Water balance at plot scale for soil moisture estimation using vegetation parameters

Abstract The experiment presented and discussed in this paper attempts to understand the relationships between vegetation cover measurements and soil moisture estimations, to improve soil moisture calculations based on the FAO56 water balance, also known as the K c - ET 0 (crop coefficient-reference evapotranspiration) approach. The experiment provided a detailed dataset of in situ measurements, such as soil moisture, vegetation parameters, and spectral and meteorological measurements. The experiment was performed in two 40 m 2 plots of barley and grass. The study primarily addressed the relationships among vegetation canopy parameters. The results showed that the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Vegetation Water Content (VWC), and Fraction of Vegetation Cover (FVC) were strongly correlated. Based on these relationships, different approaches for calculating the basal crop coefficient ( K cb ) were tested, and their influences on the water balance calculation were evaluated. The water content of the soil layer ( θ ) was calculated in the water balance as a residual of the daily calculation. Actual Evapotranspiration (AET) was also estimated. The results were validated by comparing the field soil moisture values against the soil moisture calculated through FAO56. The results of this comparison were satisfactory for barley (determination coefficient, R 2  = 0.73 and root mean square error, RMSE = 0.084 cm 3  cm −3 ) and grass ( R 2  = 0.77, RMSE = 0.058 cm 3  cm −3 ). After testing other formulations of the K cb using the LAI and FVC, the results showed that an indirect estimation of the K cb based on a growing parameter, such as the LAI, NDVI or FVC, was adequate for estimating the soil moisture through FAO56. The different approaches used to estimate the K cb had little impact on the estimate of AET (the differences were smaller than 3 mm in the total annual AET for both crops). However, the root depth and the upper limit of potential water content (field capacity) were crucial in the soil moisture and AET estimations. If the field capacity of soils, determined in the laboratory, was forced to match the upper limit of the observed soil moisture in the field, a much better estimate was obtained ( R 2  = 0.92 and RMSE = 0.035 cm 3  cm −3 for barley and R 2  = 0.91 and 0.023 cm 3  cm −3 for grass); otherwise, the AET increased by 50%.

[1]  Martha C. Anderson,et al.  Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery , 2004 .

[2]  V. Arora,et al.  Model-based assessment of water balance components under different cropping systems in north-west India , 2002 .

[3]  Ray D. Jackson,et al.  Soil Moisture Inferences from Thermal-Infrared Measurements of Vegetation Temperatures , 1982, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Alfonso Calera,et al.  Combining remote sensing and in situ soil moisture data for the application and validation of a distributed water balance model (HIDROMORE) , 2010 .

[5]  J. Zadoks.  A decimal code for the growth stages of cereals , 1974 .

[6]  Benoît Duchemin,et al.  Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region , 2007 .

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

[8]  Yanlian Zhou,et al.  Combining an ecological model with remote sensing and GIS techniques to monitor soil water content of croplands with a monsoon climate , 2010 .

[9]  Limin Yang,et al.  An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains , 1998 .

[10]  Alfonso Calera,et al.  A procedure for obtaining green plant cover: Relation to NDVI in a case study for barley , 2001 .

[11]  D. K. Cassel,et al.  Field‐Measured Limits of Soil Water Availability as Related to Laboratory‐Measured Properties , 1983 .

[12]  Zhanqing Li,et al.  Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter , 2006 .

[13]  M. Schaap,et al.  ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions , 2001 .

[14]  P. Salter,et al.  THE AVAILABLE-WATER CAPACITY OF A SANDY LOAM SOIL: I. A CRITICAL COMPARISON OF METHODS OF DETERMINING THE MOISTURE CONTENT OF SOIL AT FIELD CAPACITY AND AT THE PERMANENT WILTING PERCENTAGE , 1961 .

[15]  Luciano Mateos,et al.  Spectral vegetation indices for benchmarking water productivity of irrigated cotton and sugarbeet crops , 2008 .

[16]  José González-Piqueras,et al.  Water use of spring wheat to raise water productivity , 2009 .

[17]  Philip Lewis,et al.  On the potential of CHRIS/PROBA for estimating vegetation canopy properties from space , 2000 .

[18]  Nader Katerji,et al.  Modelling evapotranspiration of six irrigated crops under Mediterranean climate conditions , 2006 .

[19]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[20]  José González-Piqueras,et al.  Assessing satellite-based basal crop coefficients for irrigated grapes (Vitis vinifera L.) , 2010 .

[21]  H. A. Mooney,et al.  Maximum rooting depth of vegetation types at the global scale , 1996, Oecologia.

[22]  K. Rushton,et al.  Improved soil moisture balance methodology for recharge estimation , 2006 .

[23]  Anne Verhoef,et al.  The diurnal course of soil moisture as measured by various dielectric sensors: Effects of soil temperature and the implications for evaporation estimates , 2006 .

[24]  Yi-Bo Luo,et al.  A consolidated evaluation of the FAO-56 dual crop coefficient approach using the lysimeter data in the North China Plain , 2010 .

[25]  Luca Testi,et al.  Evapotranspiration of a young irrigated olive orchard in southern Spain , 2003 .

[26]  Jiwang Zhang,et al.  Root growth, available soil water, and water-use efficiency of winter wheat under different irrigation regimes applied at different growth stages in North China , 2010 .

[27]  Douglas J. Hunsaker,et al.  Wheat basal crop coefficients determined by normalized difference vegetation index , 2005, Irrigation Science.

[28]  A. Chehbouni,et al.  Monitoring wheat phenology and irrigation in Central Morocco: On the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices , 2006 .

[29]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[30]  J. Martínez-Fernández,et al.  Mean soil moisture estimation using temporal stability analysis , 2005 .

[31]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[32]  G. A. D. Medeiros,et al.  Crop coefficient for irrigated beans derived using three reference evaporation methods , 2005 .

[33]  Shaozhong Kang,et al.  Crop coefficient and ratio of transpiration to evapotranspiration of winter wheat and maize in a semi-humid region , 2003 .

[34]  Richard G. Allen,et al.  Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. , 2000 .

[35]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .

[36]  J. González-Piqueras,et al.  Monitoring barley and corn growth from remote sensing data at field scale , 2004 .

[37]  P. Pinter,et al.  Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index , 2003, Irrigation Science.

[38]  A. Huete,et al.  Leaf area index and normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River , 2004 .

[39]  H. Snyman.  Root studies on grass species in a semi-arid South Africa along a soil-water gradient , 2009 .

[40]  Michael J. Savage,et al.  Field, laboratory and estimated soil-water content limits , 2007 .

[41]  L. Alakukku,et al.  Root growth dynamics and biomass input by Nordic annual field crops , 2005 .

[42]  T. Jackson,et al.  Remote sensing of vegetation water content from equivalent water thickness using satellite imagery , 2008 .

[43]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[44]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[45]  Yongqiang Zhang,et al.  Integration of MODIS data into a simple model for the spatial distributed simulation of soil water content and evapotranspiration , 2006 .

[46]  W. Bausch,et al.  Crop Coefficients Derived from Reflected Canopy Radiation: A Concept , 1987 .

[47]  José Martínez-Fernández,et al.  Temporal Stability of Soil Moisture in a Large‐Field Experiment in Spain , 2003 .

[48]  Martha C. Anderson,et al.  A comparison of operational remote sensing-based models for estimating crop evapotranspiration , 2009 .

[49]  Richard L. Snyder,et al.  New crop coefficients estimate water use of vegetables, row crops , 1998 .

[50]  M. S. Moran,et al.  Assessing the Spatial Distribution of Evapotranspiration Using Remotely Sensed Inputs , 1991 .

[51]  S. Gandia,et al.  Analyses of spectral-biophysical relationships for a corn canopy , 1996 .