MICROWAVE REMOTE SENSING IN SOIL QUALITY ASSESSMENT

Information of spatial and temporal variations of soil quality (soil properties) is required for various purposes of sustainable agriculture development and management. Traditionally, soil quality characterization is done by in situ point soil sampling and subsequent laboratory analysis. Such methodology has limitation for assessing the spatial variability of soil quality. Various researchers in recent past showed the potential utility of hyperspectral remote sensing technique for spatial estimation of soil properties. However, limited research studies have been carried out showing the potential of microwave remote sensing data for spatial estimation of various soil properties except soil moisture. This paper reviews the status of microwave remote sensing techniques (active and passive) for spatial assessment of soil quality parameters such as soil salinity, soil erosion, soil physical properties (soil texture & hydraulic properties; drainage condition); and soil surface roughness. Past and recent research studies showed that both active and passive microwave remote sensing techniques have great potentials for assessment of these soil qualities (soil properties). However, more research studies on use of multi-frequency and full polarimetric microwave remote sensing data and modelling of interaction of multi-frequency and full polarimetric microwave remote sensing data with soil are very much needed for operational use of satellite microwave remote sensing data in soil quality assessment.


INTRODUCTION
Information of spatial and temporal variations of soil quality (soil properties) is required for various purposes such as soil and crop management for improving crop productivity; sustainable land use planning; soil erosion and runoff modeling in watershed management; land -atmosphere gaseous exchange study for climate change modeling; biogeochemical cycles study; precision agriculture etc. Traditionally, soil quality characterization is done by in situ point soil sampling and subsequent laboratory analysis.Such methodology has limitation for assessing the spatial variability of soil quality.Furthermore, there is growing recognition of the varying spatial scales over which soil properties vary, leading to a clear need for field or watershed scale measurement (Anderson and Croft, 2009).
The advantages of aero-space remote sensing techniques using optical sensors for non-destructive spatial assessment of soils characteristics have been recognized.Various researchers in recent past showed the potential utility of imaging spectrometry based remote sensing technique for spatial estimation of soil properties (reviewed by Ben-Dor et al., 2008).However, limited research studies have been carried out showing the potential of microwave remote sensing data for spatial estimation of various soil properties except soil moisture.This paper reviews the status of microwave remote sensing techniques (active and passive) for spatial assessment of soil quality parameters such as soil salinity, soil erosion, soil physical properties -soil texture & hydraulic properties; drainage condition; and soil surface roughness.

MICROWAVE REMOTE SENSING OF SOIL
QUALITY (SOIL PROPERTIES)

Soil Salinity
Soil salinity is one of the major soil degradation problems that affect crop growth and productivity.The identification of type and severity of salt affected soils with their location and areal extent is necessary for reclamation of the salt affected soils.
Although, the delineation of salt affected soils is possible using optical remote sensing data, the delineation of salt affected soils is difficult in coastal areas and desert areas and in the black clay rich soils region because of spectral mixing with sand and poor spectral contrast in black soils region.
Radar is known to be sensitive to several natural surface parameters such as vegetation, surface roughness and dielectric constant (e) (Bell et al., 2001).The dielectric constant is comprised of the permittivity or real part and the loss factor or imaginary part.Research studies indicated that soil salinity has no influence on the real part of the dielectric constant (İ ' ) whereas; the imaginary part (İ " ) is dependent and increases with increase in salinity for all three textured soils.(Ulaby et al., 1986;Sreenivas et al. 1995).Bell et al. (2001) used the airborne polarimetric SAR for mapping soil salinity.The three dielectric retrieval algorithms, the SPM (Small Perturbation Model), PO (Physical Optics) & DM (Dubois Model) were implemented and the results of these were combined to retrieve an improve estimate of the magnitude of the imaginary part of the complex dielectric constant for soil salinity discrimination Temporal Envisat polarimetric SAR data (VV & HH) (ASD, 2006) was used in a research case study to map spatial soil salinity variation in part of Unao district of Uttar Pradesh.The three dielectric constant retrieval algorithms such as SPM, PO DM were used and the results of these were combined to retrieve the magnitude of the imaginary part of the complex dielectric constant for soil salinity class discrimination and mapping (Fig. 1).

Ravine Erosion Inventory
Optical multispectral and high-resolution panchromatic data are generally utilized for delineating and mapping severally eroded (ravine affected) area and its broad classification into different depth categories principally based on visual interpretation of the image characteristics.However these methods are qualitative in nature.Due to high sensitivity of microwave SAR to terrain ruggedness and vegetation penetration capability (sparse bushy vegetation and grasses), microwave remote sensing techniques such as SAR data and the InSAR (Interferrometric SAR) has the advantages of delineation and characterization of ravines as a function of ravine density, ravine depth, and ravine surface cover in quantitative terms.
The results of a research study (Chatterjee et al 2009) carried out in part of Chambal river valley, U.P. showed that the ravine density map(Fig.3)., prepared using the local statistics-based textural measure of speckle-suppressed ERS-1 SAR amplitude image matched well with ground observations (Fig. 2).
Map (Fig. 5) showing three ravine depth classes, namely, shallow (<5 m), moderately deep (5 -20 m), and deep ravines (>20 m), was also prepared using InSAR DEM (Fig. 4) which was generated from an ERS SAR tandem pair data.Moderately deep ravines were found to be the most widespread in the study area.
Ravine surface land cover classes viz., barren land, grass/scrub/ crop land, sparse vegetation, and wet land/dense vegetation, were delineated and mapped based on the temporal decorrelation properties (coherence) in repeat pass InSAR data.

Sand Dune Characterization
Sand dunes cover up to a quarter of many desert regions.Fixed and semi fixed sand dunes are distributed around the desert periphery, and the mobile dunes are distributed mainly in the inner part of the deserts.Information about the dune attributes is very important for understanding environmental changes in arid regions.In general, desert regions are difficult to access.
Remotely sensed data are therefore very useful for monitoring desert environments.
The emphasis of this study was on, study the radar backscattering from the linear dunes surfaces to extract dune attributes such as dune height, inter dune spacing, and dune direction.These studies showed that dune height can accurately be estimated using RMS slope parameter derived from SAR satellite data.Two approaches are generally employed to derived RMS slope parameter from SAR backscatter image -(i) generation of image showing difference in local incidence angle corresponding to backscatter variation and radar incidence angle; (ii) Fast Fourier Transformation (FFT) and generation of power spectrum (Fig. 6).
The spectrum derived from the JERS-1 SAR image exhibited several major peaks, and the central part of the peaks was also consistent with the local prevailing wind direction.The inter dune spacing was calculated using the wave number k of the spectra.
The dune heights derived from the present algorithm agreed fairly well with the dune heights observed in the field investigation.

Soil texture and Hydraulic Properties
An accurate estimation of spatially variable soil physical properties such as texture and hydraulic properties is necessary to develop reliable models of water flow and transport throughout the soil-plant-atmosphere continuum, for efficient management of soil resources for improving crop productivity, and for maintenance of environmental quality.Measurements of these soil physical properties are time-consuming and expensive.In addition, a large number of measurements are necessary to quantify their space-time variability.
Several research studies showed that spatial variability of soil texture and hydraulic parameters could be assessed using temporal microwave remote sensing derived changes in brightness temperature and soil moisture content ( Camillo et al. 1984;Mattikalli et al.1995;Mattikali et al. 1997;Mattikalli et al.1998;Chang and Islam, 2000;Santanello et al. 2007).The results of these studies indicated that close relationships existed between space-time evolution of remotely sensed passive microwave brightness temperature and soil moisture and soil types with respect to soil textural class (Fig. 7), and such a relationship can be used to identify soil texture and saturated hydraulic conductivity (K sat ) from a sequence of remotely sensed images of brightness temperature and remotely sensed soil moisture.
Chang and Islam (2000) followed three layered Feed -Forward -Neural Network (FFNN) approach for estimation of soil texture using satellite derived microwave brightness temperature and soil moisture status (Fig. 8).
The FFNN consists of three layers of neurons: an input layer, a hidden layer, and an output layer.The input neuron receives and delivers the signal without changing it.The output neuron weights and sums the coming signals, and then the net result is passed through a linear activation function.The hidden neurons are similar to the output neurons except that a binary sigmoid is used as the activation function.
Ksat (Saturated hydraulic conductivity) is an important soil property that is difficult to obtain other than in a laboratory.Therefore, any methods based on remote sensing that have capabilities of deriving spatial distribution of Ksat would be an extremely important data source for hydrologic applications (Mattikali and Engman, 1997).They studied the changes in soil moisture derived from microwave brightness temperature and relate it with soil profile harmonic mean Ksat derived from hydrologic model simulations.(Qong, 2000).quarries, urban; 8, gypsum; 9, water).[Chang and Islam (2000)]

Soil Drainage
Among the various soil properties, soil drai directly affects plant growth, water flow a soils.Drainage refers to the natural ability to infiltrate and percolate.Drainage ma because soil map users usually need in properties or soil behaviour rather than taxo use and management decision.For microw the magnitude of radar backscattering fr governed by the dielectric constant and so The dielectric constant in turn, is depen moisture content and, to some extent, on soi Therefore, radar remote sensing has the properties, such as soil drainage.2002) studied practices on polarimetric SAR r data.This study examined the and polarimetric parameters pedestal height, and co-polar Results indicated that the dom the fields varies depending on cover, and whether the cro parameters most sensitive to perform best at characterizing parameters are the pedestal he polarization (HV) and the circu polarization signature plots and with the PPD are also useful i (Fig. 13).

CONC
Past and recent research studi passive microwave remote potentials for assessment of lan several soil characteristics such parameters, soil erosion condit and sand dunes), drainage cond  (Rahman et al, 2008).Baghdadi et al (2008) have given ns of various remote sensing face roughness.d a methodology for simultaneous oisture using multi temporal and ers data following inversion of M) -a physical radar back scatter r back scatter as a function of ughness of the medium.
the effects of various soil tillage response using SIR-C polarimetric sensitivity of linear polarization (circular polarized backscatter, rized phase differences (PPD)).minant scattering mechanism from n the type and amount of residue op had been harvested.Radar volume and multiple scattering g these surface conditions.These eight, as well as the linear crossular co-polarization (RR).The cod the standard deviation associated in categorizing these cover types CLUSIONS ies showed that both active and sensing techniques have great nd degradation and estimation of h as salinity, texture and hydraulic tions (characterization of ravines ditions, soil surface roughness and International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVIII-8/W20, 2011 ISPRS Bhopal 2011 Workshop, 8 November 2011, Bhopal, India tillage conditions etc.However, more research studies on use of multi-frequency and full polarimetric microwave remote sensing data and modelling of interaction of multi-frequency and full polarimetric microwave remote sensing data with soil are very much needed for operational use of satellite microwave remote sensing data. Fig.2: Multi-look (5 x 1) backscattering ERS1 SAR data of the study area -after G filtering in order to suppress the micro te highlighting the meso and macro textures.

Fig. 9 :
Fig. 9: Relationships between profile harmonic-mean Ksat derived from hydrologic model simulations and two-day initial changes in surface soil moisture obtainable from microwave remote sensing (Mattikali and Engman, 1997).

Fig. 10 :
Fig. 10: Within-field soil drainage mapping using canonical variates derived from a) hyperspectral reflectance principal components (HR PCs), b) C-Band SAR linear HH and VV polarizations,and c) apparent soil electrical conductivity within 0-30 cm (ECa30) and 0-100 cm (ECa100) depths.A higher drainage index represents a better drainage condition.

Fig 11 :
Fig 11: Roughness map (h RMS in cm) derived from radar images by the use of IEM, as formulated in Eq. (4).The solid line represents the boundary of USDA ARS Walnut Gulch Experimental Watershed (Rehman et al 2008).

Fig 12 :
Fig 12: Roughness map (Lc in cm) derived from radar images by the use of IEM, as formulated in Eq. (4).The solid line represents; the boundary of USDA ARS Walnut Gulch Experimental Watershed Rehman et al (2008).