Soil Moisture and Vegetation Water Content Retrieval Using QuikSCAT Data

Climate change and hydrological cycles can critically impact future water resources. Uncertainties in current climate models result in disagreement on the amount of water resources. Soil moisture and vegetation water content are key environmental variables on evaporation and transpiration at the land–atmosphere boundary. Radar remote sensing helps to improve our estimate of water resources spatially and temporally. This work proposes a backscattered power formulation for the Ku-band. Li et al. (2010) retrieved soil moisture and vegetation water content values using Windsat data and simultaneous collocated QuikSCAT backscattered power are used to estimate different parameters of backscatter formulation. These parameters are used to estimate soil moisture and vegetation water content using QuikSCAT power everywhere and every day during the summer season. The 2-folded cross validation method is used to evaluate the performance of soil moisture and vegetation water content retrieval. A relatively large correlation is observed between vegetation water content using WindSat and QuikSCAT data in land classes of Evergreen Needleleaf, Evergreen Broadleaf, Deciduous Broadleaf, and Mixed Forests. Similarly, the retrieved soil moisture using QuikSCAT in areas with bare surface fraction of greater than 60% shows relatively high correlation with WindSat values. QuikSCAT satellite collects data over land globally almost every day. Therefore, QuikSCAT data can be used to generate a global map of soil moisture and vegetation water content daily from 2000 to 2009.

[1]  Sassan Saatchi,et al.  Estimation of Forest Fuel Load From Radar Remote Sensing , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[2]  M. Moghaddam,et al.  Estimating subcanopy soil moisture with radar. , 2000 .

[3]  David G. Long,et al.  An Assessment of QuikSCAT Ku-Band Scatterometer Data for Soil Moisture Sensitivity , 2009, IEEE Geoscience and Remote Sensing Letters.

[4]  Yoshio Inoue,et al.  Ku- and C-band SAR for discriminating agricultural crop and soil conditions , 1998, IEEE Trans. Geosci. Remote. Sens..

[5]  Thomas J. Jackson,et al.  Estimating soil water‐holding capacities by linking the Food and Agriculture Organization Soil map of the world with global pedon databases and continuous pedotransfer functions , 2000 .

[6]  Roger D. De Roo,et al.  A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion , 2001, IEEE Trans. Geosci. Remote. Sens..

[7]  Francesco Mattia,et al.  A Time-Series Approach to Estimating Soil Moisture From Vegetated Surfaces Using L-Band Radar Backscatter , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[8]  R. Nemani,et al.  Persistent effects of a severe drought on Amazonian forest canopy , 2012, Proceedings of the National Academy of Sciences.

[9]  Thomas J. Jackson,et al.  WindSat Global Soil Moisture Retrieval and Validation , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Kamal Sarabandi,et al.  An empirical model and an inversion technique for radar scattering from bare soil surfaces , 1992, IEEE Trans. Geosci. Remote. Sens..

[11]  Konstantinos P. Papathanassiou,et al.  Polarimetric SAR interferometry , 1998, IEEE Trans. Geosci. Remote. Sens..

[12]  Thomas J. Jackson,et al.  Modeling L-Band Synthetic Aperture Radar Data Through Dielectric Changes in Soil Moisture and Vegetation Over Shrublands , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  David G. Long,et al.  Calibrating SeaWinds and QuikSCAT scatterometers using natural land targets , 2005, IEEE Geoscience and Remote Sensing Letters.

[14]  Dara Entekhabi,et al.  L-Band Radar Soil Moisture Retrieval Without Ancillary Information , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Sassan Saatchi,et al.  Coherent effects in microwave backscattering models for forest canopies , 1997, IEEE Trans. Geosci. Remote. Sens..

[16]  Seung-Bum Kim,et al.  Models of L-Band Radar Backscattering Coefficients Over Global Terrain for Soil Moisture Retrieval , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[17]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[18]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part 1: Empirical Models and Experimental Observations , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[19]  David G. Long,et al.  Global ice and land climate studies using scatterometer image data , 2001 .

[20]  Konstantinos Papathanassiou,et al.  Single-baseline polarimetric SAR interferometry , 2001, IEEE Trans. Geosci. Remote. Sens..