Search

Methane (CH4) and nitrous oxide (N2O) emissions coupled with climate change are issues which are of great concern for modern rice cultivation. It is very difficult and costly affair to quantify the CH4 and N2O emissions at regional and national scales due to large scale spatial and temporal variability in soil and crop management practices. In this study, the denitrification and decomposition (DNDC) model was used for simulating the CH4 and N2O gas emissions from rice fields in Eastern India. For simulating the gaseous emission, the maps of pH, bulk density, soil organic carbon and clay content were prepared using geostatistics and ordinary kriging with study area divided into 1178 grids with an area of 32 ha for each grid. The maps along with other datasets used for running the DNDC model were compiled as model input parameter. The model was then applied for simulation of methane and nitrous oxide emissions from rice fields with various management practices. Simulated CH4 emission in the study area ranged from 9.38 to 110.63 kg C ha−1 and N2O emissions ranged from 0.01 to 1.82 kg N ha−1 as simulated by the DNDC for the crop growing season. Simulated CH4 emission ranged from 0.07 to 1.15 kgC/ha/day, whereas N2O reached upto 8.96 g N ha−1 day−1. The study suggested that the DNDC model can be used for estimating the CH4 and N2O emissions by capturing the information about the different crop management practices from rice fields.

There is enormous scope and prospective of crop yield prediction at finer scale for both farm-level crop management as well as for crop insurance settlement at gram panchayat (GP) level in India. Now with the advent of satellite sensors like the MSI from Sentilnel-2 with fine spatial resolution, the possibility of generating frequent information on crop condition at field scale is increasing. This study demonstrated the combined use of high-resolution data from Sentinel-2 (10 m and 20 m); moderate-resolution data from MODIS (500 m) and coarser-resolution radiation data from INSAT-3D (4 km) for estimating yield of three major crops of India at GP and taluka level using a semi-physical model based on crop-specific radiation use efficiency. The novelty of this study lies in the data fusion approach using parameters from multiple spatial resolution of Geostationary and Lower Earth Orbiting satellites within the basic semi-physical model framework. The methodology has been demonstrated in Cuttack district of Odisha for rice; Rajkot district of Gujarat for cotton; and Indore district of MP and Fatehabad district of Haryana for wheat. We validated our result at GP, taluka and district level. At GP level, the root mean square error (RMSE) was found to be 16.5% for rice and 5.8% for wheat in Indore district. At taluka level, the RMSE was found to be 15%, 5.7%, 4.4% and 7.4% for rice, wheat in Indore district, wheat in Fatehabad district and cotton, respectively. The study concluded that high resolution remote sensing data would be of immense use for finer scale yield estimation, which can be aggregated at GP and taluka level with satisfactory accuracy (p = 95%).

Feed and fodder comprises about 65% of the cost of milk production of a dairy industry. It is a crucial input for enhancing the milk production. To address the issue of fodder availability at first, its assessment is required. Thus, we have implemented remote sensing technique for fodder crop assessment at state level to create a baseline for fodder crop availability for dairy managers to plan for its procurement during deficit and for better management purposes during its excess. We have devised a technique for remote sensing-based fodder crop assessment based on spectral pattern of growth, i.e. normalised difference vegetation index profile and land surface wetness index profile of series of IRS LISS-III satellite data taken during the crop growth cycle for a hybrid method of crop classification. Second objective to address the issue of mitigating the deficit of fodder crops, we have demonstrated the satellite derived intersection of probable high soil wetness area and available current fallows during a crop growing season which can be utilised for growing fodder crops. For macro-level planning in a state for developing new fodder-growing areas, we have demonstrated the availability of soil wetness factor from SMAP data. Fallow land available between two cropping seasons can be identified through remote sensing for growing short duration fast growing fodder crops. This project has been a demonstration project for AMUL in Gujarat to implement it subsequently at national level.

Present study was carried out to develop multiple linear regression (MLR) model of surface CH4 flux emission from monthly atmospheric clearness index (8 km), day-night land surface temperature (LST) at 1 km and surface soil moisture (25 km) from Kalpana-1, MODIS TERRA and GCOM-W1 satellites, respectively. All these products were aggregated to GOSAT level-4A product resolution. 2° × 2° grids representing homogeneous agro-ecosystems were used to draw data samples. Initial results showed that methane flux (from GOSAT) produced significant coefficient of determination (R2 = 0.84) with tri-variate (LST, surface soil moisture and atmospheric transmissivity) as compared to bi-variate (LST-soil moisture, LST-atmospheric transmissivity, soil moisture-atmospheric transmissivity) MLR models. These have been utilised for predicting surface methane flux for monthly scale. Validation of predicted methane flux with actual GOSAT methane flux was carried out and RMSE of 4.2–15.9% was obtained using variance-based bias correction. All these scaling models may be utilised to predict CH4 flux at regional level using high-resolution LST from thermal remote sensing and soil moisture from Synthetic Aperture Radar.

Accurate estimation of crop evapotranspiration (ET) is a key factor in crop water scheduling. The objective of this study was to estimate ET from the high-resolution satellite remote sensing data with integration of in situ observation. The surface energy balance model, Mapping Evapotranspiration with Internalized Calibration (METRIC) was utilised in this study for its simplicity, advantages, and effectiveness. It is a one-source model, which calculates the net radiation, soil heat flux, and sensible heat flux at every pixel level, and estimates the latent heat flux as the residual term in that energy budget equation. Intermediate steps like calculation of NDVI, surface temperature, and albedo served as important input parameters for ET estimate. Landat-8 satellite images were used to compute the ET in paddy field near CRRI, Cuttack, Odisha state in eastern India. Results indicated that the METRIC algorithm provided reasonably good ET over the study area with marginal overestimation in comparison to field observation by eddy covariance data. The satellite-based ET estimates represented in spatial scale has potential in improving irrigation scheduling and precise water resource management at local scales.

Airborne Visible InfraRed Imaging Spectrometer — Next Generation (AVIRIS-NG) data with high spectral and spatial resolutions are used for mapping metasediments in parts of Banswara district, Rajasthan, India. The AVIRIS—NG image spectra of major metasedimentary rocks were compared with their respective laboratory spectra to identify few diagnostic spectral features or absorption features of the rocks. These spectral features were translated from laboratory to image and consistently present in the image spectra of these rocks across the area. After ensuring the persistency of absorption features from sample to image pixels, three AVIRIS—NG based spectral indices is proposed to delineate calcareous (dolomite), siliceous (quartzite) and argillaceous (phyllite) metasedimentary rocks. The index image composite was compared with the reference lithological map of Geological Survey of India and also was validated in the field. The study demonstrates the efficiency of AVIRIS — NG data for mapping metasedimentary units from the Aravalli Supergroup that are known to host strata bound mineral deposits.

The present study deals with analyzing forest health, its parameters, and suitability of hyperspectral data for vegetation health-related studies. Sholayar reserve forest in Kerala has a huge reserve of equatorial moist evergreen forest and demands preservation in every respect. Due to increased human interferences coupled with possible climate change, its health is undergoing a stage of deterioration. Stress levels in the canopy were assessed using a number of stress-related pigments. Detailed study of vegetation response to canopy leaf pigments have been carried out in the study. Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data provides immense possibilities to study a number of stress-related pigments like anthocyanin, carotenoid, lignin, chlorophyll-a, b etc. Dominant species in these forests are Holigarna arnottiana, Grevillea robusta, Grewia tiliifolia, Syzygium cumini, Alstonia Scholaris, Cinnamomum verum, Artocarpus heterophyllus, Bischofia javanica, Mangifera indica, Bombax ceiba, Anogeissus latifolia, Terminalia paniculata etc. Apart from luscious natural vegetation, plantation of teak (Tectona Grandis), rubber (Hevea brasiliensis), tea (Camellia sinensis), Coffee (Coffee Arabica), Palm-Oil tree (Elaeis guineensis) etc. also exists. Field data pertaining to one of the selected pigments was correlated with remotely sensed pigment estimates. Correlation of field measured chlorophyll concentration and EVI showed R2 = 0.421. Similarly, the anthocyanin index showed a correlation of R2 = 0.319. In the Sholayar Reserve Forest (493.0 km2) an area of 141.0 km2 was found to be in a healthy state. Whereas about 218.0 km2 of area exhibit moderately healthy condition and 77.0 km2 area was in the least healthy state.

A field experiment was conducted in 2015 to study the land surface energy fluxes from tropical lowland rice paddy in eastern India with an objective to determine the mass, momentum, and energy exchange rates between rice paddies and the atmosphere. All the land surface energy fluxes were measured by eddy covariance (EC) system (make Campbell Scientific) in dry season (DS, 1–125 Julian days), dry fallow (DF, 126–181 Julian days), wet season (WS, 182–324 Julian days), and wet fallow (WF, 325–365 Julian days). The rice was cultivated in dry season (January–May) and wet season (July–November) in low wet lands and the ground is kept fallow during the remainder of the year. Results showed that albedo varied from 0.09 to 0.24 and showed positive value from morning 6:00 h until evening 18:00 h. Mean soil temperature (Tg) was highest in DF, while the skin temperature (Ts) was highest in WS. Average Bowen ratio (B) ranged from 0.21 to 0.64 and large variation in B was observed during the fallow periods as compared to the cropping seasons. The magnitude of aerodynamic, canopy, and climatological resistances increased with the progress of cropping season and their magnitudes decreased during the end of both cropping seasons and found minimum during the fallow periods. At a constant vapor pressure deficit (VPD) at 0.16, 0.18, 0.15, and 0.43 kPa, latent heat flux (LE) initially increased, but later it tended to level off with an increase in VPD. The actual evapotranspiration (ETa) during both the cropping seasons was higher than the fallow period. This study can be used as a source of default values for many land surface energy fluxes which are required in various meteorological or air-quality models for rice paddies. A larger imbalance of energy was observed during the wet season as the energy is stored and perhaps advected in the fresh water.

Heavy rains causing a flood like situation affect the grains stored in godowns. To save the grains from floods, they have to be transferred to safer places. Historical flood layers derived using the remote sensing data can be helpful in determining the safe place for transferring the grains from the flood affected godowns. Analyzing of satellite data for determining the safer locations needs specialized skills and GIS software’s. To overcome this, a web based information system, capable of organizing, hosting, and sharing of the flood layers and other spatial datasets as open geospatial consortium compatible web processing services is needed. For transferring the grains, the management must have the existing stock information of the flood affected godown as well as stock information of the godown where the grains are planned to be shifted. Mobile application, capable of reporting the stock information along with the godown location and ground photographs can be an efficient solution to do so. This paper presents work on web processing services integrated with mobile application using open source technologies to identify suitable grain godowns location.

Assessing the nature and extent of damage due to natural calamities remains one of the thrust areas in monitoring resource inventory through remote sensing. The effect of the cyclone Phailin and the post-incessant rains during second fortnight of October 2013 on coastal Odisha was studied in terms of rice area flooded, submerged and damaged. Multi-temporal SAR data were analysed to obtain the rice mask, and from this rice mask, the flood affected rice area was determined. Taluka-wise and district-wise crop loss proportion was estimated, and the overall production loss has been estimated. SAR data aided in delineation of flooded regions, while AWiFS NDVI data of subsequent dates showed both continued inundation and crop vigour status post-flood time period. The ground truth indicated that a major portion of the inundated region was not rice but was typha grasses and harvested rice field which should not be accounted as damage to rice crop. The damage on crop yield was difficult to assess; however, the inundation of the crop at panicle initiation and flowering would have impact on grain filling (results in chaffiness) and was considered as completely damaged. Most of the current inundated rice regions fall in this category. It was estimated that a total of 0.167 million hectares and 0.37 million tons of rice crop was lost in the cyclone and floods. The district-level percentage area of rice flooded was communicated to State Remote Sensing Centre in four days timeframe. The overall accuracy obtained for the validation of the ground truth sites was 91.5 %.

The regional impacts of future climate changes are principally driven by changes in energy fluxes. In this study, measurements on micrometeorological and biophysical variables along with surface energy exchange were made over a coniferous subtropical chir pine (Pinus roxburghii) plantation ecosystem at Forest Research Institute, Doon valley, India. The energy balance components were analyzed for two years to understand the variability of surface energy fluxes, their drivers, and closure pattern. The period covered two growth cycles of pine in the years 2010 and 2011 without and with understory growth. Net short wave and long wave radiative fluxes substantially varied with cloud dynamics, season, rainfall induced surface wetness, and green growth. The study clearly brought out the intimate link of albedo dynamics in chir pine system with dynamics of leaf area index (LAI), soil moisture, and changes in understory background. Rainfall was found to have tight linear coupling with latent heat fluxes. Latent heat flux during monsoon period was found to be higher in higher rainfall year (2010) than in lower rainfall year (2011). Higher or lower pre-monsoon sensible heat fluxes were succeeded by noticeably higher or lower monsoon rainfall respectively. Proportion of latent heat flux to net radiation typically followed the growth curve of green vegetation fraction, but with time lag. The analysis of energy balance closure (EBC) showed that the residual energy varied largely within ±30% of net available energy and the non-closure periods were marked by higher rainspells or forced clearance of understory growths.

The time of forcing of spatial LAI to crop models at single or multiple stages is important to simulate crop biomass and yield in varying agro-climatic conditions and scales. The high temporal resolution (5-day) by Advanced Wide Field Sensor (AWiFS) on-board Resourcesat-1 Satellite IRS-P6 with 56 m spatial resolution and large swath (740 km) has substantially increased the availability of regional clear sky optical remote sensing data. The present study aimed at developing empirical vegetation index VI-LAI models for wheat using AWiFS optical data in four bands and in-situ measurements sampled over five different agro-climatic regions (ACRs) during 2005–2006 followed by validation during 2006–2007. While nonlinear relations exist for all the three normalized indices such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and Green NDVI, linear relation was the best fit for ratio vegetation index (RVI). Both NDVI and RVI models generally showed better correlation ranges (0.65–0.84 for NDVI and 0.37–0.76 for RVI) than other indices. The common NDVI-LAI model was found to produce lower root mean square errors (RMSE) between 0.5 and 1.1 from pooled model than those between 0.5 and 1.32 from regional models. The rate of substantial increase in errors from NDVI-LAI model (RMSE of modeled LAI: 0.85 to 1.28) as compared to RVI-LAI model (RMSE of modeled LAI: 1.12 to 1.17) at LAI greater than 3, than below 3 revealed the early saturation of NDVI than RVI. It is therefore recommended that LAI estimates can be used to force crop simulation model upto early vegetative stage based on NDVI and maximum vegetative to reproductive stages based on RVI.

Albedo determines radiation balance of land (soil-canopy complex) surface and influence boundary layer structure of the atmosphere. Accurate surface albedo determination is important for weather forecasting, climate projection and ecosystem modelling. Albedo-rainfall feedback relationship has not been studied so far using observations on spatial scale over Indian monsoon region due to lack of consistent, systematic and simultaneous long-term measurements of both. The present study used dekadal (ten-day) composite of satellite (e.g. NOAA) based Pathfinder AVHRR Land (PAL) datasets between 1981 and 2000 over India (68–100°E, 5–40°N) at 8 km spatial resolution. Land surface albedo was computed using linear transformation of red and near infrared (NIR) surface reflectances. The cloud effects were removed using a smoothening filter with harmonic analysis applied to time series data in each year. The monthly, annual and long term means were computed from dekadal reconstructed albedo. The mean per year and coefficient of variation (CV) of surface albedo over seventeen years, averaged over Indian land region, were found to show a significantly decreasing (0.15 to 0.14 and 60 to 40%, respectively) trend between 1981 and 2000. Among all the land use patterns, the inter-annual variation of albedo of Himalayan snow cover showed a significant and the steepest reducing trend (0.42 – 0.35) followed by open shurbland, grassland and cropland. No significant change was noticed over different forest types.. This could be due to increase in snow melting period and snow melt area. A strong inverse exponential relation (correlation coefficient r = 0.95, n = 100) was found between annual rainfall and annual albedo over seven rainfall zones. The decreasing trend in snow-albedo of accumulation period (September to March) follows the declining trend in measured south-west monsoon rainfall between 1988 (980 mm) to 1998 (880 mm) over India. This finding perhaps suggests the possible reversal of reported coupling of increased snowfall followed by lower monsoon rainfall.

To study impact of climate change on vegetation time series vegetation index has a vital role to know the behaviour of vegetation dynamics over a time period. INSAT 3A CCD (Charged Couple Device) is the only geostationary sensor to acquire regular coverage of Asia continent at 1 km × 1 km spatial resolution with high temporal frequency (half-an-hour). A formulation of surface reflectances in red, near infrared (NIR), short wave infrared (SWIR) and NDVI from INSAT 3A CCD has been defined and integrated in the operational chain. The atmospheric correction of at-sensor reflectances using SMAC (Simple Model for Atmospheric Correction) model improved the NDVI by 5–40% and also increased its dynamic range. The temporal dynamics of 16-day NDVI composite at 0500 GMT for a growing year (June 2008–March 2009) showed matching profiles with reference to global products (MODIS TERRA) over known land targets. The root mean square deviation (RMSD) between the two was 0.14 with correlation coefficient (r) 0.84 from 200 paired datasets. This inter-sensor cross-correlation would help in NDVI calibration to add continuity in long term NDVI database for climate change studies.

The retrieval of land (soil-vegetation complex) surface temperature (LST) was carried out over semi-arid mixed agriculture landscape of Gujarat using thermal bands (channel 4 and 5) and ground emissivity from atmospherically corrected NDVI of NOAA AVHRR LAC images. The atmospheric correction of Visible and NIR band reflectance was done using SMAC model. The LST computed from split-window method and subsequently corrected with fractional vegetation cover were then compared with near synchronous ground observations of soil and air temperatures made during 13–17 January and April, 1997 at five Land Surface Processes Experiment (LASPEX) sites of Anand, Sanand, Derol, Arnej and Khandha covering 100 km x 100 km. The fractional vegetation cover corrected LST at noon hrs. varied from 301.6 – 311.9K in January and from 315.8 – 325.6K in April. The LSTcorr were found to lie in the mid way between AT and ST during January. But in April, LST were found to be more close to ST which may be due to relatively poor vegetation growth as indicated by lower NDVI values in April indicating more contribution to LST from exposed soil surface.