Bimal K. Bhattacharya

Evapotranspiration using MODIS data and limited ground observations over selected agroecosystems in India

Plant growth processes and productivity of agroecosystems depend highly on evapotranspiration from the land (soil‐crop cover complex) surface. A study was carried out using MODIS TERRA optical and thermal band data and ground observations to estimate evaporative fraction and daily actual evapotranspiration (AET) over agroecosystems in India. Five study regions, each covering a 10 km×10 km area falling in agricultural land use, were selected for ground observations at a time closest to TERRA overpasses. The data on radiation and crop parameters in paddy (irrigated and rainfed), cotton (rainfed), groundnut (residual moisture) crops were recorded at 14‐day intervals between August 2003 to January 2004 from 2 km×2 km homogeneous crop patches within each study region. Eight MODIS scenes in seven optical (1, 2, 3, 4, 5, 6, 7) and two thermal bands (31, 32) level 1B data acquired from the National Remote Sensing Agency, Hyderabad, India and resampled at 1 km, were used to generate surface albedo (α), land surface temperature (T s, MODIS) and emissivity (εs). Evaporative fraction and daily AET were generated using a single source energy balance approach with (i) ground based observations only (‘stand alone’ approach), and (ii) ‘fusion’ of MODIS derived land surface variables on cloud free dates and coincident ground observations. Land cover classes were assigned using a hierarchical decision rule applied to multi‐date Normalized Difference Vegetation Index (NDVI). The exponential model could be fitted between 1‐EFins, ground (ground based evaporative fraction) and difference between T s, MODIS and air temperature (T a) with R 2 = 0.77. Linear fit (R 2 = 0.74) could be obtained between 1‐EFins, ground and temperature vegetation dryness index (TVDI), derived from T s, MODIS‐NDVI triangle. Energy balance daily AET from the ‘fusion’ approach was found to deviate from water balance AET by between 4.3% to 24.5% across five study sites with a mean deviation of 11.6%. The root mean square error (RMSE) from the energy balance AET was found to be 8% of the mean water balance AET. The satellite based energy balance approach can be used to generate spatial AET, but needs more refinements before operational use in the light of progress in algorithms and their validation with huge datasets.

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.

Retrieval of land surface albedo and temperature using data from the Indian geostationary satellite: a case study for the winter months

The shortwave and longwave radiation budget at land surfaces is largely dependent on two fundamental quantities, the albedo and the land surface temperature (LST). A time series (November 2005 to March 2006) of daily data from the Indian geostationary satellite Kalpana‐1 Very High Resolution Radiometer (K1VHRR) sensor in the visible (VIS), water vapour (WV) and thermal infrared (TIR) bands from noontime (0900 GMT) observations were processed to retrieve these quantities in clear skies for five winter months. Cloud detection was carried out using bispectral threshold tests (in both VIS and TIR bands) in a dekadal time series. Surface albedo was retrieved using a simple atmospheric transmission model. K1VHRR albedo was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) AQUA noontime albedo over different land targets (agriculture, forest, desert, scrub and snow) that showed minimum differences over agriculture and forest. The comparison of spatial albedo over different landscapes yielded a root mean square deviation (RMSD) of 0.021 in VHRR albedo (9% of MODIS albedo). A mono‐window algorithm was implemented with a single TIR band to retrieve the LST. Its accuracy was also verified over different land targets by comparison with aggregated MODIS AQUA LST. The maximum RMSD was obtained over agriculture. Spatial comparison of VHRR and AQUA LSTs over homogeneous and heterogeneous landscape cutouts revealed an overall RMSD of 2.3 K. An improvement in the retrieval accuracy is expected to be achieved with atmospheric products from the sounder and split thermal bands in the imager of future INSAT 3D missions.

Field‐scale Leaf Area Index estimation using IRS‐1D LISS‐III data

A study was carried out to estimate field‐scale Leaf Area Index (LAI) using fine resolution polar orbiting IRS‐1D LISS‐III sensor data at 23 m spatial resolution. Three cloud‐free scenes on 8 January, 2002 (D1), 30 January, 2002 (D2) and 15 January, 2003 (D3) over two sites in Gujarat, India, were acquired. The sub‐scenes encompassing the study area were extracted and geo‐registered. Surface reflectances in the red (0.62–0.68 µm) and near‐infrared (NIR) (0.77–0.86 µm) bands were generated using 6S atmospheric correction code and coincident ground measurements on aerosol and water vapour. Normalized difference vegetation index (NDVI), simple ratio (SR) and soil‐adjusted vegetation index (SAVI) were computed from the reflectances in the red and NIR. A total of 70 mean measured LAI datasets on wheat and tobacco were used for regression analysis and empirical models were developed between LAI and three vegetation indices (VI). Both exponential and power models gave R 2 between 0.53 and 0.61 except for D2 (R 2 between 0.04 and 0.11) when wheat was mostly at the post‐anthesis stage and the VI–LAI relation seems to be influenced by canopy geometry and angular distribution of leaves. The analysis indicated that with the soil type of the study sites being different, the SAVI‐based model had a smaller rms. error (R 2 = 0.496 and rms. error = 0.685) in estimation of LAI when compared with the SR‐ (R 2 = 0.478, rms. error = 0.698) and NDVI‐ (R 2 = 0.491, rms. error = 0.689) based models. The LAIs for the study region were estimated by inversion of empirical models and validated against ground‐measured data.

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.

Assessing solar energy potential using diurnal remote-sensing observations from Kalpana-1 VHRR and validation over the Indian landmass

A spectrally integrated clear-sky and three-layer cloudy-sky models were developed to determine atmospheric transmittances and instantaneous surface insolation. Half-hourly observations at 8 km spatial resolution in optical and thermal infrared bands from an Indian geostationary satellite (Kalpana-1) Very High Resolution Radiometer (VHRR) sensor were used to provide inputs to these models in addition to global 8 day aerosol optical depth and columnar ozone. Sensitivity analysis of the clear-sky model showed a higher influence of aerosol on global insolation, diffuse insolation, and its fraction as compared with water vapour and ozone. The root mean square error (RMSE) of insolation estimates of the daily integral was found to be 2.05 MJ m−2 (∼11.2% of measured mean) with a high correlation coefficient (r = 0.93) when compared with in situ measurements during 1 August 2008 to 31 March 2010 over six locations in India. The errors were found to reduce to 7.5% over 3 to 5 day averages. The comparison of annual estimates and equivalent reanalysis fields showed a mean difference of the order of ±1.7 MJ m−2 over the majority of the Indian landmass.

Modelling regional level spatial distribution of aphid (Lipaphis erysimi) growth in Indian mustard using satellite-based remote sensing data

Abstract We developed a procedure for preparing a model for mapping spatially distributed zones of aphid pest (Lipaphis erysimi) outbreaks at a regional level. This study employed near-surface meteorological parameters derived from National Oceanic and Atmospheric Administration (NOAA) Television and Infra-Red Operational Satellites (TIROS) Operational Vertical Sounder (TOVS) data and field observations of disease infestation. The study area comprised three sites representing semi-arid and sub-humid regions of dominant Indian mustard (Brassica juncea L.)-growing regions of India. A model based on TOVS-derived cumulative surface air temperature and minimum specific humidity (SpH) was developed to estimate the date of ‘aphid onset’ (first appearance), date of peak infestation and location of severity with respect to aphid population density. Aphid population growth rate during the linear growth phase between aphid onset to peak was computed using SpH-weighted temperature and dates of sowing of the crop (crop age). Sowing dates of mustard crop, of northwest India, were obtained from spectral growth profiles derived from time series remote sensing (RS) products of the SPOT-4 VEGETATION sensor. Estimated dates of peak aphid infestation and peak population showed a strong match with the observed data. The location of peak aphid population density was depicted in each spatial grid of 25×25 km2 for parts of northwest India. The simulated aphid population build-up and date of peak population density was validated with observed data for an unknown site in the Sriganganager district, Rajasthan state, India. Comparison of predicted dates of attaining peak aphid population with observations showed a deviation of ±7 days. After validation, the regional level model was applied over a large area of a mustard-growing region for varying dates of sowing, surface air temperature and specific humidity, to show the spatial distribution of aphid growing severity zones (population density) and to predict dates of severe aphid infestation (peak population) at each grid level in the region.

Disaggregation of LST over India: comparative analysis of different vegetation indices

ABSTRACT The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.

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.

Estimation of relative evapotranspiration from NOAA PAL to derive growth characteristics in India

Clear‐sky dekadal relative evapotranspiration (RET) was derived using the surface energy‐balance approach applied to 10‐day composite NOAA PAL (8 km×8 km) datasets over the Indian landmass. This was further used to differentiate between growth characteristics for an irrigated intensive agriculture over a northern India state (e.g. Punjab) and a rainfed ill‐posed agriculture over a central India state (e.g. Madhya Pradesh) using time‐series data sets for five growing years (June–April): 1996–1997, 1997–1998, 1998–1999, 1999–2000, and 2000–2001. The triangular scatter between RET and normalized difference vegetation index (NDVI) showed that the minimum RET increases linearly with NDVI producing a ‘basal line’ that represents relative canopy transpiration only. A clear distinction in scatter was found between the two contrasting agro‐ecosystems showing a higher RET or root zone wetness in irrigated than rainfed systems. In rainfed rice‐growing regions, an inverse correlation (0.6–0.75) was found between RET and the Keetch–Byram meteorological drought index (KBDI), and a substantial reduction in RET was also found in a sub‐normal (2000) compared with a normal (1999) monsoon season. RET estimates were found to be most sensitive to atmospheric transmissivity followed by other land‐surface radiation budget inputs, such as NDVI, LST, and albedo. Error propagation due to three surface parameters is the opposite of that for transmissivity. The maximum possible error in clear‐sky NOAA PAL RET was estimated to be 12–15%. This test study would be helpful in deriving RET using optical and thermal data from a suite of current and future Indian geostationary satellite sensors for monitoring growing conditions.