Search

The need for information on soil moisture at large scale to facilitate a sustainable intensification of agricultural land and to ensure food security due to increasing populations cannot be oversta...

Tropical cyclones as natural disturbances, influence ecosystem structure, function and dynamics at the global scale. This study assesses the inundation due to the super cyclone Amphan in coastal districts of eastern India by leveraging the computational power of Google Earth Engine (GEE) and the availability of high resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. A cloud-based image processing framework was developed and implemented in GEE for classification using Random Forest algorithm. The inundation areas due to storm surge owing to cyclone Amphan, were mapped and further categorised to different land use and land cover classes based on an existing land cover map. Sentinel-1 images were useful in post-cyclone studies for the change detection analysis due to its higher temporal resolution and cloud penetration ability. The study found that the majority of agricultural and agricultural fallow lands were inundated in the coastal districts. The availability of open-source cloud-based data processing platforms provides cost effective way to rapidly gather accurate geospatial information. Such information could be useful for emergency response planning and post-event disaster management including relief, rescue and rehabilitation measures; and crop yield loss assessment. Cyclone and Land Use and Land Cover (LULC) change can have significant impacts on the human population and if both coexist, the consequences for people and the surrounding environment may be severe.

The dual polarimetric study including degree of polarization (mL) and energy span (λ1+ λ2) for vegetation targets infer the accuracy of vegetation algorithms. The Sentinel − 1 A and ALOS − 2 satell...

Boro rice, an emerging low-risk crop variety of rice, cultivated using residual or stored water after Kharif season. To enhance the quality and production of rice, potassium (K) and phosphorus (P) are the common constituents of agricultural fertilizers. However, excess application of fertilizers causes leaching of nutrients and contaminates the groundwater system. Therefore, assessment and optimization of fertilizer dose are needed for better management of fertilizers. Towards this, the present study determines the path, persistence, and mobility of K and P under the Boro rice cropping system. The experimental site consisted of four plots having Boro rice with four different fertilizer doses of nitrogen (N), P, K viz. 100%, 75%, 50%, and 25% of the recommended dose. Disturbed soil samples were analysed for K and P from pre-sown land to tillering stage at 0–5, 5–10, 10–15, 15–30, 30–45, and 45–60 cm depths. Simultaneously, K and available P were also simulated in the subsurface soil layers through the HYDRUS-1D model. The statistical comparisons were made with RMSER, E, and PBIAS between the modelled values and laboratory-measured values. Although, the results showed that all the treatments considered had agreeable simulations for both K and P, the K simulations were found to be better as compared to P simulations except for 25% where P simulations outperformed K. The simulated concentration at all doses was found most appropriate when measured for the subsurface layers (up to 45 cm), while showed an underestimation in the bottom layers (45–60 cm) of soil.

This study investigates the potential of different vegetation descriptors (V) in the modified water cloud model (MWCM), with a focus on comparing the Red – Edge vegetation indices (VI) based and ot...

Changes in climate cause significant alterations in morphometric parameters and may lead to hydro-meteorological hazards. In this study, an attempt has been made to identify drainage morphometric characteristics through topographic, geologic and hydrological information to assess the extreme weather events (flood) over the Gandak River Basin (GRB). The standardized precipitation index (SPI) and rainfall anomaly index (RAI) were used for deducing extreme rainfall incidences derived from the Tropical Rainfall Measuring Mission precipitation datasets. An assembled frequency distribution as well as trends in RAI and SPI was calculated to understand the hydro-climatological behaviour of the basin. During the monsoon season, the years 1998, 2007, 2011, 2013 and 2017 witnessed the extreme flood events. The variations in heavy and intense rainfall in short time can be linked to extreme flood events, which leads to channel shifting and modifications, can be deduced from provided asymmetric factors and sinuosity index. The results illustrated that both the monsoonal rainfall and the frequency of extreme rainfall over the basin are increasing, which could be a reason for a high severity and frequency of flood events in the GRB.

In this study, a comparative analysis of three satellite precipitation products including the Tropical Rainfall Measuring Mission (TRMM-3B43 V7), the Precipitation Estimation from Remotely Sensed I...

Hyperspectral acquisition provides the spectral response in narrow and continuous spectral channel. The high number of contiguous bands in hyperspectral remote sensing provides significant improvements in assessing subtle changes as compared to the multispectral satellite datasets in context of spectral resolution. Therefore, the main goal of the present research is to evaluate the sensitivity of the artificial neural networks (ANNs) for chlorophyll prediction in the winter wheat crop using different hyperspectral spectral indices. For evaluating relative variable significance in the study, the Olden’s function has been applied. The Lek’s profile method is used for sensitivity analysis of ANNs for chlorophyll prediction using the vegetation indices such as Red Edge Inflection Point (REIP), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Structure-Insensitive Pigment Index (SIPI) derived from hyperspectral radiometer. The analysis indicates a high sensitivity of SAVI followed by NDVI, REIP and SIPI for chlorophyll retrieval using ANNs. The statistical performance indices obtained from calibration (RMSE = 0.27; index of agreement = 0.96) and validation (RMSE = 0.66; index of agreement = 0.83) suggested that the ANN model is appropriate for chlorophyll prediction with good efficiency. The outcome of this work can be used by upcoming hyperspectral missions such as Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Hyperspectral Infrared Imager (HyspIRI) for large-scale estimation of chlorophyll and could help in the real-time monitoring of crop health status.

In the present study, the long-term variability in precipitation and temperatures was analyzed in relation to the urban environment of Ranchi Metropolitan Region, eastern plateau region, India. The daily meteorological observations of 5 decades (1961–2010) indicated an increasing mean temperature (0.4 °C) and decreasing cumulative precipitation in the Ranchi, capital region of the state Jharkhand. The results exhibited a declining precipitation patterns in the recent decade as compared to the earlier 4 decades. The high daily monsoon rainfall intensity with low cumulative precipitation can be observed during post-2000 periods, which indicate a highly erratic nature of precipitation in the region. Temporal census data demonstrated that the Ranchi urban region faced enormous proliferation in the human population (21 times) during the period 1927–2010 and thereby induced the extensive alteration in land use/land cover and rapid built-up expansion (> 5 times) as evidenced by the temporal satellite-based observations. The increasing annual per capita land consumption (361.50%) together with annual per capita loss of heat sink zones (96.3% during 1927–2010) and high influx of vehicles (563% during 1997–2010) influenced the local and regional climatic variable in the region. The results indicate that the rapid and haphazard urban sprawl in the last few decades and increase in built-up and impervious surface largely contributed in increasing the land surface temperature (34–42 °C) as compared to the rural environment (30–38 °C), which perhaps could be the region for the changes in climate and weather pattern of the area.

Biodiversity loss in tropical forests is rapidly increasing, which directly influence the biomass and productivity of an ecosystem. In situ methods for species diversity assessment and biomass in synergy with hyperspectral data can adeptly serve this purpose and hence adopted in this study. Quadrat sampling was carried out in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, which was used to compute Shannon–Weiner Diversity Index (H′). Above ground biomass (AGB) was calculated measuring the Height and Diameter at Breast Height (DBH) of different trees in the sampling plots. Four spectral indices, namely Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Photochemical Reflectance Index (PRI), and Structure Insensitive Pigment Index (SIPI) were derived from the EO-1 Hyperion Data. Spearman and Pearson’s correlation analysis was performed to examine the relationship between H′, AGB and spectral indices. The best fit model was developed by establishing a relationship between H′ and AGB. Fifteen models were developed by performing multiple linear regression analysis using all possible combinations of spectral indices and H′ and their validation was performed by relating observed H′ with model predicted H′. Pearson’s correlation relation showed that SIPI has the best relationship with the H′. Model 15 with a combination of NDVI, PRI and SIPI was determined as the best model for retrieving H′ based on its statistics performance and hence was used for generating species diversity map of the study area. Power model showed the best relationship between AGB and H′, which was used for the development of AGB map.

Even though satellite precipitation products have received an increasing amount of attention in hydrology and meteorology, their estimations are prone to bias. This study investigates the three approaches of bias correction, i.e., linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT), on the three advanced satellite precipitation products (SPPs), i.e., CMORPH, TRMM and PERSIANN over the Langat river basin, Malaysia by focusing on five selected extreme floods due to northeast monsoon season. Results found the LS scheme was able to match the mean precipitation of every SPP but does not correct standard deviation (SD) or coefficient of variation (CV) of the estimations regardless of extreme floods selected. For LOCI scheme, only TRMM and CMORPH estimations in certain floods have showed some improvement in their results. This might be due to the rainfall threshold set in correcting process. PT scheme was found to be the best method as it improved most of the statistical performances as well as the rainfall distribution of the floods. Sensitivity of the parameters used in the bias correction is also investigated. PT scheme is found to be least sensitive in correcting the daily SPPs compared to the other two schemes. However, careful consideration should be given for correcting the CMORPH and PERSIANN estimations.

Although satellite precipitation products (SPPs) increasingly provide an alternative means to ground-based observations, these estimations exhibit large systematic and random errors which may cause large uncertainties in hydrologic modeling. Three approaches of bias correction (BC), i.e. linear scaling (LS), local intensity scaling (LOCI), and power transformation (PT), were applied on four SPPs (TRMM, IMERG, CMORPH, and PERSIANN) during 2014/2015 extreme floods in Langat river basin, and the performance in terms of rainfall and streamflow were investigated. The results show that the original TRMM had a potential to predict the peak streamflow although CMORPH show the best performance in general. After performing BC, it is found that the LS-IMERG and LOCI-TRMM show the best performance at both rainfall and streamflow analysis. Generally, it is indicated that the current SPP estimations are still imperfect for any hydrological applications. Cross validation of different datasets is required to avoid the calibration effects of datasets.

Accurate estimation of evapotranspiration is generally constrained due to lack of required hydro-meteorological datasets. This study addresses the performance analysis of Reference Evapotranspiration (ETo) estimated from NASA/POWER, National Center for Environmental Prediction (NCEP) global reanalysis data before and after dynamical downscaling through the Weather Research and Forecasting (WRF) model. The state of the art Hamon’s and Penman-Monteith methods were utilized for the ETo estimation in the Northern India. The performances indices such as Bias, Root Mean Square Error (RMSE) and correlation(r) were calculated, which showed the values 0.242, 0.422 and 0.959 for NCEP data (without downscaling) and 0.230, 0.402,0.969 for the downscaled data respectively. The results indicated that after WRF downscaling, there was some marginal improvement found in the ETo as compared to the without downscaling datasets. However, a better performance was found in the case of NASA/POWER datasets with Bias, RMSE and correlation values of 0.154 0.348 and 0.960 respectively. In overall, the results indicated that the NASA/POWER and WRF downscaled data can be used for ETo estimation, especially in the ungauged areas. However, NASA/POWER is recommended as the ETo calculations are less complicated than those required with NASA/POWER and WRF.

Flash flood is an uncertain and most catastrophic disaster worldwide that causes socio-economic problems, devastation and loss of infrastructure. One of the major triggering factors of flash floods is the extreme events like cloudburst that causes flooding of area within a short span of time. Therefore, this study aims to understand the variations in hydro-meteorological variables during the devastating Kedarnath cloudburst in the Uttarakhand, India. The hydro-meteorological variables were collected from the global satellites such as Moderate Resolution Imaging Spectroradiometer, Tropical Rainfall Measuring Mission, modelled datasets from Decision Support System for Agrotechnology Transfer and National Center for Environmental Prediction (NCEP). For the validation of satellite meteorological data, the NCEP Global analysis data were downscaled using Weather Research and Forecasting model over the study area to achieve the meteorological variables’ information. The meteorological factors such as atmospheric pressure, atmospheric temperature, rainfall, cloud water content, cloud fraction, cloud particle radius, cloud mixing ratio, total cloud cover, wind speed, wind direction and relative humidity were studied during the cloudburst, before as well as after the event. The outcomes of this study indicate that the variability in hydro-meteorological variables over the Kedarnath had played a significant role in triggering the cloudburst in the area. The results showed that during the cloudburst, the relative humidity was at the maximum level, the temperature was very low, the wind speed was slow and the total cloud cover was found at the maximum level. It is expected that because of this situation a high amount of clouds may get condensed at a very rapid rate and resulted in a cloudburst over the Kedarnath region.

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.

This paper demonstrates machine learning based approach to retrieve soil moisture (SM) and its validation over India using CYGNSS data. CYGNSS mission is mainly designed and dedicated for monitoring the tropical cyclones over ocean.However, recent developments has highlighted the potential of GNSS-Reflectometry for land applications, specially for SM with high spatio-temporal frequency over traditional satellite data sets. It can be directly utilized to retrieve SM as complementary data to fill the spatial and temporal gaps in satellite microwave radiometer derived SM, like from SMAP and SMOS mission to meet the requirements of high spatial and temporal frequency data sets for agricultural applications. In this work, we developed an Artificial Neural Network (ANN) framework to derive SM and validated at selected agricultural sites over India. SMAP derived vegetation and roughness parameters were also used as inputs for training of ANN model to add the effect of vegetation and roughness. Detailed spatial and temporal correlation analyses of CYGNSS SM were performed to test the proposed ANN model using SMAP SM and in-situ observations from hydra probe station data from 2018 to 2019. It was observed from temporal correlation analysis that CYGNSS and SMAP SM follow a good trend with high correlation using in-situ data. Spatial correlation also shows high correlation with Pearson correlation coefficient of 0.69 and RMSD of 0.057 m3/m3during pre-monsoon and 0.65 and 0.053 m3/m3in post monsoon periods, respectively.

The objective of this research is to identify species, provide spatial distribution of the species and estimate the biomass in the mangrove Forest, Bhitarkanika India. Mangrove ecosystems play an important role in regulating carbon cycling, thus having a significant impact on global environmental change. Extensive studies have been conducted for the estimation of mangrove species identification and biomass estimation. However, estimation at a regional level with species-wise biomass distribution has been insufficiently investigated in the past because either research focuses on the species distribution or biomass assessment. Study shows that good relationship has been achieved between stem volume (field measured data) and Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from satellite image and further these two indices are employed to estimate the biomass in the study site. Three models- linear, logarithmic and polynomial (second degree) are used to estimate biomass derived from EVI and NDVI. The hyperspectral data (spatial resolution ~ 30 m) is utilised to identify ten mangrove plant species. We have prepared the spatial distribution map of these species using spectral angle mapper. We have also generated mangrove species-wise biomass distribution map of the study site along with areal coverage of each species. The results indicate that the Sonneratia apetala Buch.-Ham. and Cynometra iripa Kostel has the highest biomass among all ten identified species, 643.12 Mg ha−1 and 652.14 Mg ha−1. Our study provided a positive relationship between NDVI, EVI, and the estimated biomass of Bhitarkanika Forest Reserve Odisha India. The study finds a similar results for both NDVI and EVI derived biomass, while linear regression has more significant results than the polynomial (second degree) and logarithmic regression derived biomass. The polynomial is found slightly better than the logarithmic when using the EVI as compared to NDVI derived biomass. The spatial distribution of species-wise biomass map obtained in this study using both, EVI and NDVI could be used to provide useful information for biodiversity assessment along with the sustainable solutions to different problems associated with the mangrove forest biodiversity. Thus, biomass assessment of larger regions can be achieved by utilization of remote sensing based indices as concluded in the present study.

Soil loss through erosion and its subsequent deposition is considered as an important challenge for watersheds. In this paper, attempt has been made to integrate the Revised Universal Soil Loss Equ...

Surface roughness is a crucial parameter for the estimation of soil moisture (SM). The present study attempted to optimize the surface roughness parameter (h) for the estimation of SM from Soil Moisture Active Passive (SMAP) using tau–omega (τ-ω) model and also downscaled the estimated SM product using a polynomial regression relation among Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and SM. The brightness temperature of SMAP available at two spatial resolutions (36 and 9 km) was used for two seasons intended for SM assessment. After assessment with in-situ SM data, 9-km SM data values were further used for spatial disaggregation to obtain the optimized downscaled soil moisture (ODSM) at 1 km. Results showed that the variation in the value of the roughness parameter strongly affects the performance of the τ-ω model and the downscaling performances. The investigation provided lowest values of root-mean-square error (RMSE) to be 0.0518 (at h = 0.35) and 0.0480 (at h = 0.25) for the SM estimation at 36 km for the different seasons used in this study while the lowest values of RMSE for ODSM were found to be 0.0365 (at h = 0.4) and 0.0252 (at h = 0.25, 0.3) for different seasons.

Rainfall and land use/land cover changes are significant factors that impact the soil erosion processes. Therefore, the present study aims to investigate the impact of rainfall and land use/land cover changes in the current and future scenarios to deduce the soil erosion losses using the state-of-the-art Revised Universal Soil Loss Equation (RUSLE). In this study, we evaluated the long-term changes (period 1981–2040) in the land use/land cover and rainfall through the statistical measures and used subsequently in the soil erosion loss prediction. The future land use/land cover changes are produced using the Cellular Automata Markov Chain model (CA-Markov) simulation using multi-temporal Landsat datasets, while long term rainfall data was obtained from the Coupled Model Intercomparison Project v5 (CMIP5) and Indian Meteorological Department. In total seven CMIP5 model projections viz Ensemble mean, MRI-CGCM3, INMCM4, canESM2, MPI-ESM-LR, GFDL-ESM2M and GFDL-CM3 of rainfall were used. The future projections (2011–2040) of soil erosion losses were then made after calibrating the soil erosion model on the historic datasets. The applicability of the proposed method has been tested over the Mahi River Basin (MRB), a region of key environmental significance in India. The finding showed that the rainfall-runoff erosivity gradually decreases from 475.18 MJ mm/h/y (1981–1990) to 425.72 MJ mm/h/y (1991–2000). A value of 428.53 MJ mm/h/y was obtained in 2001–2010, while a significantly high values 661.47 MJ mm/h/y has been reported for the 2011–2040 in the ensemble model mean output of CMIP5. The combined results of rainfall and land use/land cover changes reveal that the soil erosion loss occurred during 1981–1990 was 55.23 t/ha/y (1981–1990), which is gradually increased to 56.78 t/ha/y in 1991–2000 and 57.35 t/ha/y in 2000–2010. The projected results showed that it would increase to 71.46 t/h/y in 2011–2040. The outcome of this study can be used to provide reasonable assistance in identifying suitable conservation practices in the MRB.

The present study investigates the ability of SimSphere, a soil vegetation atmosphere transfer model, to predict key parameters in characterising land surface interactions. In particular, the model's performance in predicting Net Radiation (Rnet), Latent Heat (LE) and Sensible Heat (H) was examined. For this purpose, concurrent in-situ measurements of the corresponding parameters for a total of 70 days of the year 2011 from seven CarboEurope network sites were acquired, incorporating a variety of environmental biomes and climatic conditions in the model evaluation. In overall, SimSphere was largely able to accurately predict the variables against which it was evaluated for most of the experimental sites. Statistical analysis showed highest agreement of H fluxes to the measured in-situ values for all ecosystems, with an average root mean square difference of 55.36 Wm−2. Predicted latent fluxes and Rnet also agreed well with the corresponding in-situ data with RSMDs of 62.75 and 64.65 Wm−2, respectively. Our findings contribute towards a better understanding of the model structure, functioning and its correspondence to the real-world system. They also further establish its capability as a useful teaching and research tool in modelling Earth's land surface interactions. This is important given its increasing use, including its synergies with Earth observation data.

Spatial distribution analysis of in-situ measurements within a study area using geostatistical approach is always a complex thing to perform. Present study deals with a geostatistical method to map the distribution of soil moisture and soil temperature throughout the study area using Hydra probe in-situ data. As soil moisture plays an important role in short- and long-term meteorological modelling and also is a vital component for sustaining life supporting systems at micro- and mega-scale, it is required to monitor its spatial and temporal variation with high precision. Presently, a multivariate geostatistical approach, i.e., Kriging with External Drift (KED), is used to improve the accuracy of spatial distribution mapping of soil moisture within the study area. Semi-variogram analysis is done to estimate the semi-variance in the model and the stability of the interpolated results. The correlation is established between the observed and predicted soil moisture that has shown R2 of 0.989 and Root Mean Square Error of 0.32, which shows that the model performed very well.

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections. The authors acknowledge financial support from the Spanish Government, Grant RTI2018-354 094336-B-I00 (MCIU/AEI/FEDER, UE), the Spanish Carlos III Health Institute, COV 20/01213, and the Basque Government, Grant IT1207-19.

This chapter has been focused on to understanding the persistence and mobility of selected pesticides in the unsaturated soil zone under varying irrigation treatments. The movement of the pesticide can be determined in subsurface through open source software like HYDRUS-1D. The results of various studies in technical literature have shown that numerically simulated outputs through HYDRUS-1D have a good agreement with the experimental results. Through model simulations, regulation strategies can be suggested towards safe dosages of pesticides with respect to different irrigation treatments. This chapter will provide a step-by-step procedure towards numerical simulation of pesticide with the usage of HYDRUS-1D and can be utilised for safeguarding water requirements in the agricultural fields.

Rapidly changes in potential evapotranspiration can provide significant information in understanding of hydrologic processes as well as for agricultural crop performance. Such information can also prove to be useful in climate related studies. However, accurate measurements and predictions of evapotranspiration are difficult especially at large spatial scales. Remote sensing provides a cost‐effective approach to determine potential evapotranspiration at both regional and global scales. In the present study, effectiveness of Hamon’s method for measuring annual variations in potential evapotranspiration based using INSAT-3D dataset was evaluated for agricultural area of Varanasi region, India. Observed potential evapotranspiration data was compared with INSAT-3D satellite data. The performances indices such as correlation (r), Bias and Root Mean Square Error (RMSE) indicate the values of 0.572, 0.524 and 0.834 for INSAT-3D data. Result indicated that INSAT-3D data further can be used for estimation of evapotranspiration.

The soil moisture level effectively estimates the extremity of irrigation demand, and thus acts as a significant indicator of irrigation water management. In recent time due to increase in population, demand for freshwater in all competing sectors is being a constraint for irrigation that raises the need to optimise utilisation of irrigation water with its high efficiency. Therefore, an accurate and precise assessment of soil moisture content (SMC) is required for optimal allocation and management of water in agriculture. Owing to this, the current study aims to simulate SMC from water balance–based macro-scale Variable Infiltration Capacity (VIC) hydrological model for various applications in irrigation water management. The current study was carried out in the Bundelkhand region of Uttar Pradesh, India, for a period of 5 years (2010–14). The simulated SMC was validated with ESA Climate Change Initiative soil moisture (CCI-SM) indicated high R2 value ranging from 0.73 to 0.90 and low RMSE value ranging from 0.03 to 0.05 m3m−3. The dataset of simulated and CCI-SM content values was close to the 1:1 scale line for approximately all the periods. Additionally, the Soil Moisture Deficit Index (SMDI), a dryness index, was computed for estimating irrigation demand from VIC-derived SMC for Kharif season (July–October) from 2010 to 2014; this shows more irrigation water demand in 2014 and sowing period of 2010 and 2012.

Day by day water scarcity is increasing and simultaneously waste water generation is also increasing. Therefore, proper management and recycling of waste water is required in order to minimise its ill-effects. One of the productive uses of the industrial waste water is its application in agricultural lands but not every type of industrial waste water is found fit for such application. Thus, in this study comparative analyses of soil, water and vegetable produced (growth and biochemical parameters) by irrigating using tap water and oil refinery waste water were done to bring out the differences between the cultivated vegetables. Numerous parameters from physicochemical properties of water, soil, germination, morphology, growth rate to biochemical aspects of the vegetables were analysed. It was found that epicotyl, hypocotyl and the growth of root were better when using the effluents in irrigation. Nutritional value of effluent-irrigated plants was found to be higher, as fruits contained appreciable amounts of protein, ascorbic acid, calcium, magnesium, sodium, potassium and phosphorus.

Soil contamination by heavy metals is common in floodplains throughout the world. Apart from other assessment techniques available, hyperspectral remote sensing is widely used as it offers a lucrative and fast assessment. The current work explores the possibility of on-field and laboratory spectroradiometry investigations together with geochemical data of lead (Pb), zinc (Zn), copper (Cu), and cadmium (Cd) in quantifying and modeling heavy metal soil contamination (HMSC) for a floodplain situated in Wales, United Kingdom. The goal of the study was to (1) gather on-field- as well as lab-based spectra from contaminated soils using analytical spectral devices FieldSpec3, in the spectrum range of 350–2500 nm; (2) construct spectral libraries of on-field- as well as lab-based readings; (3) carry out geochemical analyses of Pb, Zn, Cu, and Cd with the help of an atomic absorption spectrometer; (4) recognize the explicit spectral areas accompanying the modeling of HMSC; and (5) develop and validate heavy metal prediction models (HMPMs) through the spectral features of the contaminants and their concentrations in the soil. Two spectral libraries were developed from the on-field- and lab-based spectral features, which were derived from 85 soil samples. These spectral libraries along with the concentrations of Pb, Zn, Cu, and Cd were joint to construct eight HMPMs by stepwise multiple linear regression. The output provided, for the first time, the viability to predict HMSC in a highly contaminated floodplain site through the combination of geochemical analyses and field spectroradiometry. The resultant model offered support for mapping heavy metal concentrations over a huge area using space-borne hyperspectral sensors.

Rapid growing urban population has resulted in the occupancy of large proportionate of the city and its outskirts, thereby contributing factors to change in the environmental conditions. This has resulted in widespread land acquisition for built up and industrial development, covering the centre of the city while moving at the outskirts of the city as well. Land Use /Land Cover (LULC) changes causes alterations in the land use categories, mostly the concrete forests which has increased the urban temperature as compared to the rural regions due to rapidly growing urbanized environment. Urban Heat Island (UHI) is one of the human-induced environmental phenomenon affecting the urban inhabitant in many ways, such as altering and disturbing the land cover its use which changes thermal energy flow causing elevated surface and air temperature. Temporal satellite datasets (LANDSAT ETM+ image of 1989, 2000 and 2006) can be used to monitor surface temperature while vegetation indices can be used to assess the coverage of the vegetation and non-vegetation area in the region. Temporal NDVI is employed in the study area to analyse the impact of land surface temperature against NDVI in the region. Therefore, temporal remotely sensed data can be used to map LULC and its dynamic changes and other environmental phenomena such as surface temperature over a period of time. Temporal UHI has been estimated using geospatial technology to incorporate it for environmental impact assessment on the surrounding environment. The present research focuses on temporal NDVI and Surface temperature, the methodology used altogether for the assessment of resolution dynamic UHI change on environmental condition for Haridwar district, Uttrakhand India and Kanpur district, Uttar Pradesh in India. Both case study has different environmental conditions, geographical locations and demography. Hilly and forested region with almost no industrial activities for Haridwar while several industrial activities and densely populated region Kanpur located in an Indo-Gangetic plain. The research outcome demonstrates the correlation between temporal NDVI and Surface temperature exemplified with case study conducted over two different regions, geographically as well as economically. There is a need to consider the environmental dimension while making progress to urbanization.

Change in soil moisture regime is highly relevant for agricultural drought, which can be best analyzed in terms of Soil Moisture Deficit Index (SMDI). A macroscale hydrological model Variable Infiltration Capacity (VIC) was used to simulate the hydro-climatological fluxes including evapotranspiration, runoff, and soil moisture storage to reconstruct the severity and duration of agricultural drought over semi-arid region of India. The simulations in VIC were performed at 0.25° spatial resolution by using a set of meteorological forcing data, soil parameters and Land Use Land Cover (LULC) and vegetation parameters. For calibration and validation, soil parameters obtained from National Bureau of Soil Survey and Land Use Planning (NBSSLUP) and ESA's Climate Change Initiative soil moisture (CCI-SM) data respectively. The analysis of results demonstrates that most of the study regions (> 80 %) especially for central northern part are affected by drought condition. The year 2001, 2002, 2007, 2008 and 2009 was highly affected by agricultural drought. Due to high average and maximum temperature, we observed higher soil evaporation that reduces the surface soil moisture significantly as well as the high topographic variations; coarse soil texture and moderate to high wind speed enhanced the drying upper soil moisture layer that incorporate higher negative SMDI over the study area. These findings can also facilitate the archetype in terms of daily time step data, lengths of the simulation period, various hydro-climatological outputs and use of reasonable hydrological model.

Climate model faces considerable difficulties in simulating the rainfall characteristics of southwest summer monsoon. In this study, the dynamical downscaling of European Centre for Medium-Range Weather Forecast’s (ECMWF’s) ERA-Interim (EIN15) has been utilized for the simulation of Indian summer monsoon (ISM) through the Regional Climate Model version 4.3 (RegCM-4.3) over the South Asia Co-Ordinated Regional Climate Downscaling EXperiment (CORDEX) domain. The complexities of model simulation over a particular terrain are generally influenced by factors such as complex topography, coastal boundary, and lack of unbiased initial and lateral boundary conditions. In order to overcome some of these limitations, the RegCM-4.3 is employed for simulating the rainfall characteristics over the complex topographical conditions. For reliable rainfall simulation, implementations of numerous lower boundary conditions are forced in the RegCM-4.3 with specific horizontal grid resolution of 50 km over South Asia CORDEX domain. The analysis is considered for 30 years of climatological simulation of rainfall, outgoing longwave radiation (OLR), mean sea level pressure (MSLP), and wind with different vertical levels over the specified region. The dependency of model simulation with the forcing of EIN15 initial and lateral boundary conditions is used to understand the impact of simulated rainfall characteristics during different phases of summer monsoon. The results obtained from this study are used to evaluate the activity of initial conditions of zonal wind circulation speed, which causes an increase in the uncertainty of regional model output over the region under investigation. Further, the results showed that the EIN15 zonal wind circulation lacks sufficient speed over the specified region in a particular time, which was carried forward by the RegCM output and leads to a disrupted regional simulation in the climate model.

This study aimed at evaluating the three advanced satellite precipitation products (SPPs), i.e. CMORPH, TRMM 3B42V7 and PERSIANN, against the ground observation to evaluate their performance in detecting rain, capturing storms and rainfall pattern during 2014–2015 extreme flood events at three different river basins in Peninsular Malaysia (Kelantan, Langat and Johor river basins). Several spatial interpolation methods, including Arithmetic Mean, Thiessen Polygon, Inverse Distance Weighting, Ordinary Kriging and Spline were applied on the ground observations to transform the point-based precipitation into areal precipitation. Slight variations in the interpolated values were found, but overall it was comparable. Based on the daily rainfall data for the duration of 62 days, this study found that all SPPs performed with acceptable accuracy, as shown by the Kelantan river basin; however, these SPPs did not estimate accurately for Langat and Johor river basins. Overall, TRMM and CMORPH outperformed PERSIANN for the Langat and Johor river basins. In conclusion, all SPPs were capable of predicting heavy rainfall during the northeast monsoon and the level of accuracy is promising for the northern part of Peninsular Malaysia. However, as for the rest of the region, careful consideration should be given when applying the SPPs.

This study evaluates the potential of AMSR2 (Advance Microwave Scanning Radiometer2) data for the estimation of Volumetric Soil Moisture (VSM) for bare and agricultural areas. At the first step, the sensitivity of the Microwave Polarization Difference Index (MPDI) to variations in soil and vegetation characteristics were examined at different frequencies. At lower frequencies, the signal attenuation due to vegetation is minimal and thus, denser vegetation usually depolarizes the soil emission. Interestingly, the results also reveal that at higher frequencies, the sensitivity of V and H polarizations over relatively dense vegetation covers is not the same at all. Therefore, MPDI at both low and high frequencies can be a good indicator of the soil moisture and Vegetation Water Content (VWC), respectively. After evaluation of AMSR2 datasets, a model called Multi-channel/MPDI-based Land Parameters Retrieval Model (MMLPRM) is proposed. The MMLPRM optimizes optical depth of vegetation and soil dielectric constant, with simultaneous retrieval of soil moisture and surface temperature by using the AMSR2 brightness temperature data. This algorithm also includes the surface roughness parameters to increase the soil moisture retrieval efficiency. In this way, calibration and validation have been done, using in situ observations of 50 monitoring stations obtained from the International Soil Moisture Network (ISMN) over the United States. Consequently, the analysis on the MMLPRM retrieval model demonstrates its potential and usefulness for soil moisture retrieval. The outcome of this study will help in estimating the accurate soil moisture to optimize the irrigation management strategies and help in water conservation.

Soil moisture (SM) plays an important role in the water and energy exchanges that occur in the terrestrial surface. Soil moisture can be retrieved at a larger scale by using the visible and InfraRed (IR) bands as well as through the microwave remote sensing. Because of very high importance of SM for variety of applications, there are now two dedicated microwave satellites in the Earth’s orbit for soil moisture retrieval from space. The first, Soil Moisture and Ocean Salinity (SMOS) satellite has been launched by the European Space Agency in November 2009 and second is Soil Moisture Active and Passive (SMAP) launched by the National Aeronautics and Space Administration (NASA) in January 2015. In this review, brief background of soil moisture retrieval algorithms are presented with different applications in the area of water resources. The first section provides the introduction of the soil moisture, presents several in situ techniques for measurement of soil moisture and soil moisture retrieval algorithms from visible/IR and microwave remote sensing. Section 2 describes the satellite soil moisture applications in water resources.

Floods are among the most destructive natural disasters causing huge loss to life and property. Any flood management strategy requires floodplain mapping through discrimination of the flood prone areas. The city of Varanasi or Benaras is believed to be the oldest continuously inhabited city of the world. This study aims to develop tools for mapping and discrimination of floodplain of river Ganga at Varanasi. During 2014 floods, the flooded areas were extracted through Normalized Difference Water Index (NDWI) and by Modified NDWI (MNDWI) using the NIR and SWIR bands separately from that of the Landsat 8 satellite imagery. The inundated areas were then identified through Support Vector Machines (SVMs) classification. The results reveal that the MNDWI images provide a better result for flood discrimination than the NDWI images. Ground based measurements for floodplain distance varied between 11 ± 5 m at Janki ghat (bank) and 80 ± 5 m at Asi ghat. The validation between measured and SVMs derived values indicate a strong positive correlation of 0.88 and a low value of Root Mean Square Error (RMSE) of 12.62. The t-test is suggestive of no significant difference between the observed and SVMs values at 95% confidence level, indicating a satisfactory performance of the SVMs for floodplain mapping using Landsat 8 imagery. Therefore, the methodology proposed in this study provides a novel and robust way for floodplain mapping and has potential applications in disaster management and mitigation in the flood affected regions.

Ozone dynamics in two urban background atmospheres over middle Indo-Gangetic Plain (IGP) were studied in two contexts: total columnar and ground-level ozone. In terms of total columnar ozone (TCO), emphases were made to compare satellite-based retrieval with ground-based observation and existing trend in decadal and seasonal variation was also identified. Both satellite-retrieved (Aura Ozone Monitoring Instrument-Differential Optical Absorption Spectroscopy (OMI-DOAS)) and ground-based observations (IMD-O3) revealed satisfying agreement with OMI-DOAS observation over predicting TCO with a positive bias of 7.24 % under all-sky conditions. Minor variation between daily daytime (r = 0.54; R 2 = 29 %; n = 275) and satellite overpass time-averaged TCO (r = 0.58; R 2 = 34 %; n = 208) was also recognized. A consistent and clear seasonal trend in columnar ozone (2005-2015) was noted with summertime (March-June) maxima (Varanasi, 290.9 ± 8.8; Lucknow, 295.6 ± 9.5 DU) and wintertime (December-February) minima (Varanasi, 257.4 ± 10.1; Lucknow, 258.8 ± 8.8 DU). Seasonal trend decomposition based on locally weighted regression smoothing technique identified marginally decreasing trend (Varanasi, 0.0084; Lucknow, 0.0096 DU year-1) especially due to reduction in monsoon time minima and summertime maxima. In continuation to TCO, variation in ground-level ozone in terms of seasonality and precursor gases were also analysed from September 2014 to August 2015. Both stations registered similar pattern of variation with Lucknow representing slightly higher annual mean (44.3 ± 30.6; range, 1.5-309.1 μg/m3) over Varanasi (38.5 ± 17.7; range, 4.9-104.2 μg/m3). Variation in ground-level ozone was further explained in terms water vapour, atmospheric boundary layer height and solar radiation. Ambient water vapour content was found to associate negatively (r = -0.28, n = 284) with ground-level ozone with considerable seasonal variation in Varanasi. Implication of solar radiation on formation of ground-level ozone was overall positive (Varanasi, 0.60; Lucknow, 0.26), while season-specific association was recorded in case of atmospheric boundary layer.

Forests are the potential source for managing carbon sequestration, regulating climate variations and balancing universal carbon equilibrium between sources and sinks. Further, assessment of biomass, carbon stock, and its spatial distribution is prerequisite for monitoring the health of forest ecosystem. Moreover, vegetation field inventories are valuable source of data for estimating aboveground biomass (AGB), density, and the carbon stored in biomass of forest vegetation. In view of the importance of biomass, the present study makes an attempt to estimate temporal AGB of Tripura State, India, using Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI), leaf area index (LAI) and the field inventory data through geospatial techniques. A model was developed for establishing the relationship between biomass, LAI, and NDVI in the selected study site. The study also aimed to improve method for quantifying and verifying inventory-based biomass stock estimation. The results demonstrate the correlation value obtained between LAI and NDVI were 0.87 and 0.53 for the years 2011 and 2014, respectively. The correlation value between estimated AGB with LAI were found as 0.66 and 0.69, while with NDVI, the values were obtained as 0.64 and 0.94 for the years 2011 and 2014, respectively. The regression model of measured biomass with MODIS NDVI and LAI was developed for the data obtained during the period 2011–2014. The developed model was used to estimate the spatial distribution of biomass and its relationship between LAI and NDVI. The R2 values obtained were 0.832 for estimated and the measured AGB during the training and 0.826 for the validation. The results indicate that the methodology adopted in this study can help in selecting best fit model for analyzing relationship between AGB and NDVI/LAI and for estimating biomass using allometric equation at various spatial scales. The developed output thematic map showed an average biomass distribution of 32–94 Mg ha−1. The highest biomass values (72–95 Mg ha −1) was confined to the dense region of the forest while the lowest biomass values (32–46 Mg ha−1) was identified in the outer regions of the study site.

The SCATSAT-1 satellite data can be used for various applications in the field of agriculture. The main aim of the study is to investigate the water cloud model (WCM) for backscattering simulation by using the field-measured soil moisture in order to validate the SCATSAT-1 measured backscattering. WCM requires various input datasets for simulation of backscattering such as vegetation parameters A and B and soil parameters C and D, which can be estimated by Non-linear least square fitting method by using with experimental dataset. The results showed that the simulated WCM values are well correlated with the backscattering of SCATSAT-1 satellite data. However, it can be further improved when each parameter of WCM is generated by using the ground-based measurements. In this study, some progress has been made toward backscattering simulations using the SCATSAT-1; however, it can be further refined with the advancement in the retrieval algorithms and sensor sensitivity.

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec&reg, 3, where the spectrum varies between 350 and 2500 nm, (ii) build field- and lab-based spectral libraries, (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer, (iv) identify the specific spectral regions associated to the modelling of HMSC, and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations.

Morphometric analysis is a promising technique for watershed management. It provides quantitative descriptions of river basin and useful for understanding the behaviour of basin. This study is conducted in Pahuj river basin (Bundelkhand Region) Jhansi, Central India to understand the basin characteristics for watershed prioritization. The Shuttle Radar Topography Mission satellite (SRTM) is used to derive the Digital Elevation Model (DEM) and for creation of thematic layers such as drainage order, drainage density and slope map. In total, 20 mini-watersheds are generated for understanding the morphometric parameters and estimating the compound factor for mini-watersheds. For watershed prioritization, soil hydraulic parameter, compound factor and monthly average monsoon precipitation from TRMM (Tropical Rainfall Measure Mission) for 18 years period (1998–2015) are used. The overall analysis indicates that the mini-watershed numbers 18, 19 needs utmost attention for water conservation followed by mini-watershed number 20. Our results are also of considerable scientific and practical value to the wider scientific community, given the number of practical applications and research studies in which morphometric analysis are needed.

Correcting radiometric biases is crucial prior to the use of satellite observations in a physically based retrieval or data assimilation system. This study proposes an algorithm – RARMA ( R adiometric A djustment using R educed M ajor A xis) for correcting the radiometric biases so that the observed radiances coincide with the simulation of a radiative transfer model. The RARMA algorithm is a static bias correction algorithm, which is developed using the reduced major axis (RMA) regression approach. NOAA's Community Radiative Transfer Model (CRTM) has been used as the basis of radiative transfer simulation for adjusting the observed radiometric biases. The algorithm is experimented and applied to the recently launched Global Precipitation Measurement (GPM) mission's GPM Microwave Imager (GMI). Experimental results demonstrate that radiometric biases are apparent in the GMI instrument. The RARMA algorithm has been able to correct such radiometric biases and a significant reduction of observation residuals is revealed while assessing the performance of the algorithm. The experiment is currently tested on clear scenes and over the ocean surface, where, surface emissivity is relatively easier to model, with the help of a microwave emissivity model (FASTEM-5).

The study of long-term precipitation record is critically important for a country, whose food security and economy rely on the timely availability of water. In this study, the historical 102-year (1901–2002) rainfall data of the Sindh River basin (SRB), India, were analyzed for seasonal and annual trends. The Mann–Kendall test and Sen's slope model were used to identify the trend and the magnitude of the change, respectively. Spatial interpolation technique such as Kriging was used for interpolating the spatial pattern over SRB in GIS environment. The analysis revealed the significantly increasing precipitation trend in both seasonal and annual rainfall in the span of 102 years.

Summary This study explores the performance of soil moisture data from the global European Centre for Medium Range Weather Forecasts (ECMWF) ERA interim reanalysis datasets using the Weather Research and Forecasting (WRF) mesoscale numerical weather model coupled with the Noah Land surface model for hydrological applications. For evaluating the performance of WRF for soil moisture estimation, three domains are taken into account. The domain with best performance is used for estimating the soil moisture deficit (SMD). Further, several approaches are presented in this study to evaluate the efficiency of WRF simulated soil moisture for SMD estimation and compared against Soil Moisture and Ocean Salinity (SMOS) downscaled and non-downscaled soil moisture. In this study, the first approach is based on the empirical relationship between WRF soil moisture and the SMD on a continuous time series basis, while the second approach is focused on the vegetation cover impact on SMD retrieval, depicted in terms of growing and non-growing seasons. The linear growing and non-growing seasonal model in combination performs well with the NSE = 0.79, RMSE = 0.011 m; Bias = 0.24 m, in comparison to linear model (NSE = 0.70, RMSE = 0.013 m; Bias = 0.01 m). The performance obtained using WRF soil moisture is comparable to SMOS level 2 product but lower than the downscaled SMOS datasets. The results indicate that methodologies could be useful for modelers working in the field of soil moisture information system and SMD estimation at a catchment scale. The study could be useful for ungauged basins that pose a challenge to hydrological modeling due to unavailability of datasets for proper model calibration and validation.

This paper describes the validation of the SimSphere SVAT (Soil–Vegetation–Atmosphere Transfer) model conducted at a range of US and Australian ecosystem types. Specific focus was given to examining the models' ability in predicting shortwave incoming solar radiation (Rg), net radiation (Rnet), latent heat (LE), sensible heat (H), air temperature at 1.3 m (Tair 1.3 m) and air temperature at 50 m (Tair 50 m). Model predictions were compared against corresponding in situ measurements acquired for a total of 72 selected days of the year 2011 obtained from eight sites belonging to the AmeriFlux (USA) and OzFlux (Australia) monitoring networks. Selected sites were representative of a variety of environmental, biome and climatic conditions, to allow for the inclusion of contrasting conditions in the model evaluation. Overall, results showed a good agreement between the model predictions and the in situ measurements, particularly so for the Rg, Rnet, Tair 1.3 m and Tair 50 m parameters. The simulated Rg parameter exhibited a root mean square deviation (RMSD) within 25 % of the observed fluxes for 58 of the 72 selected days, whereas an RMSD within ~ 24 % of the observed fluxes was reported for the Rnet parameter for all days of study (RMSD = 58.69 W m−2). A systematic underestimation of Rg and Rnet (mean bias error (MBE) = −19.48 and −16.46 W m−2) was also found. Simulations for the Tair 1.3 m and Tair 50 m showed good agreement with the in situ observations, exhibiting RMSDs of 3.23 and 3.77 °C (within ~ 15 and ~ 18 % of the observed) for all days of analysis, respectively. Comparable, yet slightly less satisfactory simulation accuracies were exhibited for the H and LE parameters (RMSDs = 38.47 and 55.06 W m−2, ~ 34 and ~ 28 % of the observed). Highest simulation accuracies were obtained for the open woodland savannah and mulga woodland sites for most of the compared parameters. The Nash–Sutcliffe efficiency index for all parameters ranges from 0.720 to 0.998, suggesting a very good model representation of the observations. To our knowledge, this study presents the most detailed evaluation of SimSphere done so far, and the first validation of it conducted in Australian ecosystem types. Findings are important and timely, given the expanding use of the model both as an educational and research tool today. This includes ongoing research by different space agencies examining its synergistic use with Earth observation data towards the development of global operational products.