Rahul Nigam

Relative Calibration Using Natural Terrestrial Targets: A Preparation Towards Oceansat-2 Scatterometer

Scatterometer instruments transmit a series of microwave pulses and measure the returned echo to determine the normalized radar cross section (σ⁰) over the target to derive the near-ocean-surface wind vector. Accuracy of the derived wind vector over the data sparse oceans therefore depends on the accuracy of σ⁰ measurement. For this purpose, accurate calibration of the scatterometer is required. As a preparation toward calibration of the Oceansat-2 mission, of the Indian Space Research Organisation, a relative calibration technique has been proposed in this study by selecting homogeneous areas over the globe with isotropic radar response and temporally stable signature of σ⁰. For this purpose, the daily averaged σ⁰ and Level-2A (L2A) σ⁰ measurements of the QuikSCAT scatterometer have been used. Analyzing the monthly mean and standard deviation in σ⁰ for the period of 2005-2006, several regions are chosen which have a quasi-isotropic radar response and minimal temporal variation in σ⁰. The analysis shows that the selected areas over Antarctica and Greenland with permanent ice covers have temporally stable signatures of σ⁰. The regions like the Amazon forests and parts of Australia also show high temporal stability of σ⁰ but greater standard deviation than the snow-covered areas. The QuikSCAT L2A data have also been used to study the day-night variation and azimuthal dependence of the σ⁰ over these targets. The present work demonstrated that quasi-uniform natural sites such as Sahara, Amazon forest, Kutch, Greenland region, and Antarctica region, covering wide dynamic range of σ⁰, can be used for the purpose of calibration.

Impact of Kalpana-1 derived land surface albedo on short-range weather forecasting over the Indian subcontinent

The skill of weather forecasts at high spatial resolution depends on accurate representation of land surface states at appropriate spatial and temporal scales that modulate flux partitioning in the numerical weather prediction models. In this study, the Weather Research and Forecasting (WRF) model is customized to assess the impact of land surface albedo (LSA) derived from Kalpana-1 Very High Resolution Radiometer (K1VHRR) in comparison to default monthly climatological albedo from the United States Geological Survey (USGS). A two-stage upscaling of ground-measured albedo from Agro-Met Stations is performed to derive K1VHRR LSA. This combines multispectral reflectance at intermediate scales from the Advanced Wide Field Sensor on board Resourcesat-2 at Low Earth Orbiting platform and the planetary (Earth-atmosphere system) albedo from Kalpana-1 visible band at Geostationary Earth Orbiting platform. Two separate experiments, with real-time K1VHRR LSA and USGS climatological LSA (CNT), are performed to evaluate the impact of real-time K1VHRR LSA on daily WRF model forecasts during July 2009. Additional experiments are performed to assess the impact of real-time and climatological K1VHRR albedo against USGS climatological albedo based experiment. Results show that real-time K1VHRR albedo improves the surface temperature, specific humidity, and wind speed forecasts as compared to CNT experiments. The impact of climatological and real-time K1VHRR LSA is small compared to the advantage of using K1VHRR over USGS. Moreover, real-time K1VHRR albedo has additional benefits to improve the representation of seasonal variability. Results show that the real-time K1VHRR LSA has slight positive impact on rainfall forecast.

A combined deficit index for regional agricultural drought assessment over semi-arid tract of India using geostationary meteorological satellite data

Abstract 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.

Modeling temporal growth profile of vegetation index from Indian geostationary satellite for assessing in-season progress of crop area

Highlights In-season agricultural area tracking at regular interval from geostationary satellite. Modelling of temporal profile of vegetation index spread across two consecutive agriculture seasons to track crop area. The crop area estimates and their frequent updates in an agricultural growing season are essential to formulate policies of country’s food security. A new methodology has been developed with high temporal vegetation index data at 1000 m spatial resolution from Indian geostationary satellite (INSAT 3A) to track progress of country-scale rabi (post-rainy) crop area in six agriculturally dominant states of India. The 10-day (dekad) maximum normalized difference vegetation index (NDVI) composite products at 0700 GMT (Greenwich Mean Time) were generated and used in the study. A cubic function was fitted to NDVI temporal profile on the training data-sets of 2009–2010. Model parameters were standardized over 40 agroclimatic subzones, which were used to estimate rabi crop area at 10-day interval in the next two seasons. Uncertainties in the model, in terms of days, were found to be less than (3–8 days) compositing period. The INSAT-based estimates showed –18.1% to 14.6% deviations from reported rabi crop area. Subpixel heterogeneity was found to be the major cause for the delay in crop area tracking in study region. The interseasonal variability in the estimate was consistent with the reported statistics with a correlation coefficient of 0.89. A comparative study showed that INSAT estimated rabi area had 16.36% deviation from high spatial resolution AWiFS (Advanced Wide-Field Sensor)-estimated area at 2 km × 2 km grid over ground observation points. It is recommended that high temporal NDVI product with finer spatial resolution satellite would, by offsetting the impact of subpixel heterogeneity, enable improved country-scale crop area monitoring.

Retrieval of regional LAI over agricultural land from an Indian geostationary satellite and its application for crop yield estimation

Abstract 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 the ProSail 1-D (Dimensional) canopy radiative transfer (CRT) model. The ProSail model 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 available in situ measurements 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 20 in situ measured 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−1 with 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.

Ground-based hyperspectral remote sensing to discriminate biotic stress in cotton crop

A large gap exists between the potential yield and the yield realized at the agricultural field. Among the factors contributing towards this yield gap are the biotic stresses that affect the crops growth and development. Severity of infestation of the pests and diseases differs between agroclimatic region, individual crops and seasons within a region. Information about the timing of start of infestation of these diseases and pests with their gradual progress in advance could enable plan necessary pesticide schedule for the season, region on the particular crop against the specific menace expected. This could be enabled by development of region, crop and pest-specific prediction models to forewarn these menaces. In India most (70%) of the land-holding size of farmers average 0.39 ha (some even 20 m x 20 m) and only 1% crop growers hold< 10 ha (mean: 17.3 ha). Patchiness of disease and pest incidence could pose problems in its proper assessment and management. Thus, such exercise could be highly time-consuming and labour-intensive for the seventh largest country with difficult terrain, 66% gross cropped area under food crops, lacking in number of skilled manpower and shrinking resources. Remote sensing overcomes such limitations with ability to access all parts of the country and can often achieve a high spatial, temporal and spectral resolution and thus leading to an accurate estimation of area affected. Due to pest and disease stress plants showed different behavior in terms of physiological and morphological changes lead to symptoms such as wilting, curling of leaf, stunned growth, reduction in leaf area due to severe defoliation or chlorosis or necrosis of photosynthetically active parts (Prabhakar et al., 2011; Booteet al., 1983; Aggarwal et al., 2006). Damage evaluation of diseases has been largely done by visual inspections and quantification but visual quantification of plant pest and diseases with accuracy and precision is a tough task. Utilization of remote sensing techniques are based on the assumption that plant pest and disease stresses interfere with physical structure and function of plant and influence the absorption of light energy and therefore changes the reflectance spectrum of plants. Moreover, remote sensing provides better means to objectively quantify crop stress than visual methods and it can be used repeatedly to collect sample measurements non-destructively and non-invasively (Nutteret et al., 1990; Nilson, 1995). Recent advances in the field of spectroscopy and other remote sensing techniques offer much needed technology of hyperspectral remote sensing (Prabhakar et al., 2011). Hyperspectral remote sensing for disease detection helps in monitoring the diseases in plants with the help of different plant spectral properties at the visible, near infrared and shortwave infrared regions ranging from 350 – 2500 nm, which develops specific signatures for a specific stress for a given plant (Yang et al., 2009). It has been effectively used in assessment of disease in agricultural crops like wheat, rice, tomato etc across the world. Cotton (Gissypium hirsutum L.) is one of the major commercial crops grown in India, and supports about 60 million people in the country directly or indirectly through the process of production, processing, marketing and trade (Prabhakar et al., 2011). India ranks first in global acreage, occupying about 33% of world cotton area. With regard to production it is ranked second next to China. In recent years, farmers are facing many challenges because of rising incidents of white flies, jassid, leafhoppers, aphids, mealybugs and stainers. Whiteflies are tiny, sap- sucking insects that may become abundant in vegetable and ornamental plantings, especially during warm weather. They excrete sticky honeydew and cause yellowing or death of leaves. Outbreaks often occur when the natural biological control is disrupted. Management is difficult once populations are high. White flies develop rapidly in warm weather, and populations can build up quickly in situations where natural enemies are ineffective and when weather and host plants favor outbreaks. Large colonies often develop on the undersides of leaves. The most common pest species such as greenhouse white fly (Trialeurodes vaporariorum) and sweet potato white fly (Bemisia tabaci) have a wide host range that includes many weeds and crops. White flies normally lay their tiny oblong eggs on the undersides of leaves. The eggs hatch, and the young white flies gradually increase in size through four nymphal stages called instars. The first nymphal stage (crawler) is barely visible even with a hand lens. The crawlers move around for several hours before settling to begin feeding. Later nymphal stages are immobile, oval, and flattened, with greatly reduced legs and antennae, like small scale insects. The winged adult emerges from the last nymphal stage (sometimes called a pupa, although whiteflies don’t have a true complete metamorphosis). All stages feed by sucking plant juices from leaves and excreting excess liquid as drops of honeydew as they feed. White flies use their piercing, needle like mouthparts to suck sap from phloem, the food-conducting tissues in plant stems and leaves. Large populations can cause leaves to turn yellow, appear dry, or fall off plants. Like aphids, white flies excrete sugary liquid called honeydew, so leaves may be sticky or covered with black sooty mold that grows on honeydew. The honeydew attracts ants, which interfere with the activities of natural enemies that may control white flies and other pests. High white fly infestation was reported at several locations in Punjab during year 2015. The application of non-destructive methods to detect vegetation stress at an early stage of its development is very important for pest management in commercially important crops. Earlier few studies have been done to characterize reflectance spectra of nutrient stress nitrogen deficiency and irrigation management for cotton but no literature is available regarding characterization of spectral reflectance to study white fly infestation. Therefore, the primary objectives of this study are: (i) to study changes in chlorophyll content and water content due to white fly infestation. (ii) characterization of spectral signature from cotton crop infested by white fly, (iii) establishment of most sensitive wavebands to white fly infestation.

Development of real-time reference evapotranspiration at the regional scale using satellite-based observations

ABSTRACT Real-time data of reference evapotranspiration (ET0) at different space-time scales are essential to regional agricultural drought assessment, water accounting at the watershed to basin scale, and provide irrigation advisory to farmers. Here, we present a data-fusion approach that integrates satellite-based insolation product (8 km) from an Indian geostationary satellite (Kalpana-1) sensor (VHRR; Very High Resolution Radiometer) and high-resolution (~ 5 km) short-range weather forecast into an FAO56 model based on the classical Penman–Monteith (P-M) formulation. Five year (2009–2013) mean monthly estimates from the daily ET0 product over the Indian landmass were found to vary between 10 and 350 mm. It increased from January to May (70–350 mm), followed by a decrease to reach the lowest in November (10–140 mm), thus typically showing unimodal distribution. The comparison of daily space-based and station-based estimates (at six ground stations) produced a root mean square deviation (RMSD) ranging from 21% to 38% for 977 paired data sets with the correlation coefficient (r) varying from 0.32 to 0.82. The error was reduced from 25% to 10% with an increase in ‘r’ from 0.43 to 0.98 for daily to 10 day summation period. Spatial grid-to-grid comparison of monthly ET0 estimates with Global Data Assimilation System (GDAS) potential evapotranspiration (PET) showed RMSD within a range of 1.4–18.4% for most of the months, except for two. Further ET0 analysis over normal and drought years showed that it could be used for comprehensive drought assessment with other existing indicators.