Regionalization of Coarse Scale Soil Moisture Products Using Fine-Scale Vegetation Indices - Prospects and Case Study

Surface soil moisture (SSM) plays a critical role in many hydrological, biological and biogeochemical processes. It is relevant to farmers, scientists, and policymakers for making effective land management decisions. However, coarse spatial resolution and complex interactions of microwave radiation with surface roughness and vegetation structure present limitations within active remote sensing products to directly monitor soil moisture variations with sufficient detail. This paper discusses a strategy to use vegetation indices (VI) such as greenness, water stress, coverage, vigor, and growth dynamics, derived from Earth Observation (EO) data for an indirect characterization of SSM conditions. In this regional-scale study of a wetland environment, correlations between the coarse Advanced SCATterometer-Soil Water Index (ASCAT-SWI or SWI) product and statistical measurements of four vegetation indices from higher resolution Sentinel-2 data were analyzed. The results indicate that the mean value of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) correlates most strongly to the SWI and that the wet season vegetation traits show stronger linear relation to the SWI than during the dry season. The correlation between VIs and SWI was found to be independent of the underlying dominant vegetation classes which are not derived in real-time. Therefore, fine-scale vegetation information from optical satellite data convey the spatial heterogeneity missed by coarse synthetic aperture radar (SAR)-derived SSM products and is linked to the SSM condition underneath for regionalization purposes.

[1]  V. Radeloff,et al.  Image texture as a remotely sensed measure of vegetation structure , 2012 .

[2]  F. Baret,et al.  Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data , 2006 .

[3]  Katarzyna Dabrowska-Zielinska,et al.  Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery , 2018, Remote. Sens..

[4]  Venkat Lakshmi,et al.  Soil Moisture Remote Sensing: State‐of‐the‐Science , 2017 .

[5]  R. Scott,et al.  Measuring soil moisture content non‐invasively at intermediate spatial scale using cosmic‐ray neutrons , 2008 .

[6]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[7]  S. Prince.  A model of regional primary production for use with coarse resolution satellite data , 1991 .

[8]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[9]  Suat Irmak,et al.  Light interactions, use and efficiency in row crop canopies under optimal growth conditions , 2020 .

[10]  Wouter Dorigo,et al.  Validation of the ASCAT Soil Water Index using in situ data from the International Soil Moisture Network , 2014, Int. J. Appl. Earth Obs. Geoinformation.

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

[12]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

[13]  Mehrez Zribi,et al.  Synergic Use of Sentinel-1 and Sentinel-2 Images for Operational Soil Moisture Mapping at High Spatial Resolution over Agricultural Areas , 2017, Remote. Sens..

[14]  Thomas Alexandridis,et al.  Spatial and Temporal Distribution of Soil Moisture at the Catchment Scale Using Remotely-Sensed Energy Fluxes , 2016 .

[15]  Mac McKee,et al.  Estimation of Surface Soil Moisture in Irrigated Lands by Assimilation of Landsat Vegetation Indices, Surface Energy Balance Products, and Relevance Vector Machines , 2016 .

[16]  Yi Y. Liu,et al.  Evaluating global trends (1988–2010) in harmonized multi‐satellite surface soil moisture , 2012 .

[17]  Karsten Schulz,et al.  Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale , 2012 .

[18]  D. Deering.  Rangeland reflectance characteristics measured by aircraft and spacecraft sensors , 1979 .

[19]  Kelly K. Caylor,et al.  Validation of SMAP surface soil moisture products with core validation sites , 2017, Remote Sensing of Environment.

[20]  W. Wagner,et al.  Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture , 2012 .

[21]  M. Batistella,et al.  Exploring TM Image Texture and its Relationships with Biomass Estimation in Rondônia, Brazilian Amazon. , 2005 .

[22]  Birgit Kleinschmit,et al.  Indicator-Based Soil Moisture Monitoring of Wetlands by Utilizing Sentinel and Landsat Remote Sensing Data , 2018, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

[23]  Wade T. Crow,et al.  Effect of vegetation index choice on soil moisture retrievals via the synergistic use of synthetic aperture radar and optical remote sensing , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Jian Peng,et al.  Recent Advances in Soil Moisture Estimation from Remote Sensing , 2017 .

[25]  S. Attinger,et al.  Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale , 2010 .

[26]  Luca Brocca,et al.  Soil moisture as a potential variable for tracking and quantifying irrigation: A case study with proximal gamma-ray spectroscopy data , 2020 .

[27]  Jean-Pierre Wigneron,et al.  Sensitivity of Passive Microwave Observations to Soil Moisture and Vegetation Water Content: L-Band to W-Band , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[28]  W. Wagner,et al.  Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) , 2007 .

[29]  R. Myneni,et al.  Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .

[30]  W. Wagner,et al.  Evaluation of the ESA CCI soil moisture product using ground-based observations , 2015 .

[31]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

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

[33]  Luca Brocca,et al.  Soil Moisture from Fusion of Scatterometer and SAR: Closing the Scale Gap with Temporal Filtering , 2018, Remote. Sens..