Desertification is a prolonged type of land degradation which converts the
productive ecosystem to a fragile one by two crucial factors, namely, climate and
negative human intrusion. The present study concentrates on identifying the
causative factors of desertification, namely temperature, wind, rainfall
scarcity and human pressure. It also concentrates on distinguishing the desertified land from
degraded land and assessing the way in which the soil degradation process
becomes accelerated by these factors, by employing data sets such as
meteorological data and Landsat ETM
Desertification is defined as the “degradation of the soil, landscape and bio-productive terrestrial system, in arid, semiarid and subhumid areas resulting from several factors including climate change and human activities” (UNCCD, 1994). Desertification is generally perceived as a “slow” hazard in semiarid regions, initially induced by climatic factors, and becoming accelerated when combined with human actions (population pressure, intensive land use, improper land management etc.) and the active state of parent material prevailing in a longer time frame (Lin and Tang, 2002), which causes an adverse effect on people and the ecological system. Drought-induced desertification is aggravated due to the spatiotemporal variations in rainfall, temperature, wind and solar radiation (D'Odorico et al., 2013) and human-induced desertification is exacerbated due to the ever-increasing population, along with food and fodder demand, which results in socioeconomic pressure on the land (Wang et al., 2010). This situation demonstrates that desertification is not only a global concern but also a local problem (Salvati et al., 2013) which has to be addressed in order to mitigate the desertification process (Fleskens and Stringer, 2014).
Globally, this phenomenon affects about 1.9 billion hectares of land and 250 million people (Low, 2013). Among many events which affect earth's environment and ecosystem, drought often has a direct association with desertification (Shewale and Shravan, 2005). Poor land management during periods of unusually dry weather can cause loss of vegetation, which in turn leads to desertification (Sharmia, 2008). Desertification is the end stage of the land degradation process (Hill et al., 2005) which ultimately affects the economical and biological productivity of the land (Izzo et al., 2013) and leads to economical stress to the vulnerable population (Bisaro et al., 2014) by degrading soil fertility completely.
Though desertification-inducing factors had an immense negative effect on many environmental components, soil is a prime component that becomes deteriorated by the process. Land degradation caused by the removal of vegetation is perceived as a consequence of soil degradation (Akhtar-Schuster et al., 2011). Once the physical, chemical and biological properties of the soils start degrading, natural regeneration is not possible in a human lifespan (UNCCD, 2012); hence soil is termed as a nonrenewable resource. Roxo et al. (2001) have defined desertification in relation to soil degradation through the continuous loss of soil fertility by damage of structure and composition of the soil which ultimately affects the sustainable agricultural production. Since desertification impacts soil directly, it is necessary to identify the prime causative factors which accelerate desertification and evaluate the soil degradation process which is stimulated by these factors, in order to protect this precious resource before it loses its capacity entirely.
Remote sensing technology is successfully applied to the process of monitoring desert expansion and the assessment of factors that cause desertification (Hanan et al., 1991). In recent days, a range of desertification processes has been successfully analyzed and addressed, through the application of remote sensing techniques (Barbero-Sierra et., 2015; Miao et al., 2015; Wang et al., 2015; Torres et al., 2015; Yan et al., 2015; Xu et al., 2015). The land degradation of arid and semiarid zones, often called desertification when it is irreversible in form, and the main factors of this phenomenon being either climate- or human-induced, has been much debated since the mid-1970s (Rasmussen et al., 2001). Climate fluctuations and human activities together induce desertification in semiarid regions; their individual impacts should be assessed in detail in order to resolve the ambiguity over which of them is the primary cause (Runnstrom, 2003; Wang et al., 2006). Besides rainfall, temperature and wind are also important climatic factors which can accelerate desertification through the process of evaporation. Wind speed, combined with temperature, is a key element to assess the moisture stress of exposed soil and vegetation in arid and semiarid regions. Soil moisture stress results from the occurrence of high daytime wind speed (Jacobson, 1999). The higher wind speed does not help the soil and plants to retain moisture content, and thus contributes to strong vegetation decline. Weishou et al. (2011) found a strong negative correlation between the Normalized Difference Vegetation Index (NDVI) and mean wind speed, which shows that wind speed and vegetal degradation effects are dependent on each other.
The rate of rainfall distribution in the western part of the study region is hampered by the Western Ghats, as it is situated on the leeward side, which results in poor vegetal status over a period of time. Therefore the influence of temperature and wind is expected to be dominant in the western part of the study region. The condition is reversed in the eastern part of the study area, where the rate of rainfall is adequate and temperature is moderate. There have not been many reports that emphasize the identification of the accelerating factor of desertification by considering wind and temperature along with NDVI and rainfall so far. This is the first time the spatial correlation models have been developed with the combination of wind speed, NDVI and rainfall in order to predict the wind–temperature, rainfall–temperature (drought) and human-affected regions of desertification.
Furthermore the present study identifies and extracts the completely desertified area from the degraded area with the help of the recent short-term negative vegetation trend, which was lacking in the previous studies. The reason behind the incorporation of the recent short-term vegetal trend in the present study is that “in semiarid regions the ecosystem retains its consistency and ability to respond to the recurrence of the rainy season” (Kassas, 1977). If the degraded land does not respond to adequate rain, the situation is termed as desertification.
Desertification-inducing factors (temperature-, wind-, rainfall- and human-inducing factors) have an immense effect on soil fertility in semiarid regions. After the identification of the causative factors and extraction of desertified lands, the study also focuses on the crucial and suspected degradation components invoked by four base factors on the desertified lands that make the soil of these regions entirely unproductive/irreversible. The study region has naturally inherited high fluoride content from the groundwater. A positive correlation between fluoride and pH indicated that alkaline conditions improved the solubility of fluoride (Adhikary et al., 2014). Hence, the groundwater salinity has been carried to the subsurface and some surface levels of the soil through capillary action, and during the extreme temperature event, the saline water is evaporated and salt remains accumulated. The climatic factors such as temperature, rainfall and wind have been combined with the active presence of natural fluoride, which greatly influences the soil desertification process.
According to the facts, the theoretical prediction is formulated as the area that is affected by desertification due to the high rainfall–temperature/drought factor which should have definitely experienced soil salinity because of extreme temperatures, native fluoride content and soil moisture stress at surface and subsurface level (root zone) due to rainfall scarcity; similarly, the desertified area due to high wind speed would have experienced soil moisture stress at the surface level.
Hence the testable directional hypothesis was formed in order to prove the above-stated theoretical prediction. A directional hypothesis is usually formed in such a way that predicts the specific relationship between the components and direction of that relationship. After the execution of the testable hypothesis, the empirical multispectral models have been proposed in the current study to extract the soil salinity and moisture stress from the images with reasonable accuracy.
The prime objective of the study is (i) to identify and differentiate different zones of degradation and desertification with respect to rainfall–temperature/drought, wind and anthropogenic factors using a geostatistical model; (ii) to form the hypothesis in such a way that proves the theoretical prediction of what soil degradation process can be expected from each zone of desertification; (iii) to quantify and assess the possible soil degradation processes, namely soil moisture stress and salinity, at surface and subsurface levels through remote sensing models and techniques; (iv) to validate the work to assure the reliability of the geostatistical and remote sensing models through in situ observations.
Dharmapuri is located in the northwestern climate zone of Tamil Nadu, India.
It lies between 11
Study area – Dharmapuri district of Tamil Nadu, India. It is a typical example of semiarid regions across the world where 70 % of the population rely on dry land agriculture. The annual evapotranspiration rate exceeds the rate of rainfall because of its locality under rain-shadow region of the Western Ghats. Prevalence of extreme temperatures, native fluoride in nature and unsustainable agricultural practices have caused the region to experience drought, which often leads to desertification.
The district also faces strong salinization, affecting 2 % of land.
About 1 % of land is affected by waterlogging and it is proved with the
presence of hard pans (Fig. 5e). The entire region suffers from water scarcity
and out of five taluks, four are over-exploited (Central Ground water Board
Report, 2009). The presence of fluoride is more than the permissible limit (above
1.5 mg L
Since MODIS (Moderate Resolution Imaging Spectroradiometer) and NOAA AVHRR satellites have high temporal resolution, they
have been adopted for long-term change detection, land degradation and
desertification analysis so far by various researchers across the world.
However, the limiting factor which hinders the applicability to employ those images
in the subregional-level assessment is their low spatial resolution. Therefore,
moderate spatial resolution with long-term temporally available images are
required to assess the phenomena at subregional level. Landsat satellite
images have the longest spatial record for land observation (Williams et
al., 2006). Therefore, the present study is carried out using ETM
The two satellites are in a sun-synchronous, near-polar orbit at 705 km
altitude and have the moderate spatial resolution of 30 m for multispectral
bands. ETM
Daily meteorological data sets such as rainfall, minimum temperature, maximum temperature, wind speed, solar radiation and relative humidity from 2001 to 2015 have been collected from Centre for Climate Change and Adaptation Research, Anna University, India. Dharmapuri is one of the districts of Tamil Nadu, India, which is affected by the kharif season or southwest monsoon rain (June–October). Therefore, the crop growth is considerably high from June to October. There are six meteorological stations, distributed in and around the study region. Plotted points are interpolated using the inverse distance weighting method with same resolution (30 m cell size) as the Landsat data. The minimum temperature, maximum temperature, wind speed, solar radiation, relative humidity and rainfall raster of different months of growing season are then averaged to obtain the mean values of growing season of each year.
Land degradation to desertification phase identification plays a vital role before an assessment of desertification can begin. The phase can be initially identified through rainfall and temperature distribution. The time span of land degradation/drought has to be identified in order to recognize the progress of desertification. From 2006 to 2011 there was a significant decline in rainfall and increment in temperature followed by sudden, high rainfall which occurred in 2012 (Fig. 2). Therefore, the land degradation probably occurred in the 2006–2011 time frame.
Climate diagram for one of the meteorological stations (Nallampalli
12.34
Soil degradation has a more direct relationship with desertification than other environmental parameters. The degraded soil cannot be reversed when the natural degradation component and anthropogenic activities are prolonged for more than a decade. There are many soil degradation processes which are irreversible in nature and may have a direct association with desertification. If the degraded soil is not regenerated from the high rainfall that began in 2012 and continued until 2015 (4 years), then it is termed as soil desertification, otherwise it is just seen as degradation.
The research question arose here as follow. By which component have the soil desertification processes been accelerated? The research question was answered by formulating the hypothesis. Soil salinity and soil moisture stress is hypothesized in the study region based on the climatic prevalence (less rainfall and fewer extreme temperature events), geological functions (nature of the parent material – fluoride concentration) and human pressure. The soil desertification processes which were active during both the periods were analyzed, quantified and extracted with respect to the prime inducing components (temperature-, wind-, rainfall- and human-inducing components) after the successful execution of the hypothesis.
Before estimating the NDVI (Normalized Difference Vegetation Index), the
bands of the images have to be topographically normalized in order to
eliminate the effects from the shadowed region on NDVI images. Bands 3 and 4 of
ETM
A strong linear trend was identified between the NDVI and 3-months' cumulative
rainfall (Nicholson et al., 1990). Since the rainfall and NDVI follows a linear
trend, the spatial linear regression model is formulated by taking rainfall as an
independent variable and NDVI as a regressed parameter. The regression
analysis has been performed for each year from 2001 to 2011 after computing
the slope and intercept for each pixel, in order to yield the result of a longer
term NDVI that should have been present in the study region with respect to
rainfall; hence it is called NDVI
In order to identify the negatively correlated area where the vegetation
stress is dependent on wind speed, the spatial correlation coefficient
analyses is done for a longer term mean wind speed and mean predicted NDVI
(NDVI
The region of positive correlation (wind speed–NDVI
A statistically significant negative slope in the NDVI time series is an indicator of degradation (Wessels et al., 2007). The long-term vegetation stress can be well identified and monitored by NDVI data and thus used for desertification assessment (Kundu and Dutta, 2011). The time trend analysis has been performed for a longer term NDVI to identify the spatiotemporal gradual and sudden changes in the vegetation condition of the study region over a period of time. Both a longer term (2001–2011) (Fig. 6a) and a short-term (2012–2015) trend have been estimated spatially for maximum NDVI.
At a regional level, both supervised and unsupervised classification works well for extracting soil salinity (Naseri, 1998). Supervised classification was performed for Landsat Satellite images of 2001, 2005, 2010 and 2014 in order to classify the saline, non-saline soils, wetlands and human structures. Maximum likelihood algorithm has been implemented in the supervised classification. About 50 in situ observations are used as samples for training the system to classify the images. The Level 1 land cover types, namely, forest region, agricultural land, wetland, built-up, barren land and water body were identified in the study region with the help of ground knowledge. The overall accuracy of the work was verified through confusion matrix, and the attained accuracy of the classification was 91 %.
Few authors like Moren et al. (1994), Sandholt et al. (2002) and Shafian and
Mass (2015) have proved the potential of the triangle and trapezoidal model on
temperature–vegetation scatter plot in the extraction of soil moisture
with adequate accuracy level (
The distribution pattern of NDVI with respect to the TIR band follows a shape
of an inclined rectangle. From the rectangle we found two wet edges and two dry
edges based on their position on the scatter plot. Edge 1 and 2 are wet
edges; 3 and 4 are dry edges. The diagonal line is formed from edge 1 to
4, as edge 1 is extremely wet compared to edge 2, and 4 is extremely dry
compared to edge 3. The wetness or moisture decreases with increasing
diagonal distance (Eq. 1) from extreme wet edge 1.
Like temperature, the wind speed also influences the moisture content in the
surface soil, which is not considered in the previous studies. Since the
diagonal distance or moisture stress is inversely proportional to NDVI and
directly proportional to wind speed, the model has been formed as shown in
Eq. (2). TIR band and average wind speed data of the growing period were
normalized before feeding them into the equation. Soil moisture stress maps of
4 years (2001, 2005, 2010 and 2014) have been mapped using the DSMSI model
(Eq. 2). The DSMSI model is a function of NDVI, TIR and Wind speed.
The values of the model vary from 0 to 100 in which 0 indicates sufficient
moisture content in the soil, 100 indicates the extreme dryness and
intermediate values illustrates the moisture stress severity levels. Soil is
said to be stress-affected if DSMSI
Saline soils are very difficult to identify because of their dynamic nature. Dry areas are naturally prone to soil salinization due to a lower rate of rainfall and high evaporation which limits the leaching of salts, and this effect is expected to be magnified when it is combined with humans' negative intrusion like the over-fertilization of farmland (Metternicht and Zinck, 2009). Allbed et al. (2014) found that the Salinity Index (SI) and red band (band 3) have significant correlation with electrical conductivity (EC). Though much research has been carried out in the field of soil salinity identification so far, through various models like SI, Normalized Differential Salinity Index (NDSI) etc., an extraction of soil salinity still needs more accuracy because of the confusion created from the same spectral signature values of settlement roofs and saline-affected zones in the study region. Therefore, additional independent variables have to be involved in the analysis in order to reduce the places of uncertainty. Abdul-Qadir and Benni (2010) found that the mid-infrared (mid-IR) band has shown high correlation between SI and NDSI. Saline soils make it difficult for the plant to absorb the moisture content present in the soil, which results in soil moisture stress, especially at the root zone. Therefore, zone 3 (salinity-induced moisture stress) (Fig. 3b) in the present study is solely considered as saline-affected.
Since the diagonal distance of the scatter plot (Fig. 3a) had a similar relationship with soil salinity as the salinity increases with increasing diagonal distance, the same model was used in association with the mid-IR band for deriving a new model, named the Diagonal Soil Salinity Index (DSSI), which has increased the accuracy of extracting surface soil salinity with moisture stress at the extreme ranges. Inclusion of the mid-IR band in the model has significantly reduced the effect of building roofs on extracting salinity.
Spectral plot of the study region shows that the saline-affected region can
be separated from the settlements from the 0.7 to 1 (after normalization) range in
the mid-IR region. Low organic matter exhibits higher reflectance in the
mid-IR region rather than the settlements. The diagonal distance of the
right-angled triangle, observed from the extreme corner of the scatter plot of
mid-IR and NDVI, was considered as a saline line where the salinity increases
with increasing distance from an apex point (NDVI
Hypothesis combinations, computed
The directional hypothesis was formed in such a way that proves the expected
direction of relationship between temperature–salinization, wind–soil
moisture stress and rainfall–moisture stress at 0.05 % confidence
level. Based on the hypothesis, the rejection area, i.e., the area of high
correlation existence between the variables, has been extracted for each case
and shown in Table 1. For the temperature–salinity combination, the computed
value of the
In situ measurements are taken (Fig. 5b) for the chemical characteristics of the soil such as soil salinity, EC, pH, temperature and soil moisture at 100 locations in and around the study region using EI Deep Vision water and soil analysis equipment (Model 161). A questionnaire was also conducted with the inhabitants of the study region, along with the field measurements, in order to be familiarized with the socioeconomic condition of the people, their land management practices and their awareness towards land degradation processes. Soil samples were collected at both surface and subsurface level (1 m) (Fig. 5f) and dissolved with groundwater of that region in order to facilitate the process of measuring chemical parameters as listed in Table 2.
Oleander (
Statistical measure of the chemical parameters at sampled locations (33 per region). EC and salinity follow positive correlation with pH in the alkaline range (> 7). The moisture content is below 35 % in all three zones of desertification. In the drought-affected region the higher salinity is found at the subsurface level because of the fluoride concentration in groundwater. We found that the inverse relationship in human-induced desertified zone, i.e., the surface salinity, is higher than the subsurface and the standard deviation (SD) of surface-level salinity is lesser than the subsurface, which indicates that the human-induced salinity is not fluctuating at surface level because of the constant application of fertilizers for more than 2 decades. A high extent of surface-level soil moisture stress (90 %) is observed in the wind-induced desertified regions.
From the spatial correlation analysis between a longer term mean wind speed
and mean predicted NDVI (Fig. 6b), we found that 46 % (192 395 ha) of
land is negatively correlated, 48 % (199 526 ha) of land is positively
correlated and 6 % (21 846 ha) is experiencing no correlation because
of the stable NDVI over a period of time. The negatively correlated area is
the direct illustration of NDVI reduction due to the high wind speed over a
period of time. The land degradation due to wind speed is identified if the negative
NDVI
The positively correlated area of wind speed
Similarly, human-induced degradation was identified if the negative
NDVI
As discussed earlier, in this study, if the degraded land is not responding well to the sufficient rainfall, then the situation is termed as desertification. Desertified land area is identified by considering the short-term NDVI (2012–2015) trend when the rainfall rate is sufficient for the plant growth. Desertified area has been extracted where the degraded land is still facing a negative short-term NDVI trend. About 70 % (121 179.15 ha) of the degraded land would have been wrongly identified as desertified land if the short-term NDVI trend had not been included in the analysis. The research found that 15 % of the total area (439 189.71 ha) is in a desertified state. Figure 6d illustrates the three zones of desertification due to wind–temperature, rainfall–temperature and human events.
The soil degradation process, invoked by the four crucial factors, is recognized in the present study as soil is the predominant component which becomes deteriorated by the process. Red gravelly clay soil is predominately found in the drought-affected region. The fine pores of clay-like soil have a great ability to retain the water through capillary action. Therefore, the soil of the drought-affected region has naturally inherited high fluoride content from the groundwater through capillary action. During times of extreme temperatures, the saline water in the subsurface and some surface levels of the soil evaporates and salt remains accumulated, as cited earlier. Higher fluoride content and extreme pH have high positive correlation and thus enable the water and soil to experience salinity (Adhikary et al., 2014b).
In the drought-affected zone the soil salinity should have been accelerated due
to native fluoride concentration on groundwater, a high evaporation rate
induced by temperature and the absence of the leaching process because of the
inadequate amount of rainfall over a period of time. As hypothesized, the
drought-induced desertified area should have been strongly affected by
salinity. Higher salinity hinders the vegetation growth and thus supports the
water erosion and saline leaching process during the occurrence of extreme
rain soon after a longer period of insufficient rainfall, which happened in 2012.
Surface-level soil salinity was estimated for 2001, 2005, 2010 and 2015 using
the DSSI model as it yielded a best fit with ground truth measurements
(
Hence, the initial prediction has proved that the drought-affected zone
should have emphatically experienced a higher level of soil salinity than other
soil degradation factors. From the ground truth observations it was noticed
that the subsurface salinity or EC was significantly higher (mean
6.5 mS cm
Based on the directional hypothesis, the wind–temperature-affected region is
expected to experience soil moisture stress at the surface level. Therefore, the
further component, the surface soil moisture stress, was extracted for 2001, 2005, 2010
and 2015 through the DSMSI model, and it yielded a best fit
with in situ observations (
Though the levels of moisture stress and salinity were obtained at surface level effectively in the drought- and wind-affected region using the DSMSI and DSSI model, we could not model the subsurface moisture stress and salinity as we have dealt with multispectral images which do not have surface penetration capacity. The DSMSI model is only applicable for dry conditions/the leeward side of mountain regions where the occurrence of rainfall is majorly hampered by the augmentation of wind speed and temperature. Conversely, the model is not applicable to the areas where the (i) rate of urbanization is high; (ii) the soil is waterlogged in a higher proportion; (iii) there is an active presence of water bodies. Similarly, the DSSI model is also applicable for dry conditions and not suitable for waterlogging situations. Therefore, the model was only employed in climate-affected regions (drought- and wind-affected region).
The consequences of the above two declared factors of desertification, namely
drought and wind, are slow and can be suspected and quantified with respect to
past, current and future prediction models as they solely depend on climatic
variables. But the third factor, human activities, causes an adverse
effect on the land in an unexpected and rapid manner and cannot be predicted
in advance. According to Vieira et al. (2015) human activities are the
predominant factor for desertification expansion. In the case of human-affected regions, the salinity was measured from LULC maps, not from the
model, as the uncertainty developed from the distribution of Oleander
(
Human-induced salinity increased by 9 % in the degradation phase and becomes accelerated in the desertification phase (14 %). About 5 % of the saline track increased in the desertification phase. Dissolved nitrate is the main source for the concentration of fluoride in the groundwater. There is no evidence for the geological source of nitrate (Ramesh and Vennila, 2012) in the human-induced desertified region. Therefore, it was resultant from human activities. The surface EC values are higher than the subsurface in the human-affected region. The fact may be supported by the opinions obtained by local farmers (Fig. 5a) that the long-term application of fertilizers for more than 2 decades raises the salinity in the soil more than the fluoride does. In the human-affected regions, extreme surface salinity is observed in few low-lying areas (Fig. 5c), because of the leaching of fertilizers from surrounding elevated areas. The distribution of salinity is discrete in the human-affected regions. Hence, the substantial increment of salinity in the human-affected zone was due to the high application of fertilizers on land that is already fragile to achieve a high production rate, more than its sustainable capacity in order to support the growing population.
Furthermore the human occupancy has significantly increased from 2001 to 2015, particularly in the degradation period (2006–2011). This is due to the increment of population (8 %) from 2001 to 2011 (Fig. 8) in the human-affected region. Population growth of the other regions (temperature/drought affected, rainfall-affected and wind-affected) has followed a decreasing trend from 2001 to 2011, which is the direct illustration of migration of the inhabitants to urbanized areas due to the prevalence of drought in these years. About 38 % of decadal growth (2001–2011) of the population was particularly observed in the urban areas of the study region (Census of India, 2011). Therefore, during the aridity stage, human pressure should have started on the agricultural land in order to support the growing population or attain financial security, as the agricultural practice was not very feasible in the period. Figure 7d depicts the rate of increment of salinity and urbanization. Soil moisture stress had little effect in the human-affected region, which only increases by 1 % during the degradation phase.
Population trend of three zones of desertification from 1991 to 2011. The population trend has been significantly increased (8 %) from 2001 to 2011 in the human-induced desertified zone, where as in the temperature/drought, wind and rainfall-affected zone, it follows a decreasing trend. This is because of the migration of the inhabitants towards urbanized area in order to stabilize their economical needs during drought period.
The standard deviation of subsurface EC and salinity is significantly
higher (1.46 and 1.49) than the surface level which shows that the values of the
salinity are highly fluctuating at the subsurface level (Table 1)
particularly in the human-affected zone. Hence, the DSSI model has yielded poor
accuracy for the subsurface level (
The proposed study focused on four driving forces such as temperature-, wind-, rainfall- and human-induced factors for the assessment of land degradation and desertification with the aid of an appropriate geostatistical model. The successful directional hypothesis has assisted the research in the identification of the highly influential soil degradation process in all three affected zones. From the new perspective of assessment, appropriate models can be applied to the area based on the predicted soil degradation process, because we cannot apply a soil erosion model to a region which has been affected by salinity over the years. However, some limitations are demonstrated in the present work which have to be improved upon in future research. (1) The wind-affected region would have also faced strong wind erosion, but only soil moisture stress was studied. (2) Either soil salinity or soil moisture is extracted at surface level only, but in the study area, the salinity has been inherited from the groundwater and it is expected to be present at the subsurface level too, which cannot be measured by multispectral remote sensing models. (3) A study of soil textural variations would increase the reliability of the results. (4) Human activities are studied through LULC maps, but the incorporation of overgrazing and an excessive yield estimation model may increase the quality of the work. (5) Since soil salinity, soil moisture stress and hard pans are the dominant features present in the surface and subsurface levels, recent advanced technology like microwave remote sensing should be employed in order to quantify the salinity regions accurately. Because of the potentiality of all weathers, the capability of surface penetration and the response towards the electrical properties of the target, microwave remote sensing is able to provide adequate ground for extracting the surface and subsurface salinity and moisture compared to other methods such as optical and multispectral remote sensing.
We would like to express our sincere thanks to Department of Science and Technology, India, for the financial support (grant no. DST/Inspire Fellowship/2013/1109/IF131152), without which we would not have been able to purchase the required instruments and data to carry out the research work.
We thank the Centre for Climate Change and Adaptation Research, Anna University, for providing the meteorological data on time, and we are also thankful to the USGS for their free distribution of long-term Landsat images through the online data portal. Edited by: A. Cerdà