The above table shows that in coastal regions specially where there is regular accumulation of sea water in high tides, only few exotic plant species with high salinity tolerance levels can thrive well.
Increased soil salinity not only causes less water intake in plants but also leads to acute nutrient imbalances (Hu & Schmidhalter, 2005). This often causes toxins detrimental for plant growth to accumulate and reduction in water infiltration in the event of high sodium ion (Na+) concentration(Qadir & Schubert, 2002). Statistics reveal that globally about 1 billion hectares, close to 7% of earth’s continental crust is salinity affected (Lewis & Maslin, 2015). Soil salinity can occur in any soil type anywhere on earth. However, semi-arid and arid regions are the worst affected (Jordán, et al., 2004). Soil salinity depends upon (Jolly, McEwan, & Holland, 2008)-
Salt could be found along with parent material like rock or salt layers accumulated over time.
Leaching and weathering of parent rock materials causes the free ion accumulation in soil thereby increasing soil salinity.
During erosion by wind or water, soil salinity can occur by materials brought by erosive forces from one area to another.
In case when the underground water becomes saline due to prolonged leaching, the places where the water table is near the surface soil becomes saline after long spells of dryness and evaporation from surface.
Most common cause of soil salinity is however the unscientific flood irrigation. The irrigation water from tube-wells or canals if containing dissolved salts often leads to soil salinity or increase in the salinity levels of soil. Remote sensing is a non-evasive and time saving tool which can be applied effectively for monitoring of soil salinity levels and mapping of salinity affected areas (Katsaros, Vachon, Liu, & Black, 2002). Since many decades and ever since the inception of remote sensing as an advanced surveying tool with launch of first remote sensing satellite- Landsat-1 in early 1970s; spaceborne remote sensing found extensive use in agriculture (Zhang et al., 2002). However, there was still paucity of dedicated use of spaceborne remote sensing for soil health studies (Pampaloni & Calvet, 2007; Wagner, Lemoine, & Rott, 1996). Initial soil health studies were done in conjunction with remote sensing research for precision agriculture using optical multispectral satellite datasets (Viterbo & Betts, 1999; Y. Xie, Sha, & Yu, 2008). Since the optical, thermal, and hyperspectral datasets have their own constraints owing to their non availability every time and in all seasons due to dense cloud covers specially in monsoon months, a need for all weather data was felt (Kim & Hong, 2007). It was due to this that microwave or Synthetic Aperture RADAR (SAR) remote sensing came into use (Hong & Pan, 2000). It has a penetration ability and being an active sensor, it has high temporal resolution with 24-hour data availability (Holmes, 1959; Liu et al., 2011). Apart from this, owing to its several modelling and decomposition approaches, it becomes the most versatile branch of remote sensing research (Engman, 1991; Larson et al., 2010). But spaceborne remote sensing has its own limitations (Pachepsky, Guber, & Jacques, 2005). High resolution optical datasets are mostly available on commercial and paid basis and almost all currently active SAR Datasets are available at very high costs (Delaney, 1974; Walker, Houser, & Willgoose, 2004). This often causes budgetary constraints on advanced research. Hence a cost-effective study is needed to carry out research that is feasible and at par with those conducted using commercial data. Moreover, SAR data has lots of speckle noise which is absent in optical data (Chong & T, 2005). Keeping all the limitations and constraints in view, this study was conducted using synergy of both optical and SAR remotely sensed and freely available high temporal resolution satellite data for soil salinity estimation and modelling. This study uses backscatter coefficients generated after calibrating the SAR data from Sentinel-1 checks their sensitivities for soil moisture and electrical conductivity values collected from ground along with NDSI calculated from Sentinel-2 optical multispectral data of the same dates (David R . Anderson, 2000; Pope & Webster, 1972). Also, surface temperature data in Fahrenheit was also used using a thermal imager on ground during field data collection (Congalton, Fenstermaker, & Mcgwire, 1991; Richardson & Hollinger, 2005). This study is unique in the sense that it uses a multisensor remote sensing approach along with a synergy of field data to be fed into developing a Ordinary Least Squares (OLS) model for estimation of soil salinity (Dekker, 1998; Sowter et al., 2016).1.1 Short Literature reviewRemote sensing is defined as the science and art of data collection, processing, interpretation, and analysis of data from a distance without coming in actual tangible contact with the target object (Guzha, 2004; Metternicht & Zinck, 2003). In optical remote sensing, the target is illuminated by the Sun’s rays and the reflectance is captured by the sensors of the satellite operating in narrow band ranges of the electromagnetic spectrum and capture imagery in several wavelength band ranges (Herrick, 2000; Stenberg, Rossel, Mouazen, & Wetterlind, 2010; K. T. (eds). 2013 Wymann von Dach S, Romeo R, Vita A, Wurzinger M, 2014). In microwave remote sensing, the sensor being an active sensor, illuminates the target with emitted electromagnetic waves in the microwave region (Raina, Joseph, & Haribabu, 2010; K. T. (eds). Wymann von Dach S, Romeo R, Vita A, Wurzinger M, n.d.). The waves are received back after interaction with the target and are received on the receiver of the sensor platform which generates an image of the target object (Braidwood, 1960; Muir, Pretty, Robinson, Thomas, & Toulmin, 2010). Remote sensing data is collected from a variety of sensors like visible and infrared sensors, optical and thermal imaging sensors, hyperspectral sensors and microwave sensors and based on the respective behavioural properties of salinity effected areas with the incident radiation, mapping of soil salinity is done (Schmugge et al., 2002). In remote sensing applied to land resource surveys, wavelengths between 0.4-1.5mm are most used (Aslan et al., 2016). Landsat TM (Thematic Mapper) and SPOT were the satellites that found usage widespread for natural resource mapping for landscapes spread over hectares of land (Akshar Tripathi, 2018). The image type depended upon not only the purpose but also on the sensor used and the number of spectral bands it offered (Lobell et al., 2015), with Landsat providing much greater number of spectral bands than SPOT. Landsat images were used for classification, both supervised and unsupervised (Dengsheng Lu, Tian, Zhou, & Ge, 2008). Multispectral bands 3,4,5 is used along with TM bands 7 for the proper mapping of salinity effected soils as described by (Hari Shanker Srivastava et al., 2008). Davidson & Finlayson, 2007; Mahlke, 1996, used synergy of thermal and microwave remotely sensed data and used RADAR backscatter for fresh and saline water and surface temperature as parameters to model for soil salinity. (Leckie, 1984) found that Landsat bands 1-5 and 7 are sensitive for soil minerals and are good for mapping when salinity causing minerals are dominant in soil also soil salinity affects the thermal properties of the soil. (Anderson & Croft, 2009) used Landsat MSS data to produce maps for calcareous, gypsiferous and clayey soils and also found the TM bands helpful when used with aerial photographs for arid and semi-arid regions (Mougenot, Pouget, & Epema, 1993). Sharma, Saxena, & Verma, 2000 used topographic survey maps and standard False colour composite from Landsat MSS imagery to map saline and non-salinity affected areas. Saha, Kudrat, & Bhan, 1990, used digital image classification using Landsat data and successfully mapped saline, non-saline and moderately saline areas which were waterlogged for a long period of time, with 96% accuracy in classification. Similar classification study was done by D Lu & Weng, 2007. Calvão & Palmeirim, 2004 used band rationing and proved that in Middle to Near Infrared bands from Landsat were useful in mapping chlorosis affected soils. Mougenot et al., 1993, used thermal infrared band and found that the hygroscopic properties of soil can be analysed and the reflectance from leaves of plants depends upon the chemical composition of the dissolved salts in the up taken water and morphology of plant. Hansen, Dubayah, & Defries, 1996, found classification tree was useful when Normalised Differential Vegetation Index (NDVI) is used along with brightness index for mapping of soil salinity affected areas in Morocco and Pakistan Respectively. Ahmed & Luis, 2010, used multiple regression analysis using electrical conductivity values from field and based on that generated a soil salinity map for entire Mexico. Mayaux et al., 2004 used NDVI and Surface Energy Balance Algorithm for Land (SABAL) to map and classify the soils based on the various salinity levels. It is clear from the studies above that most of the soil salinity studies conducted were of qualitative classification and mapping based. There are only few dedicated studies for soil salinity estimation and modelling using multiple remote sensing sensors in a quantitative way. Most of the studies conducted for soil salinity estimation using remotely sensed data have been done at surface level since remote sensing is a surface phenomenon. But after going through literature, it was found that it is the sub-surface soil salinity that affects the plant growth as the salts underneath get trapped in root nodules and prevent further moisture intake by plants. This study is one of the few studies which estimate soil salinity in terms of electrical conductivity using remotely sensed SAR and optical data in synergy with field data. Apart from this, it was also aimed to provide a simple and robust soil salinity estimation approach, in this study. Early stage of wheat crop growth was chosen owing to the presence of more exposed area of soil to the satellite sensor during it pass. Moreover, C-band satellite data from Sentinel-1 cannot penetrate the vegetation cover once the crop matures. Hence study of soil becomes difficult.1.2 Electrical Conductivity as Soil Salinity IndicatorThe amount of electrical current that a material allows to pass through it, or the current carrying capacity of a material is defined as its electrical conductivity(Frackowiak, 2001). Electrical conductivity is also known as specific conductance and is measured in mS.m-1 (milli Siemens per meter)(Riffat & Ma, 2003). Electrical conductivity of soil correlates with soil properties like soil texture, cation exchange capacity, salinity, as a measurement of current conducting capacity of soil (Landauer, 1978). Since soil salinity refers to the concentration of ions in the soil pore water that make the further water up take difficult, the laboratory determination of soil salinity is a cumbersome process which is time taking(Corwin & Lesch, 2003). Electrical conductivity measurement as an indicator of soil salinity is an effective and time saving method for soil salinity estimation(Rhoades, 1993). The cause of the electrical conductivity is the ions present in the soil which become more loosely bound to the soil pores in presence of moisture owing to the high dielectric constant of water (Corwin & Lesch, 2005). The ions get aligned once the electric current is applied to soil, which are otherwise in random state in the soil(Rhoades & Miyamoto, 1990).1.3 SAR Backscatter coefficientsBackscatter is defined as the RADAR signal that returns to the receiver of the antenna after interaction with target under observation(Allbed & Kumar, 2013). The coefficient of scatter of RADAR signal in RADAR direction is called backscatter coefficient, represented by sigma nought σ0(Verhoest et al., 2008). It is the RADAR backscatter per unit area of the distributed target with which the incident signal interacts. It is measured in degree decibels -dB ( Tripathi, Maithani & Kumar, 2018; Shashi, 2019; Tripathi & Maithani, 2018). Beta nought (β0) is the brightness coefficient of the RADAR. It is a dimensionless quantity. Gamma nought (γ0) is the RADAR backscatter coefficient dependent upon RADAR Brightness and incidence angle and is suitable for volume Scatterers(Saha, 2011; Tripathi and Tiwari, 2019).1.4 Problem StatementThe prime objective of the study is to estimate the soil salinity in early stage of wheat crop growth at sub-surface level in a simple and robust way with freely available satellite datasets while maintaining the accuracy good enough.2. Study Area and Datasets2.1 Study AreaRupnagar district of Punjab state in India is the study area for this study. Punjab has been the cradle of India’s first green revolution and has been a primarily agriculture dependent state(Kumar, Kumar, & Mittal, 2004). Though in terms of annual rainfall, it lies in the semi-arid zone but owing to the nutrient rich alluvial soil brought down from the Himalayas by the five rivers and extensive canal and tube-well network for irrigation, Punjab leads the country in wheat production. Along with Haryana, it is the wheat bowl of the country. Rupnagar lies to the north of India’s first planned city after independence- Chandigarh. Rupnagar is a district and headquarters of Rupnagar division of Punjab(Levinson et al., 2004). Rupnagar located on the banks of river Sutluj is one of the prominent sites of the erstwhile Indus Valley Civilization. Located between 30.970 N and 76.530 E, Rupnagar’s average elevation is 260m above mean sea level and is bordered by the Shivalik hills of the mighty Himalayas in North and North-East(Kumar et al., 2004; Levinson et al., 2004).2.2 DatasetsSAR/Microwave remote sensing data from Sentinel-1 satellite of the European Space Agency (ESA), Optical multispectral remote sensing data from Sentinel-2 satellite of the ESA. The datasets were acquired from the Alaska Satellite Facility (ASF) which is a freely available datasets for download from this portal. The data used were of 20th November and 27th December 2019 and 20th January 2020, which were the days of satellite pass over Rupnagar. It was decided to acquire the field data of volumetric soil moisture and Electrical Conductivity (EC) simultaneously on the same dates at 60 cm depth. The following table (Table 2) gives details of Datasets used-Table 2. DATASETS USED