Spatiotemporal Analysis of Landsat-8 and Sentinel-2 Data to Support Monitoring of Dryland Ecosystems

Drylands are the habitat and source of livelihood for about two fifths of the world’s population and are highly susceptible to climate and anthropogenic change. To understand the vulnerability of drylands to changing environmental conditions, land managers need to effectively monitor rates of past change and remote sensing offers a cost-effective means to assess and manage these vast landscapes. Here, we present a novel approach to accurately monitor land-surface phenology in drylands of the Western United States using a regression tree modeling framework that combined information collected by the Operational Land Imager (OLI) onboard Landsat 8 and the Multispectral Instrument (MSI) onboard Sentinel-2. This highly-automatable approach allowed us to precisely characterize seasonal variations in spectral vegetation indices with substantial agreement between observed and predicted values (R2 = 0.98; Mean Absolute Error = 0.01). Derived phenology curves agreed with independent eMODIS phenological signatures of major land cover types (average r-value = 0.86), cheatgrass cover (average r-value = 0.96), and growing season proxies for vegetation productivity (R2 = 0.88), although a systematic bias towards earlier maturity and senescence indicates enhanced monitoring capabilities associated with the use of harmonized Landsat-8 Sentinel-2 data. Overall, our results demonstrate that observations made by the MSI and OLI can be used in conjunction to accurately characterize land-surface phenology and exclusion of imagery from either sensor drastically reduces our ability to monitor dryland environments. Given the declines in MODIS performance and forthcoming decommission with no equivalent replacement planned, data fusion approaches that integrate observations from multispectral sensors will be needed to effectively monitor dryland ecosystems. While the synthetic image stacks are expected to be locally useful, the technical approach can serve a wide variety of applications such as invasive species and drought monitoring, habitat mapping, production of phenology metrics, and land-cover change modeling.

[1]  Jianping Huang,et al.  Dryland climate change: Recent progress and challenges , 2017 .

[2]  G. Henebry,et al.  Exploration of scaling effects on coarse resolution land surface phenology , 2017 .

[3]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[4]  Thomas K. Maiersperger,et al.  eMODIS: A User-Friendly Data Source , 2010 .

[5]  Kelly K. Caylor,et al.  Dryland ecohydrology and climate change: critical issues and technical advances , 2012 .

[6]  Yi Y. Liu,et al.  Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle , 2014, Nature.

[7]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[8]  Jennifer L. Dungan,et al.  Sentinel-2 MSI Radiometric Characterization and Cross-Calibration with Landsat-8 OLI , 2017 .

[9]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[10]  David P. Roy,et al.  A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring , 2017, Remote. Sens..

[11]  Peter M. Atkinson,et al.  Fusion of Landsat 8 OLI and Sentinel-2 MSI Data , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Le Yu,et al.  Scaling up spring phenology derived from remote sensing images , 2018, Agricultural and Forest Meteorology.

[13]  B. Wylie,et al.  Linking Phenology and Biomass Productivity in South Dakota Mixed-Grass Prairie , 2013 .

[14]  Jianping Huang,et al.  Accelerated dryland expansion under climate change , 2016 .

[15]  Lingling Liu,et al.  Comparisons of global land surface seasonality and phenology derived from AVHRR, MODIS, and VIIRS data , 2017 .

[16]  Bo Yang,et al.  Flexible neural trees ensemble for stock index modeling , 2007, Neurocomputing.

[17]  Alejandro N. Flores,et al.  Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain , 2017, Remote. Sens..

[18]  Jindi Wang,et al.  Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI , 2016, Remote. Sens..

[19]  Mark A. Friedl,et al.  A Method for Robust Estimation of Vegetation Seasonality from Landsat and Sentinel-2 Time Series Data , 2018, Remote. Sens..

[20]  Predicitions Huanhuan Chen,et al.  Ensemble Regression Trees for Time Series , .

[21]  A. Richardson,et al.  Landscape controls on the timing of spring, autumn, and growing season length in mid‐Atlantic forests , 2012 .

[22]  Matthias Baumann,et al.  Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[23]  Yingxin Gu,et al.  Downscaling 250-m MODIS Growing Season NDVI Based on Multiple-Date Landsat Images and Data Mining Approaches , 2015, Remote. Sens..

[24]  Devendra Singh,et al.  Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[25]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[26]  Bruce K. Wylie,et al.  Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for central Great Basin rangelands, USA , 2018 .

[27]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[28]  Bruce K. Wylie,et al.  Cheatgrass Percent Cover Change: Comparing Recent Estimates to Climate Change — Driven Predictions in the Northern Great Basin☆,☆☆ , 2016, Rangeland Ecology and Management.

[29]  Christopher E. Holden,et al.  Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time , 2015 .

[30]  Zhe Zhu,et al.  Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .

[31]  James Rowland,et al.  A weighted least-squares approach to temporal NDVI smoothing , 1999 .

[32]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[33]  Alessandro Anav,et al.  Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 , 2013, Remote. Sens..

[34]  D. Richardson,et al.  Effects of Invasive Alien Plants on Fire Regimes , 2004 .

[35]  Jinwei Dong,et al.  A global moderate resolution dataset of gross primary production of vegetation for 2000–2016 , 2017, Scientific Data.

[36]  Lawrence E. Band,et al.  Evaluating drought effect on MODIS Gross Primary Production (GPP) with an eco‐hydrological model in the mountainous forest, East Asia , 2008 .

[37]  M. Schaepman,et al.  Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006 , 2009 .

[38]  N. Coops,et al.  Satellites: Make Earth observations open access , 2014, Nature.

[39]  Jianping Huang,et al.  Global semi-arid climate change over last 60 years , 2016, Climate Dynamics.

[40]  Conghe Song,et al.  Reanalysis of global terrestrial vegetation trends from MODIS products: Browning or greening? , 2017 .

[41]  Suming Jin,et al.  Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information , 2015 .