Retrieving vegetation growth patterns from soil moisture, precipitation and temperature using maximum entropy

Abstract This study employs entropy theory to evaluate the relation of vegetation cover to soil moisture, precipitation and temperature patterns in the Texas Gulf watershed. Over a 12-year period, we consider the Normalized Differential Vegetation Index (NDVI) of the growing season (May to September) for deciduous forest and grasslands, as well as precipitation, temperature, and soil moisture data at a biweekly time scale. Using three different vegetation growth metrics, we analyze patterns in vegetation responses. An entropy scaling of the system of vegetation-soil moisture-precipitation-temperature reveals trends toward maximum entropy and shows the relevance of coupling these atmospheric variables in vegetation dynamic analysis. Our analysis indicates that soil moisture is potentially efficient to use for vegetation dynamics monitoring at finer time scales compared to precipitation. The near-surface (5 cm) soil moisture series shows meaningful relationship with vegetation growth series. This seems interesting, as the recent satellite soil moisture monitoring projects are designed for estimating near-surface moisture. Month-wise, the vegetation response to atmospheric variables shows important dissimilarities. Therefore, we use an entropy-based clustering approach to discriminate the growing season. Later, we propose a nested statistical model for retrieving an estimate of NDVI. We find that the inclusion of soil moisture and temperature explains up to 68% and 62% of the variation of NDVI, respectively, for deciduous forest and grassland during June and July. However, these relationships appear weaker during the end of the growing season (August and September). The outcomes of the study are suitable for ecosystem monitoring under the realm of climate change. Likewise, the techniques employed may find useful applications for water resources management at the scale of large watersheds. Nevertheless, further studies on the topic are necessary and should involve diversified ecosystems and remote sensed products.

[1]  Thierry Pellarin,et al.  Soil moisture mapping over West Africa with a 30-min temporal resolution using AMSR-E observations and a satellite-based rainfall product , 2009 .

[2]  R. Mojena,et al.  Hierarchical Grouping Methods and Stopping Rules: An Evaluation , 1977, Comput. J..

[3]  Peter B Reich,et al.  Predicting leaf physiology from simple plant and climate attributes: a global GLOPNET analysis. , 2007, Ecological applications : a publication of the Ecological Society of America.

[4]  Jianjun Wu,et al.  Response of NDVI dynamics to precipitation in the Beijing–Tianjin sandstorm source region , 2013 .

[5]  V. Singh,et al.  THE USE OF ENTROPY IN HYDROLOGY AND WATER RESOURCES , 1997 .

[6]  S. Suárez‐Seoane,et al.  Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution , 2008 .

[7]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[8]  Raghavan Srinivasan,et al.  Comparison of raingage and WSR-88D Stage III precipitation data over the Texas-Gulf basin , 2004 .

[9]  Nathaniel A. Brunsell,et al.  Land surface response to precipitation events using MODIS and NEXRAD data , 2008 .

[10]  M. Austin.  Species distribution models and ecological theory: A critical assessment and some possible new approaches , 2007 .

[11]  R. McMurtrie,et al.  The peaked response of transpiration rate to vapour pressure deficit in field conditions can be explained by the temperature optimum of photosynthesis , 2014 .

[12]  Dagbegnon Clement Sohoulande Djebou,et al.  Analysis of watershed topography effects on summer precipitation variability in the southwestern United States , 2014 .

[13]  W. Wagner,et al.  Evaluation of the agreement between the first global remotely sensed soil moisture data with model and precipitation data , 2003 .

[14]  Vijay P. Singh,et al.  An entropy-based investigation into the variability of precipitation , 2009 .

[15]  W. G. Price,et al.  A rational cubic spline based on function values , 1998, Comput. Graph..

[16]  Shuai Wang,et al.  Responses of soil moisture in different land cover types to rainfall events in a re-vegetation catchment area of the Loess Plateau, China , 2013 .

[17]  Y. Yamaguchi,et al.  Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982-1990 , 2002 .

[18]  G. Szeicz,et al.  SOLAR RADIATION FOR PLANT GROWTH , 1974 .

[19]  J. T. Ritchie,et al.  Soil water availability , 1981, Plant and Soil.

[20]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[21]  Vijay P. Singh,et al.  Hydrologic Synthesis Using Entropy Theory: Review , 2011 .

[22]  R. Pielke,et al.  Simulation of the effects of surface fluxes of heat and moisture in a mesoscale numerical model: 1. Soil layer , 1981 .

[23]  R. Slatyer.  The significance of the permanent wilting percentage in studies of plant and soil water relations , 2008, The Botanical Review.

[24]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[25]  D. Chessel,et al.  Analyses de la co-inertie de K nuages de points , 1996 .

[26]  Roderick C. Dewar,et al.  Maximum entropy production and plant optimization theories , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[27]  Salvador Pueyo,et al.  The maximum entropy formalism and the idiosyncratic theory of biodiversity , 2007, Ecology letters.

[28]  A. Gastón,et al.  Modelling species distributions with penalised logistic regressions: A comparison with maximum entropy models , 2011 .

[29]  S. A. Leake,et al.  Ground-water surface-water interactions and long-term change in riverine riparian vegetation in the southwestern United States , 2006 .

[30]  Rod Swenson,et al.  Emergent attractors and the law of maximum entropy production: Foundations to a theory of general evolution , 1989 .

[31]  L. A. Richards,et al.  FIFTEEN‐ATMOSPHERE PERCENTAGE AS RELATED TO THE PERMANENT WILTING PERCENTAGE , 1943 .

[32]  J. Peixoto,et al.  Entropy budget of the atmosphere , 1991 .

[33]  Steven M. Quiring,et al.  Comparison and application of multiple methods for temporal interpolation of daily soil moisture , 2014 .

[34]  Kazuhito Ichii,et al.  Global monitoring of interannual changes in vegetation activities using NDVI and its relationships to temperature and precipitation , 2001 .

[35]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[36]  S. Nicholson,et al.  A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR , 1990 .

[37]  A. Knapp,et al.  Variation among biomes in temporal dynamics of aboveground primary production. , 2001, Science.

[38]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[39]  C. Potter,et al.  Remote sensing-based time-series analysis of cheatgrass (Bromus tectorum L.) phenology. , 2010, Journal of environmental quality.

[40]  R. D. Ramsey,et al.  Mapping moderate-scale land-cover over very large geographic areas within a collaborative framework : A case study of the Southwest Regional Gap Analysis Project (SWReGAP) , 2007 .

[41]  Dagbegnon Clement Sohoulande Djebou,et al.  Vegetation response to precipitation across the aridity gradient of the southwestern United states , 2015 .

[42]  James Hansen,et al.  Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation , 2013, Stochastic Environmental Research and Risk Assessment.

[43]  Nathalie Pettorelli,et al.  Early onset of vegetation growth vs. rapid green-up: impacts on juvenile mountain ungulates. , 2007, Ecology.

[44]  C. Tucker,et al.  Coupled vegetation‐precipitation variability observed from satellite and climate records , 2003 .

[45]  Christopher A. Barnes,et al.  Completion of the 2006 National Land Cover Database for the conterminous United States. , 2011 .

[46]  Irina Krasovskaia,et al.  Entropy-based grouping of river flow regimes , 1997 .

[47]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[48]  Jeff Eldenshink,et al.  A 16-year Time Series of 1 km AVHRR Satellite Data of the Conterminous United States and Alaska , 2006 .

[49]  R.A.M. de Jeu,et al.  Using satellite based soil moisture to quantify the water driven variability in NDVI: A case study over mainland Australia , 2014 .

[50]  Alexander I. Rozhenko,et al.  A new method for finding the optimal smoothing parameter for the abstract smoothing spline , 2010, J. Approx. Theory.

[51]  D. Whitehead,et al.  Soil water availability influences the temperature response of photosynthesis and respiration in a grass and a woody shrub. , 2014, Functional plant biology : FPB.

[52]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[53]  Jean Thioulouse,et al.  ADE-4: a multivariate analysis and graphical display software , 1997, Stat. Comput..

[54]  Peter S. Eagleson,et al.  Climate, soil, and vegetation: 3. A simplified model of soil moisture movement in the liquid phase , 1978 .

[55]  C. Tucker,et al.  Northern hemisphere photosynthetic trends 1982–99 , 2003 .

[56]  D. A. Ratkowsky,et al.  A Stopping Rule and Clustering Method of Wide Applicability , 1984, Botanical Gazette.

[57]  Randal D. Koster,et al.  Soil Moisture Memory in AGCM Simulations: Analysis of Global Land–Atmosphere Coupling Experiment (GLACE) Data , 2004 .

[58]  Vijay P. Singh,et al.  Entropy Theory and its Application in Environmental and Water Engineering: Singh/Entropy Theory and its Application in Environmental and Water Engineering , 2013 .

[59]  J. Wickham,et al.  Completion of the 2001 National Land Cover Database for the conterminous United States , 2007 .

[60]  Dara Entekhabi,et al.  An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[61]  Jean Thioulouse,et al.  Simultaneous analysis of a sequence of paired ecological tables , 2004 .

[62]  Limin Yang,et al.  Development of a 2001 National land-cover database for the United States , 2004 .

[63]  Delphis F. Levia,et al.  Soil moisture: A central and unifying theme in physical geography , 2011 .

[64]  Paulin Coulibaly,et al.  Hydrometric network evaluation for Canadian watersheds , 2010 .

[65]  Chao Li,et al.  Entropy theory‐based criterion for hydrometric network evaluation and design: Maximum information minimum redundancy , 2012 .