analysis of changes in the NDVI time series was conducted using the “Breaks for Additive Seasonal and Trend” method (BFAST). This structural change analysis indicated that at least one NDVI abrupt change breakpoint was detected in 25% of the MODIS pixels covering the NOAT. All of the large wildfires mapped by Landsat burn severity classifications within the NOAT since the year 2000 coincided with multiple negative NDVI breakpoints. Results showed that six large drainage basins, all within the Eli River and Kugururok River systems of the western NOAT, had significant correlations between early spring snowmelt and annual area burned. Later-than-average snowmelt dates were strongly associated with a high number of abrupt shifts in greenness cover detected by BFAST. Negative (browning) NDVI trends in the de-seasonalized residuals were detected as significant (p < 0.05) at 15% of the total MODIS 250-m pixel coverage of the NOAT.

Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico

Abstract. During the spring of 2014, 130 million m3 of water were released from the United States' Morelos Dam on the lower Colorado River to Mexico, allowing water to reach the Gulf of California for the first time in 13 years. Our study assessed the effects of water transfer or ecological environmental flows from one nation to another, using remote sensing. Spatial applications for water resource evaluation are important for binational, integrated water resources management and planning for the Colorado River, which includes seven basin states in the US plus two states in Mexico. Our study examined the effects of the historic binational experiment (the Minute 319 agreement) on vegetative response along the riparian corridor. We used 250 m Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and 30 m Landsat 8 satellite imagery to track evapotranspiration (ET) and the normalized difference vegetation index (NDVI). Our analysis showed an overall increase in NDVI and evapotranspiration (ET) in the year following the 2014 pulse, which reversed a decline in those metrics since the last major flood in 2000. NDVI and ET levels decreased in 2015, but were still significantly higher (P &lt; 0.001) than pre-pulse (2013) levels. Preliminary findings show that the decline in 2015 persisted into 2016 and 2017. We continue to analyse results for 2018 in comparison to short-term (2013–2018) and long-term (2000–2018) trends. Our results support the conclusion that these environmental flows from the US to Mexico via the Minute 319 “pulse” had a positive, but short-lived (1 year), impact on vegetation growth in the delta.

Mapping daily leaf area index at 30 m resolution over a meadow steppe area by fusing Landsat, Sentinel-2A and MODIS data

ABSTRACTThe leaf area index (LAI) is a key vegetation canopy structure parameter and is closely associated with vegetation photosynthesis, transpiration, and energy balance. Developing a landscape-scale LAI dataset with a high temporal resolution (daily) is essential for capturing rapidly changing vegetation structure at field scales and supporting regional biophysical modeling efforts. In this study, two daily 30 m LAI time series from 2014 to 2016 over a meadow steppe site in northern China were generated using a spatial and temporal adaptive reflectance fusion model (STARFM) combined with an LAI retrieval radiative transfer model (PROSAIL). Gap-filled Landsat 7, Landsat 8 and Sentinel-2A surface reflectance (SR) images were used to generate fine-resolution LAI maps with the PROSAIL look-up table method. Two daily 500 m moderate-resolution imaging spectroradiometer (MODIS) LAI product-the existing MCD15A3H LAI product and one was generated from the MCD43A4 SR product and the PROSAIL model, were used to provide temporally continuous LAI variations. The STARFM model was then used to fuse the fine-resolution LAI maps with the two 500 m LAI products separately to generate two daily 30 m LAI time series. Both results were assessed for three types of pasture (mowed pasture, grazing pasture, and fenced pasture) using ground measurements from 2014–2015. The results showed that the PROSAIL-generated LAI maps all exhibited a high accuracy, and the root mean squared errors (RMSEs) for the Landsat 7 LAI and Landsat 8 LAI compared to the ground-measured LAI were 0.33 and 0.28 respectively. The Landsat LAI maps also showed good agreement and similar spatial patterns with the Sentinel-2A LAI with mean differences between ± 0.5. The MCD43A4_PROSPECT LAI product exhibited similar seasonal variability to the ground measurements and to the Landsat and Sentinel-2A LAIs, and these data are also smoother and contain fewer noisy points than the gap-filled MCD15A3H LAI product. Compared to the ground measurements, the daily 30 m LAI time series fused from the fine-resolution LAI maps and PROSPECT generated MODIS LAI product demonstrated better performance with an RMSE of 0.44 and a mean absolute error (MAE) of 0.34, which is an improvement from the LAI time series fused from the fine-resolution LAI maps and the existing MCD15A3H LAI product (RMSE of 0.56 and MAE of 0.42). The latter dataset also exhibited abnormal temporal fluctuations, which may have been caused by the interpolation method. The results also demonstrated the very good performance of the STARFM model in grazing and mowed pasture with homogeneous surfaces compared to fenced pasture with smaller patch sizes. The Sentinel-2A data offers increased landscape vegetation observation frequency and provides temporal information about canopy changes that occur between Landsat overpass dates. The scheme developed in this study can be used as a reference for regional vegetation dynamic studies and can be applied to larger areas to improve grassland modeling efforts.

Analyzing the December 2013 Metaponto Plain (Southern Italy) Flood Event by Integrating Optical Sensors Satellite Data

Timely and continuous information about flood dynamics are fundamental to ensure an effective implementation of the relief and rescue operations. Satellite data provided by optical sensors onboard meteorological satellites could have great potential in this framework, offering an adequate trade-off between spatial and temporal resolution. The latest would benefit from the integration of observations coming from different satellite systems, also helping to increase the probability of finding cloud free images over the investigated region. The Robust Satellite Techniques for detecting flooded areas (RST-FLOOD) is a sensor-independent multi-temporal approach aimed at detecting flooded areas which has already been applied with good results on different polar orbiting optical sensors. In this work, it has been implemented on both the 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) and the 375 m Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS). The flooding event affecting the Basilicata and Puglia regions (southern Italy) in December 2013 has been selected as a test case. The achieved results confirm the RST-FLOOD potential in reliably detecting, in case of small basins, flooded areas regardless of the sensor used. Flooded areas have indeed been detected with similar performance by the two sensors, allowing for their continuous and near-real time monitoring.

Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps

Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from “snow-covered” to “no snow” during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 m MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001–2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001–2015 average, with 25% of its land area melting>65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.

Monitoring cotton root rot by synthetic Sentinel-2 NDVI time series using improved spatial and temporal data fusion

To better understand the progression of cotton root rot within the season, time series monitoring is required. In this study, an improved spatial and temporal data fusion approach (ISTDFA) was employed to combine 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Different Vegetation Index (NDVI) and 10-m Sentinetl-2 NDVI data to generate a synthetic Sentinel-2 NDVI time series for monitoring this disease. Then, the phenology of healthy cotton and infected cotton was modeled using a logistic model. Finally, several phenology parameters, including the onset day of greenness minimum (OGM), growing season length (GLS), onset of greenness increase (OGI), max NDVI value, and integral area of the phenology curve, were calculated. The results showed that ISTDFA could be used to combine time series MODIS and Sentinel-2 NDVI data with a correlation coefficient of 0.893. The logistic model could describe the phenology curves with R-squared values from 0.791 to 0.969. Moreover, the phenology curve of infected cotton showed a significant difference from that of healthy cotton. The max NDVI value, OGM, GSL and the integral area of the phenology curve for infected cotton were reduced by 0.045, 30 days, 22 days, and 18.54%, respectively, compared with those for healthy cotton.

Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

Land surface albedo is a key parameter controlling the local energy budget, and altering the albedo of built surfaces has been proposed as a tool to mitigate high near‐surface temperatures in the urban heat island. However, most research on albedo in urban landscapes has used coarse‐resolution data, and few studies have attempted to relate albedo to other urban land cover characteristics. This study provides an empirical description of urban summertime albedo using 30 m remote sensing measurements in the metropolitan area around Boston, Massachusetts, relating albedo to metrics of impervious cover fraction, tree canopy coverage, population density, and land surface temperature (LST). At 30 m spatial resolution, median albedo over the study area (excluding open water) was 0.152 (0.112–0.187). Trends of lower albedo with increasing urbanization metrics and temperature emerged only after aggregating data to 500 m or the boundaries of individual towns, at which scale a −0.01 change in albedo was associated with a 29 (25–35)% decrease in canopy cover, a 27 (24–30)% increase in impervious cover, and an increase in population from 11 to 386 km−2. The most intensively urbanized towns in the region showed albedo up to 0.035 lower than the least urbanized towns, and mean mid‐morning LST 12.6°C higher. Trends in albedo derived from 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were comparable, but indicated a strong contribution of open water at this coarser resolution. These results reveal linkages between albedo and urban land cover character, and offer empirical context for climate resilient planning and future landscape functional changes with urbanization.

The dynamics of wetland cover change using a state estimation technique applied to time-series remote sensing imagery

ABSTRACTMonitoring the dynamics of inundation areas in wetlands over contiguous years is important because it influences wetland ecosystem monitoring. However, because the variable nature of wetlands tends to hamper monitoring change analyses, the potential for misinterpretation increases. The Kalman filter (KF) or extended Kalman filter (EKF), which uses recursive processing based on the former information, can be applied to time-series remote sensing imagery. In the experiment, a periodic triangle function of two modulated parameters is treated as the system model, and Normalized Difference Vegetation Index (NDVI) time-series data are used for the measurement model in the correction processes of the state estimation. A decision metric is computed from the mean and amplitude sequence, which results from the state estimation filter. Consequently, an optimal threshold is calculated using a minimum error thresholding algorithm based on a pre-labelled sample. NDVI time-series data from Poyang Lake, China – derived from 250-m Moderate Resolution Imaging Spectroradiometer satellite data obtained from January 2009 to December 2013 – are applied to monitor the dynamics of inundation changes. The results show that the EKF achieves satisfactory results, with 85.52% accuracy in the year 2009, while the KF has an accuracy of 84.16% during that same time.

Breaks in MODIS time series portend vegetation change: verification using long‐term data in an arid grassland ecosystem

Frequency and severity of extreme climatic events are forecast to increase in the 21st century. Predicting how managed ecosystems may respond to climatic extremes is intensified by uncertainty associated with knowing when, where, and how long effects of extreme events will be manifest in an ecosystem. In water-limited ecosystems with high inter-annual variability in rainfall, it is important to be able to distinguish responses that result from seasonal fluctuations in rainfall from long-term directional increases or decreases in precipitation. A tool that successfully distinguishes seasonal from directional biomass responses would allow land managers to make informed decisions about prioritizing mitigation strategies, allocating human resource monitoring efforts, and mobilizing resources to withstand extreme climatic events. We leveraged long-term observations (2000-2013) of quadrat-level plant biomass at multiple locations across a semiarid landscape in southern New Mexico to verify the use of Normalized Difference Vegetation Index (NDVI) time series derived from 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) data as a proxy for changes in aboveground productivity. This period encompassed years of sustained drought (2000-2003) and record-breaking high rainfall (2006 and 2008) followed by subsequent drought years (2011 through 2013) that resulted in a restructuring of plant community composition in some locations. Our objective was to decompose vegetation patterns derived from MODIS NDVI over this period into contributions from (1) the long-term trend, (2) seasonal cycle, and (3) unexplained variance using the Breaks for Additive Season and Trend (BFAST) model. BFAST breakpoints in NDVI trend and seasonal components were verified with field-estimated biomass at 15 sites that differed in species richness, vegetation cover, and soil properties. We found that 34 of 45 breaks in NDVI trend reflected large changes in mean biomass and 16 of 19 seasonal breaks accompanied changes in the contribution to biomass by perennial and/or annual grasses. The BFAST method using satellite imagery proved useful for detecting previously reported ground-based changes in vegetation in this arid ecosystem. We demonstrate that time series analysis of NDVI data holds potential for monitoring landscape condition in arid ecosystems at the large spatial scales needed to differentiate responses to a changing climate from responses to seasonal variability in rainfall.