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Efficient discrimination of diverse kharif crops, remains crucial for crop monitoring and production forecasting, and plays a pivotal role in decision-making for food security in India. This study aims to harness temporal backscatter data from EOS-04 C-band synthetic aperture data (SAR) payload to achieve precise discrimination among six short-duration (cereal, oilseeds, fibre) and long-duration (fibre, pulses) kharif crops. The study integrates limited ground-truth polygons and a Random Forest machine learning approach for analysing EOS-04 time-series data. The classification accuracies were found to be higher than 75% across all kharif crops, with cereals exhibiting the highest accuracy, succeeded by fibre, oilseed and pulse crops. A key focus lies in identifying optimal polarization combinations for effective discrimination among diverse kharif crop types. The study reveals that the synergistic utilization of dual polarizations outperforms individual co- or cross-polarizations, notably benefiting discrimination of cotton, soybean and groundnut crops. Horizontal–vertical polarizations are found to be most effective for achieving peak accuracies in rice and red gram crops. Furthermore, the analysis indicates a promising potential for early crop assessment, presenting an opportunity to furnish precise crop estimates at least one and a half months before the harvest. [ABSTRACT FROM AUTHOR]

• Plot level rice lodging assessment with ten band multi-spectral UAV imagery. • Identification of optimal spectral, textural, and colour features for lodging assessment. • RF-based classifier yields >95 % accuracy in lodging assessment with combined features. • Demonstrated superiority of present approach and model over comparable studies. Rice plant lodging leads to change in canopy structure, yield loss and creates a menace in harvest operations. In situ assessment of lodging is time consuming, labour intensive, inefficient and inaccurate. Its assessment contributes greatly in in-situ field management and damage analysis. In this study, imaging observations from ten-band (within 444–842 nm) multispectral camera at 0.06 m Ground Sampling Distance (GSD) on-board an unmanned aerial vehicle (UAV) were acquired over a rice research farm (22.7930 N and 72.57140 E), Anand, Gujarat in western part of India. A set of features such as spectral reflectance, vegetation indices, colour coordinates and index, textural parameters and combination of all these were used for discriminating lodged rice crop from standing ones. All these features were extracted and analysed to optimize the sensitive features followed by discrimination of these two classes of rice using ensemble learning based Random Forest (RF) classifier. The analysis revealed that Green, Red-edge and Near-infrared (NIR) bands showed most optimal spectral features for lodging detection. The mean texture of these bands was also found to be sensitive indicators for rice lodging. Combined features with RF classifier produced an overall accuracy of 96.1% with kappa coefficient (κ) of 0.92 followed by textural features with an overall accuracy of 93.5 % and κ of 0.86. Plot level lodging assessment revealed that lodged area varied from 0.1 % to 15.5 % of the cropped area over different plots. The results were validated with the visually interpreted lodged areas using RGB image that resulted into R2 of 0.97 with relative root mean square error (rRMSE) of 0.02 %. Our results conclude that multispectral UAV based remote sensing can help in rapid damage assessment and plot-level field management with high precision. [ABSTRACT FROM AUTHOR]

The first phase of an airborne science campaign has been carried out with the Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) imaging spectrometer over 22,840 sq. km across 57 sites in India during 84 days from 16 December 2015 to 6 March 2016. This campaign was organized under the Indian Space Research Organisation (ISRO) and National Aeronautics and Space Administration (NASA) joint initiative for HYperSpectral Imaging (HYSI) programme. To support the campaign, synchronous field campaigns and ground measurements were also carried out over these sites spanning themes related to crop, soil, forest, geology, coastal, ocean, river water, snow, urban, etc. AVIRIS-NG measures the spectral range from 380 to 2510 nm at 5 nm sampling with a ground sampling distance ranging from 4 to 8 m and flight altitude of 4-8 km. On-board and ground-based calibration and processing were carried out to generate level 0 (L0) and level 1 (L1) products respectively. An atmospheric correction scheme has been developed to convert the measured radiances to surface reflectance (level 2). These spectroscopic signatures are intended to discriminate surface types and retrieve physical and compositional parameters for the study of terrestrial, aquatic and atmospheric properties. The results from this campaign will support a range of objectives, including demonstration of advanced applications for societal benefits, validation of models/techniques, development of state-of-the-art spectral libraries, testing and refinement of automated tools for users, and definition of requirements for future space-based missions that can provide this class of measurements routinely for a range of important applications. [ABSTRACT FROM AUTHOR]

The annual water loss through evapotranspiration (ET) is an uncertain but significant component of India’s water budget. The present study generated independent estimates of baseline annual ET, calibrated with in situ micrometeorological data over Indian sub-continent, using surface energy balance framework and satellite-based long-term thermal remote sensing, visible and near-infrared observations as the primary data sources. Thirty years’ (1981–2010) of satellite-based ET estimates at 0.08° grid resolution were used to assess trend in regional ET, to find out change hot-spots and probable causes. Long-term collateral data, influencing ET, such as gridded (0.5° × 0.5°) annual rainfall (RF), annual mean surface soil moisture (SSM) at 25 km resolution from ESA scatterometers and annual mean incoming shortwave radiation from MERRA-2D reanalysis were also analyzed. Mean annual ET loss was found to be the highest for Indian cropland (890 Cubic Km) than forest (575 Cubic Km). Annual water consumption pattern over vegetation systems showed declining ET trend at the rate of -16 Cubic Km yr −1 upto 1995 during 30 years which might be due to declining rainfall and solar dimming. This was followed by increasing ET trend (34 Cubic Km yr −1 ). During 2001–2010, irrigated cropland showed a steep increase in water consumption pattern with an average rate of 4 Cubic Km yr −1 while grassland and forest showed declining consumption patterns since 2003 and 2007, respectively thus showing crossover points of their consumption patterns with irrigated cropland. Four agriculturally important Indian eastern, central, western and southern states showed significantly increasing ET trend with S-score of 15–25 and Z-score of 1.09–2.9 during this period. Increasing ET in western and southern states was found to be coupled with increase in annual rainfall and SSM. But in eastern and central states, no significant trend in rainfall was observed though significant increase in ET was noticed. Region-specific correlation of annual ET with natural forcing variables was higher for incoming shortwave radiation as compared to rainfall. The increase in ET over irrigated croplands as well as over some of the Indian states could be due to increase in anthropogenic factors which need more detailed investigations in future. [ABSTRACT FROM AUTHOR]

Sustainable agricultural growth and management reduces over-utilization of farm resources and squeezes risk of negative impacts on environment. Monitoring continuous crop growth and health under various conditions at different spatio-temporal resolutions is a key to assess yield stability, crop diversity, adaptability, mitigation for stress and response. The quantification of crop biophysical and biochemical variables spatially from remotely sensed data with help of spectroscopic methods provide a reliable discerning information in the context of crop foliar condition like leaf greenness, senescence, canopy density, crop growth, stress and eco-physiological processes. Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) airborne hyperspectral data offers high spatial and spectral resolution giving a unique advantage and opportunity to test retrievals of crop biophysical (BP)-biochemical (BC) variables under varying conditions over different types of crops. A hybrid inversion of leaf-canopy Radiative Transfer model – PROSAIL-D complemented with use of data driven nonlinear non-parametric methods which offer simplicity, fastness, reliability and competency is a powerful method to retrieve crop biophysical and biochemical variables. The Hyperspectral band (feature) selection is a computationally cost efficient method to overcome data redundancy in high dimensional correlated input spectral bands. The determination of optimum subset or combination of hyperspectral bands specific to retrieval of vegetation properties (including canopy effects) determined using feature selection algorithm for regression-based retrievals are active research topic unlike classification problems in which it is more common. A band selection algorithm based on Gaussian Processes Regression was used to choose most sensitive bands from in-situ biophysical-biochemical measurements and crop spectral signatures collected for analysis in two different agricultural regions: Raichur (Karnataka) and Anand (Gujarat) districts of India representing diverse landscapes, heterogeneous crop canopies and agro climatic settings. The retrieval algorithm for AVIRIS-NG image employed a decision tree ensemble algorithm Canonical Correlation Forests using the optimum subset of bands for retrieval of targeted crop variable. Validation of retrieved crop variables were done using in-situ ground observations collected over heterogeneous diverse crop landscape. The results showed chlorophyll (RMSE = 6.61 µg cm−2), equivalent water thickness (RMSE = 0.002 cm), leaf area index (RMSE = 0.35 m2/m−2) and dry matter (RMSE = 0.003 g cm−2), carotenoid (RMSE = 14.3 µg cm−2), anthocyanin (RMSE = 12.92 µg cm−2) retrieved with better accuracies. The results obtained in context of feature (band) selection approach for Radiative Transfer model inversion show overlapping of sensitive spectral bands of chlorophyll-ab, carotenoid and anthocyanin especially between narrow spectral range 480 to 560 nm as predominant reason for decreased accuracies of anthocyanin and carotenoid compared to other variable. This poses a limitation as well as opportunity for further research in signal separation in context of feature selection approach especially in context of broader spectral bandwidths. It also sets ground for further development of feature selection algorithms that use hybrid regression methods customized for crop specific traits. [ABSTRACT FROM AUTHOR]

Quantification of assured solar energy potential is essential to select locations for solar photovoltaic, thermal power plants and to quantify solar power potential. The use of remote sensing observations from geostationary satellite sensors is ideal to capture space-time variability of surface insolation. The annual clear solar energy exposure over India was determined using three years' insolation data at 8 km spatial resolution from Kalpana-1 satellite. High density solar energy pockets were diagnosed in western, central and southern India including Gujarat, Rajasthan, Madhya Pradesh, Karnataka, Tamil Nadu and Chhattisgarh states with annual solar energy exposure ranging from 2500 to 3500 kW h m-2 yr-1. [ABSTRACT FROM AUTHOR]

The untimely onset and uneven distribution of south-west monsoon rainfall lead to agricultural drought causing reduction in food-grain production with high vulnerability over semi-arid tract (SAT) of India. A combined deficit index (CDI) has been developed from tri-monthly sum of deficit in antecedent rainfall and deficit in monthly vegetation vigor with a lag period of one month between the two. The formulation of CDI used a core biophysical ( e.g. , NDVI) and a hydro-meteorological ( e.g. , rainfall) variables derived using observation from Indian geostationary satellites. The CDI was tested and evaluated in two drought years (2009 and 2012) within a span of five years (2009–2013) over SAT. The index was found to have good correlation (0.49–0.68) with standardized precipitation index (SPI) computed from rain-gauge measurements but showed lower correlation with anomaly in monthly land surface temperature (LST). Significant correlations were found between CDI and reduction in agricultural carbon productivity (0.67–0.83), evapotranspiration (0.64–0.73), agricultural grain yield (0.70–0.85). Inconsistent correlation between CDI and ET reduction was noticed in 2012 in contrast to consistent correlation between CDI and reduction in carbon productivity both in 2009 and 2012. The comparison of CDI-based drought-affected area with those from existing operational approach showed 75% overlapping regions though class-to-class matching was only 40–45%. The results demonstrated that CDI is a potential indicator for assessment of late-season regional agricultural drought based on lag-response between water supply and crop vigor. [ABSTRACT FROM AUTHOR]

Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) ( e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over two different wheat growing regions, situated in different agro-climatic settings/environments: Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simulation of canopy reflectances in four AWiFS bands viz. green (0.52–0.59 μm), red (0.62–0.68 μm), NIR (0.77–0.86 μm) and SWIR (1.55–1.70 μm) were carried out to generate the look up table (LUT) using CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive wheat growing seasons (November 2005–March 2006 and November 2006–March 2007) datasets were used in this study. The empirical models were developed from first season data and second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R 2 of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise. [ABSTRACT FROM AUTHOR]

Abstract: Indian economy is largely depending upon the agricultural productivity and thus influences the trade among the SAARC countries. High-resolution and good-quality regional weather forecasts are necessary for planners, resource managers, insurers and national agro-advisory services. In this study, high resolution updated land-surface state in terms of vegetation fraction (VF) from operational vegetation index products of Indian geostationary satellite (INSAT 3A) sensor (CCD) was utilized in numerical weather prediction (NWP) model (e.g. WRF) to investigate its impact on short-range weather forecast over the control run. Results showed that the updated vegetation fraction from INSAT 3A CCD improved the low-level 24h temperature (∼18%) and moisture (∼10%) forecast in comparison to control run. The 24h rainfall forecast was also improved (more than 5%) over central and southern India with the use of updated vegetation fraction compared to control experiment. INSAT 3A VF based experiment also showed a net improvement of 27% in surface sensible heat fluxes from WRF in comparison to control experiment when both were compared with area-averaged measurements from Large Aperture Scintillometer (LAS). This triggers the need of more and more use of realistic and updated land surface states through satellite remote sensing data as well as in situ micrometeorological measurements to improve the forecast quality, skill and consistency. [Copyright &y& Elsevier]

A three-parameter sigmoidal 'local' model (climate-specific) and a 'regional' model (common for all climates) have been developed for measuring the monthly average diffuse solar radiation fraction from atmospheric transmissivity by using large, 21-year datasets (1973-1993) over four stations (Jodhpur, New Delhi, Nagpur and Kolkata). These stations represent the four 'prime' climates (arid, semi-arid, sub-humid and humid) over the Indian sub-tropics. The models have been validated with the longer time-series (10 years) of independent datasets (1994-2003) of 4 'prime' climates as well as datasets from 16 stations using one-year datasets (termed as secondary stations) of the Indian region. The monthly diffuse fraction estimates were also compared with seven globally existing models. The 'regional' model showed more accurate estimates than 'local' models over three (semi-arid, sub-humid and humid) of the four 'prime' climates. However, the different error statistics showed that the 'regional' model outperformed the globally existing models which failed to capture diffuse fraction variability over the Indian sub-tropics. The extendibility of the 'regional' model over 'secondary' stations in India showed an overall good performance with R2: 0.78-0.96 and RMSE: 0.017-0.125, except for two stations. These models are unique for Indian sub-tropics and can undoubtedly be used for predicting future diffuse solar radiation fraction from transmissivity datasets of climate simulations, and also for other meteorological, climatological, solar energy-based applications. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: Accurate pre-harvest assessment of a staple food crop is an integral part of policy formulation in relation to food security issues. Here, two different approaches were attempted to estimate wheat yield using time series multi-year satellite (MODIS Aqua) optical-thermal data from a single earth observation (EO) mission. Surface energy budgeting was used to estimate evapotranspiration in terms of latent heat fluxes from net available energy and evaporative fraction to predict wheat yield over four agro-climate zones in semi-arid climate of Gujarat, India. Satellite based estimates of latent heat fluxes were found to show substantially less error with respect to the area-averaged heat flux measurements from LAS (large aperture scintillometer) as compared to measurements from BREB (Bowen Ratio Energy Balance) alone. The deviations in satellite based zonal CWU were found to have a strong correlation (r =0.71) with the deviations from zonal wheat yield. Among both the approaches, the radiation use efficiency (RUE) based approach produced better accuracy in the predicted yield with lower root mean square error (RMSE) of 390kgha−1 (14.8% of reported mean) and higher correlation coefficient (r =0.92) than the water use efficiency (WUE) based approach (RMSE 573kgha−1, 21.8% of reported mean; r =0.80). Uncertainties in the satellite based core inputs resulted into a net 10-12% error in predicted yield in case of RUE approach. Our demonstrative case studies recommend that the coupled use of satellite observations from multiple EO missions and radiative transfer simulation would be effective to make efficiency based approaches operationally viable for regional wheat yield forecasting in near real time. [Copyright &y& Elsevier]

Time series vegetation index using observations of the earth from space platform is a valuable source to derive several plant biophysical parameters for ecological, hydrological, climate models and to study landuse land-cover change dynamics. Indian geostationary satellite (INSAT 3A) sensor (CCD) observes the earth surface with continental (Asia) coverage at 1 km × 1 km spatial resolution and hightemporal frequency (half-an-hour) at constant view direction. This study was aimed at defining and implementing an algorithm to retrieve normalized difference vegetation index (NDVI) at continental scale from INSAT 3A CCD surface reflectances in red (0.62–0.68 μm), near infrared (0.77–0.86 μm) bands and evaluate it with the global product. The methodology includes vicarious calibration, cloud screening, atmospheric correction of at-sensor reflectances and development of protocol. The temporal dynamics of 16-day NDVI composite at 0500 GMT (10 : 30 local mean time) for a growing year (June 2008–April 2009) showed matching profileswith respect to global products (e.g. MODIS TERRA) over known land targets such as agriculture, forest and desert. The root mean square deviation between the two was 0.13 with correlation coefficient (r) 0.83. The differences were attributed to surface anisotropy, view angle difference and differences in spectral bandwidths withtheir relative positions in the electromagnetic spectrum. [ABSTRACT FROM AUTHOR]

Abstract: A simplified evaporative fraction (Λ) based single-source energy balance scheme was tested with moderate resolution (∼1km) noontime satellite observations to evaluate clear sky latent heat flux (λE) estimates over diverse agricultural landscapes. This approach uses two-dimensional (2D) scatter between land surface temperature (LST) and albedo to determine Λ. The operational utility of this scheme was demonstrated for estimating regional evapotranspiration and consumptive water use during rabi (November to April) crop growing season to predict pre-harvest wheat yield (error within 15.9% of reported mean) using time series data. The existence of triangular relations between Λ and LAI (leaf area index) or NDVI (normalized difference vegetation index) was found with basal line (hypotenuse) linearly coupled with LAI or NDVI at low level of surface soil wetness. The analysis of diurnal course of in situ Λ proved the validity of constant-Λ hypothesis over pure, uniform, homogeneous crop canopies but showed irregular and wave-like patterns over heterogeneous, mixed crop canopies. The root mean square error (RMSE) of noontime and daytime average λE estimates with respect to in situ λE measurements were also smaller over homogeneous agricultural canopies (41 and 23Wm−2) with correlation coefficients (r) 0.94 and 0.96, respectively, from 135 clear sky datasets as compared to RMSE over heterogeneous ones (59 and 28Wm−2 with r =0.66 and 0.82, respectively from 22 datasets). The intercomparison with another Λ based approach (LST–NDVI 2D scatter) showed the supremacy of Λ determined from LST–albedo 2D scatter. The efficiency of LST–NDVI scatter was better during the dry down or water limited phases of crop growth only. The uncertainties of λE estimates were attributed to errors in core radiation budget inputs, relative loss of conservativeness of Λ due to canopy heterogeneity, and the inherent limitations of the single-source approach. There is further scope to reduce present λE uncertainties by combining the new findings on Λ (LST–albedo scatter)–NDVI triangular relations, diurnal Λ and two-source radiation budget. [Copyright &y& Elsevier]

Abstract: Surface soil wetness determines moisture availability that controls the response and feedback mechanisms between land surface and atmospheric processes. A study was carried out to estimate volumetric surface soil moisture content (θ v ) in cropped areas at field (<102 m) to landscape (≤103 m) scales. Triangular scatters from land surface temperature (LST) and normalized difference vegetation index (NDVI) space were utilized to obtain a soil wetness index (SWI), from which θ v was derived, with the combination of dry and wet edges using data from ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) for field scale and MODIS (MODerate resolution Imaging Spectroradiometer) AQUA for landscape scale studies. The root mean square error (RMSE) of field scale θ v estimates was higher (0.039m3 m−3) than that of the landscape scale (0.033m3 m−3). The narrow swath (∼60km) of finer resolution sensors (e.g. ASTER) often fails to capture the surface heterogeneity required in the triangle method for deriving SWI and could be one of the main reasons leading to relatively high error in θ v estimates. At both the scales, the lowest error of θ v estimates was found to be restricted within the NDVI range of 0.35–0.65. A geostatistical technique was applied to assess the influence of sub-pixel heterogeneity as an additional source of error for cross-scale comparison of θ v estimates obtained from LST–NDVI scatters. The overall errors of θ v estimates from LST–NDVI space were comparable with other globally available test results. The comparison of landscape scale θ v from MODIS AQUA with large-area global estimates from a passive microwave sensor (e.g. AMSR-E) with longer microwave frequency (e.g. C-band) yielded 75% correlation and 0.027m3 m−3 root mean square deviation (RMSD) for fractional vegetation cover less than 0.5. The study recommends the synergistic use of shorter microwave frequency (e.g. L-band) and optical–thermal infrared bands as the best way of satellite based passive soil moisture sensing for vegetated surfaces. [Copyright &y& Elsevier]

This study proposes the relationship between fungal bioaerosols and biotic stress on crops using a case study on wheat leaf rust reported from northern India. We sampled and quantified the size-resolved fungal bioaerosols using the next-generation sequencing technique from a wheat crop field during winter. Puccinia recondita, the fungal pathogen that causes wheat leaf rust, was identified during the study period. The pathogen is known for its frequent and widespread occurrence of new variants that causes disease-resistant crop varieties susceptible to infections. This reveals the need for frequent and systematic monitoring to prevent rust infection. In the current study, the size-resolved fungal bioaerosol characterization was linked to the dispersal properties of the fungal propagules, and using a theoretical dispersion model, originating source and the areas of high risk for wheat leaf rust infection were identified. Our findings may serve as a vital reference for crop pathologists, agro technologists, environmentalists, and policymakers to expand the investigation on the biotic stress caused by the invasion of fungal bioaerosols on various crops and to implement preventive measures to ensure global food security. [ABSTRACT FROM AUTHOR]

The present study employs temporal C-band Sentinel-1 synthetic aperture radar (SAR) data within the Google Earth Engine (GEE) platform to evaluate discriminability and estimate acreage of kharif rice across major Indian states. Utilizing multi-temporal Sentinel-1 Cband SAR data, including time-series cross-polarization vertical--horizontal channels, the research spanned states such as Punjab, Haryana, Uttar Pradesh, Madhya Pradesh, Bihar, Jharkhand, Chhattisgarh, Telangana, Andhra Pradesh, West Bengal, Odisha and Assam. Employing five machine learning algorithms on GEE, with random forest demonstrating high performance, achieved 98.59% accuracy and 0.92 kappa coefficient ( κ) in Odisha. Subsequently, the RF algorithm was applied for kharif rice acreage estimation, yielding overall accuracies from 88.48% to 97.28% and κ between 0.87 and 0.96 with deviations from reported acreage ranging from 0.95% to 12% across diverse states. The study underscores the efficacy of SAR data and machine learning within GEE for precise large-scale automated mapping of kharif rice. [ABSTRACT FROM AUTHOR]

Net surface radiation (Rn) at earth surface comprises of outgoing and incoming components of longwave and shortwave radiation fluxes at the surface. This influences energy and mass exchange over earth surface at sub-daily, seasonal and annual time scales. This is a critical component for land surface models which characterize climatic, ecological and biological processes. Measured net radiation datasets are rarely available and can be subjected to errors and high uncertainties due to equipment malfunctions and failure. In this study, a regional-scale model is developed and implemented to estimate the net surface radiation fluxes over Indian landmass under cloudless sky conditions using the data from an Indian Geostationary (GEO) Meteorological satellite (INSAT 3D) and polar-orbiting (LEO) MODIS-Aqua for daytime conditions. The estimates were evaluated over a 6-month period (Nov 2019–Apr 2020) during winter and summer months, at three in situ measurement sites, located in cropland and grassland. The results showed a correlation of 0.86 between satellite-based Rn estimates and in situ measurements with a root mean square error (RMSE) of 40.4 W m−2 (4.6% of measured mean) and a mean absolute error (MAE) of 5.6% of measured mean. The modelled estimates were compared with NCEP (National Center for Environmental Protection) reanalysis global surface radiation estimates diurnally. The NCEP model tends to under- or over-estimate during the study time period especially in the month of January. [ABSTRACT FROM AUTHOR]

One of the important variables in the retrieval of Land Surface Temperature (LST) is surface emissivity. The main aim of this study is to test how surface emissivity estimated using different methods affect the accuracy of the retrieved LST. The emissivity was estimated from Landsat data using different variants of the vegetation index (VI) based model and also obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset (ASTER GED). The LST was retrieved using Statistical Mono Window (SMW) and Generalized Single Channel (GSC) algorithms. The retrieved LSTs were compared with the Landsat collection-2, level-2 surface temperature (L2ST) product and also validated against ground measurements over eight sites across India. From the results, it was observed that the emissivity estimated by different methods resulted in LST with no significant differences in accuracy between them. However, the LST retrieved from the SMW algorithm performed better than the LST from the GSC algorithm. In addition, the LST from the L2ST product performed relatively poorer in comparison with the SMW algorithm. The results indicated that the accuracy of retrieved LST is highly dependent on the LST retrieval algorithm as different surface emissivity methods caused only minor variations in LST values. [ABSTRACT FROM AUTHOR]

Multi-spectral and diurnal meteorological data from optical-thermal observations of Indian Geostationary satellites were used to estimate country-scale evapotranspiration (ET). In this study radiation and vegetation products from a suite of Indian geostationary satellites, Kalpana-1 (K-1) and Indian National SATellite (INSAT) 3A Charged Couple Detector (CCD), were used. ET was derived as a function of net available energy and evaporative fraction within a single-source energy balance framework. The scaling function for albedo, net radiation and soil heat flux were developed with ground measured data from twenty-three Agro-Met Station (AMS) installed in different agro-climatic zones of India. These scaling functions were applied on the satellite data to derive intermediate inputs for surface energy balance. Further, evaporative fraction was derived from Priestley-Taylor parameter (φ) and day-time net available energy. On regional scale, pixel-by-pixel dynamic φi was computed from upper (φmax) and lower (φmin) limits of warm and wet edges of thermal inertia-Normalized Difference Vegetation Index (NDVI) triangle. Here, thermal inertia was represented by morning rise in surface brightness temperature (ΔT) between 02:30 and 06:30 Greenwich Mean Time (GMT). The dynamic φi with upper and lower limits were computed using ΔT – NDVI triangle. The derived net radiation was validated with AMS measured net radiation at seven agro-climatic zone of India showed Root mean Square Error (RMSE) between 37 and 87 W m−2. The estimated ET was validated at six agricultural and five natural grassland and wetland vegetation sites with AMS measured ET for a year. The RMSE of 10 days ET sum were varying from 7.50 to 17.70 mm for agricultural and for natural vegetation sites it varies from 8.60 to 13.10 mm. The errors variation among all site is depending on the sub-pixel heterogeneity and difference in K-1 and AMS footprints. The ET sum over agricultural kharif (June to October) and rabi (November to April) season also able distinguish normal and drought year. This study provides a robust methodology for generating near real time ET at country scale from Indian Space Research Organisation (ISRO's) present and future high-resolution geostationary satellites over India. [ABSTRACT FROM AUTHOR]

In a recent scenario, the prediction of environmental changes through plant functional types can aid in simplification of ecosystem processes. The present study attempts to identify and map plant functional types (PFTs) in the AVIRIS-NG campaign site, namely Shoolpaneshwar Wildlife Sanctuary (site id 67), using AVIRIS-NG data combined with spectral mixture analysis that accounts for endmember variability. Due to the occurrence of heterogeneous vegetation in the selected AVIRIS-NG site, the measured spectral signal for every pixel of surface reflectance data will be the outcome of fractions in which various plant functional types and the soil background exist. The interest of the present research lies in these fractions; hence spectral mixture analysis was applied. Ground truthing was carried out simultaneous to AVIRIS-NG flight pass. Six plant functional traits, namely Diameter at breast height (DBH), Height, biomass, leaf chlorophyll content (CC), Fraction of Photosynthetic Active Radiation (FPAR), and Leaf Area Index (LAI), were measured for twenty-three tree species found in the study site which were further used for plant functional grouping by applying k-means clustering algorithm. Trait-based cluster analysis classified the tree species into four plant functional types. Endmember selection from AVIRIS-NG image for these plant functional types was done using a manual field observation-based approach, which was used as input in spectral mixture analysis. The final product of the analysis is a set of fractional abundance images for each plant functional type. Considerable accuracy was obtained on validating the fractional abundance image by in situ data. The study highlighted the potential of a spectral mixture analysis classifier in identifying and mapping different plant functional types using AVIRIS-NG data when performed using an appropriate number of end members. [ABSTRACT FROM AUTHOR]

This paper primarily focuses on identifying and mapping vegetation stress caused by the impact of coal mining using airborne hyperspectral image (AVIRIS-NG) at a fine scale level and is validated using spectroscopic field data. In this work, we have calculated and tested vegetation stress-affected narrow banded vegetation indices (VIs) based on Separability (S) and Coefficient of discrimination (R2) statistical tests for the identification of suitable vegetation stress indices. We have considered the highest S and R2 values of stress indices for weighted and vegetation combined pixels' analysis. The final weighted combination index has been used for vegetation stress detection and mapping in coal mining sites. The outcome has been validated using field-based healthy and stress-affected plant spectral data and compared to ENVI software's agriculture stress tool (AST) based vegetation stress result. Some indices exhibited higher performance for vegetation stress analysis (NDVI, NSI-2, SR-1, SWST-3, SRWI, NWI-2, CSVI, HMSSI, and ARI-1), according to statistical results (S and R2) of VIs. Based on the vegetation stress index, high-stress zones are located in and around the mines whilelow-stress regions are positioned farther from the mines. The findings additionally demonstrated that the VIs-based stress result (AUC-0.69) fits the data more accurately than the ENVI's AST result (AUC-0.61). The VI's-based stress index had a clearly negative correlation with the distance of vegetation from the mines, which could be verified using the on-field NIR spectral measurements. Therefore, effective vegetation health monitoring and planning in coal mining sites might benefit from these results. [ABSTRACT FROM AUTHOR]

The identification of crop diversity in today's world is very crucial to ensure adaptation of the crop with changing climate for better productivity as well as food security. Towards this, Hyperspectral Remote Sensing (HRS) is an efficient technique that offers the opportunity to discriminate crop types based on morphological as well as physiological features due to availability of contiguous spectral bands. The current work utilized the benefits of Airborne Visible Infrared Imaging Spectrometer- New Generation (AVIRIS-NG) data and explored the techniques for classification and identification of crop types. The endmembers were identified using the Geo-Stat Endmember Extraction (GSEE) algorithm for pure pixels identification and to generate the spectral library of the different crop types. Spectral feature comparison was done among AVIRIS-NG, Analytical Spectral Device (ASD)-Spectroradiometer and Continuum Removed (CR) spectra. The best-fit spectra obtained with the Reference ASD-Spectroradiometer and Pure Pixel spectral library were then used for crop discrimination using the ten supervised classifiers namely Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), Support Vector Machine (SVM), Minimum Distance Classifier (MDC), Binary Encoding, deep learning-based Convolution Neural Network (CNN) and different algorithms of Ensemble learning such as Tree Bag, AdaBoost (Adaptive Boosting), Discriminant and RUSBoost (Random Under Sampling). In total, nine crop types were identified, namely, wheat, maize, tobacco, sorghum, linseed, castor, pigeon pea, fennel and chickpea. The performance evaluation of the classifiers was made using various metrics like Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score. The classifier 2D-CNN was found to be the best with Overall Accuracy, Kappa Coefficient, Precision, Recall and F1 score values of 89.065 %, 0.871, 87.565%, 89.541% and 88.678% respectively. The output of this work can be utilized for large scale mapping of crop types at the species level in a short interval of time with high accuracy. [ABSTRACT FROM AUTHOR]

Non-destructive estimation of chlorophyll-a (Chl-a) and chlorophyll-b (Chl-b), is required for assessment and understanding of vegetation structural and functional dynamics. We have developed an empirical model for estimating Chl-a and Chl-b for different crops using Airborne Visible-Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) data. Second derivative reflectance at 672 nm and 587 nm has shown maximum sensitivity towards Chl-a and Chl-b content, respectively. Separate linear models within a bootstrapped resampling framework were developed. During calibration, mean R2 of 0.46 and 0.51 were obtained for Chl-a and -b respectively. While during model validation, in both the cases mean R2 was found to be 0.3. This work shows the capability of airborne hyperspectral imaging data in segregating chlorophyll types using a simplistic modeling framework. [ABSTRACT FROM AUTHOR]

Timely detection of pest infestation in agricultural crops plays a pivotal role in the planning and execution of pest management interventions. In this study, a ground measured electromagnetic spectrum through hyperspectral sensing (400–2500 nm) was conducted in healthy and aphid-infested mustard crops in different regions of the Bharatpur district of Rajasthan state, India. The ground measured hyperspectral reflectance and its derivatives during the mustard aphid infestation period were used to identify the sensitive spectral regions in the electromagnetic spectrum concerning Aphid Infestation Severity Grade (AISG) to discriminate Lipaphis-infested mustard crops from the healthy ones. Further Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were utilized to identify specific spectral bands to differentiate the healthy from aphid-infested crops. The spectral regions of 493–497 nm (blue), 509–515 nm (green), 690–714 nm (red), 717–721 nm (red edge), and 752–756 nm (NIR) showed high correlation with AISG for reflectance, first and second order derivatives. Further analysis of the spectra using PCA and PLSR indicated that spectral bands of 679 nm, 746 nm, and 979 nm had high sensitivity for discriminating aphid-infested crops from the healthy ones. Average reflectance and various spectral indices such as ratio spectral index (RSI), difference spectral index (DSI), and normalized difference spectral index (NDSI) of identified spectral regions and absolute reflectance of identified specific spectral bands were used for predicting AISG. Several regression models, including PCR and PLSR, were examined to predict the AISG. PLSR was found to better predict infestation grade with RMSE of 0.66 and r2 0.71. Our outcomes counseled that hyperspectral reflectance data have the ability to detect aphid-infested severity in mustard. [ABSTRACT FROM AUTHOR]

Alternaria blight is a destructive disease of mustard crops that causes significant economic loss and usually warrants extensive pesticide application. Characterization and estimation of pest severity for better management is the need of the hour to increase the scope of advisories to the farmers. In this study, statistical approaches like multiple stepwise linear regression, principal component regression and partial least square regression have been employed to detect the disease severity of Alternaria blight in mustard leaves using hyperspectral reflectance data. The predicted values of diseased leaves in all the regression models were significantly reduced vis-à-vis healthy mustard leaves. The results of this study indicate that it is possible to detect the disease severity of Alternaria blight in mustard crops using hyperspectral reflectance data. [ABSTRACT FROM AUTHOR]

Estimation of latent heat flux at the agricultural field scale is required for proper water management. The current generation thermal sensors except Landsat-8 provide data on the order of 1000 m. The aim of this study is to test three approaches based on contextual models using only remote sensing datasets for the disaggregation of latent heat flux over India. The first two approaches are, respectively, based on the estimation of the evaporative fraction (EF) and solar radiation ratio at coarser resolution and disaggregating them to yield the latent heat flux at a finer resolution. The third approach is based on disaggregation of the thermal data and estimating a finer resolution latent heat flux. The three approaches were tested using MODIS datasets and the validation was done using the Bowen Ratio energy balance observations at five sites across India. From the validation, it was observed that the first two approaches performed similarly and better than the third approach at all five sites. The third approach, based on the disaggregation of the thermal data, yielded larger errors. In addition to better performance, the second approach based on the disaggregation of solar radiation ratio was simpler and required lesser data processing than the other approaches. In addition, the first two approaches captured the spatial pattern of latent heat flux without introducing any artefacts in the final output. [ABSTRACT FROM AUTHOR]

Agriculture productivity at spatio-temporal scales can be modelled through quantification of biophysical parameters like LAI (leaf area index) and radiation parameters from satellites. The Indian geostationary satellite INSAT 3A CCD was used to retrieve agricultural LAI at regular temporal intervals from theProSail1-D (Dimensional) canopy radiative transfer (CRT) model. TheProSailmodel was customized to simulate reflectance for three CCD spectral bands. The model was run in forward mode and then inverted by using reflectances from the CCD and the generated LUT by applying the least square distance approach to retrieve LAI for agricultural crops. Daily CCD data from January 1 to March 30, 2010 at 0700 GMT were used to retrieve agricultural crop LAI data. The validation of daily retrieved LAI was done with availablein situmeasurements over wheat crops in Punjab, Haryana and Madhya Pradesh states. The overall root mean square error (RMSE) of 0.84 with correlation of 0.8 was observed for 20in situmeasured LAI at different phenological phases of wheat crops. Retrieved INSAT CCD LAI has been compared with LAI retrieved from high-resolution IRS P6 AWiFS using an empirical approach for wheat crop. The CCD-derived wheat crop LAI showed a RMSE of 0.45 (n= 55, 14.2 percent from mean) with mean absolute error (MAE) of 0.34. It was also compared with the 8-day MODIS TERRA global LAI product from January to March 2010. The LAI profiles extracted for different regions of India representing different crops using CCD data and MODIS products were compared and an overall RMSE of 2.25 (n= 156, 73 percent from mean) with MAE of 2.85 was observed. INSAT CCD-retrieved LAI was further used for wheat yield estimation over Madhya Pradesh state. At district level, yield showed a RMSE of 516.6 kg ha−1with 29.4 percent deviation from the mean. Our demonstrative case studies recommended coupled use of satellite observations from multiple EO missions and radiative transfer simulation to make efficiency-based approaches operationally viable for regional crop yield estimation in near real time. [ABSTRACT FROM PUBLISHER]

Hyperspectral Remote Sensing (HRS) data is vital for crop growth monitoring due to availability of contiguous bands. This research work provides a new novel crop estimator model given the name “Crop Stage estimator” developed using the HRS data on an open-source R platform. The generic model structure provides an easy way to test and modify the importance of crop parameter namely Leaf Area Index to deduce crop growth stages of winter wheat (Triticum aestivum L.) particularly during –heading, tillering and booting. Further, to know the LAI variations at different agriculture sites, the best model was implemented using the AVIRISNG (Airborne Visible Near-Infrared Imaging Spectrometer - Next Generation) hyperspectral datasets. The analysis indicates that during tillering stage the performance was found best during calibration (r = 0.66, RMSE =0.40, and Bias =0.80) and validation (r = 0.98, RMSE =0.20, and Bias =0.12) in comparison to the ground measurements. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) at weekly and monthly time scales is often needed for various applications. When using remote sensing (RS)-based models, this can be achieved either by averaging all the required input variables to the intended time scale and simulating ET using models (input aggregation), or by estimating daily ET from the models and averaging to weekly or monthly ET (output aggregation). It is not clear if both these aggregation approaches yield the same outcome when using RS-based models for the estimation of ET. Another issue in obtaining ET at longer time scales is the lack of enough satellite observations to estimate ET with reasonable accuracy. This study aimed to compare the input and output aggregation approaches to obtain ET at weekly and monthly time scales using three RS ET models, namely, Priestley–Taylor Jet Propulsion Lab (PT-JPL), Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE), and Surface Temperature Initiated Closure (STIC) models. The study was conducted using in situ data over six sites of different agro-climatic conditions in India, Tunisia, and France. The results indicate that the input aggregation provided relatively better results for monthly and weekly ET values than the output aggregation, having a lower RMSE (1–40%). Further, it was found that at least seven to eight satellite observations per month are required to obtain reliable ET estimate when using RS-based models. [ABSTRACT FROM AUTHOR]

Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%-94% for AVIRI-NG and 42%-55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was developed for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (Cab) using AVIRIS-NG narrow bands. The retrieved LAI and Cab showed 19%-27% and 23%-29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662-702 and 2155-2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]