Indrani Choudhury - Academia.edu (original) (raw)

Papers by Indrani Choudhury

The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014

This study focuses on the understanding and mapping of coupling hotspots of LE versus terrestrial... more This study focuses on the understanding and mapping of coupling hotspots of LE versus terrestrial and meteorological parameters. Single source surface energy balance model was used to derive surface energy balance parameters. Agro climatic region wise monthly information of terrestrial, energy balance and meteorological parameters were derived during June-September from decadal analysis of MODIS data (2003-2012) over India (68-100°E, 5-40°N) at 5 km spatial resolution. Information on rainfall was obtained from gridded rainfall data (1°× 1° spatial resolution) from Indian Meteorological Department (IMD). The spatiotemporal variability of the parameters such as rainfall, evapotranspiration (ET), evaporative fraction (EF), soil water index (SWI), land surface temperature (LST) and air temperature (T a) showed strong influence on seasonal LE fluctuation. LE showed positive linear coupling with ET (0.52 <R 2 ≤ 0.91), EF (0.79 ≤ R 2 ≤0.96), SWI (0.80 ≤ R 2 ≤0.93) and negative exponential coupling with LST (0.63 ≤ R 2 ≤0.87), T a (0.55 < R 2 ≤0.83). The pixel based knowledge of the parameters was incorporated into hierarchical decision rule algorithm and pixel-by-pixel segmentation of monthly coupling of LE versus parameters (ET, EF, SWI, LST, T a) was generated. The rainfall zonations in a spatiotemporal domain were done based on the LE couplings that clearly demarcated the highest (West Coast Plains and Hills Region, Himalayan region), moderate (Gangetic Plains and Hills Regions, and the Plateau and Hills Regions) and lowest rainfall (Western dry region) areas. The transition of zone-wise availability of rainfall (both surplus and deficient) can be very well understood from the seasonal dynamics of the LE couplings.

This paper assesses the synergy of RADARSAT and ENVISAT data for rice monitoring. Crop growth pro... more This paper assesses the synergy of RADARSAT and ENVISAT data for rice monitoring. Crop growth profile derived from the analysis of temporal backscatter of RADARSAT SCNB (July-August) and ENVISAT of IS4 and IS5 (September-November) enables to classify early, normal and late sown crop with 10-12 dB difference throughout the growth cycle. An inversion algorithm relating backscatter and plant height was used to retrieve transplantation date whereas the peak vegetative stage was retrieved from peak backscatter value of the temporal profile. Good correlation was observed between backscatter and crop growth parameters obtained from field measurements. Linear relation between polarization ratio (HV/HH) and fresh biomass indicated that even though ENVISAT data were acquired during vegetative stage, rice biomass could be retrieved with less uncertainty. Rice map was generated using decision rule algorithm with 94.8 % accuracy. The results appear promising and increase the possibility of acqui...

International Journal of Remote Sensing, 2021

ABSTRACT Assessment of consumptive water use (CWU) and water productivity at the regional scale i... more ABSTRACT Assessment of consumptive water use (CWU) and water productivity at the regional scale is important to diagnose vulnerable zones to improve water use efficiencies in cropland to achieve the sustainable development goal 6.0 prescribed by the United Nations. The present study was carried out to segregate and quantify CWU into agricultural green (CWUg) and blue (CWUb) water use and water productivity (AWP) on seasonal, annual, and decadal (2009–2018) scales over the Indian region using satellite remote-sensing data from geostationary and polar-orbiting platforms. A logical algorithm was used to determine partitioned water use from a combination of satellite-based estimates of key variables such as agricultural water demand (AWD), actual evapotranspiration (ETa), and effective rainfall (ER). Satellite-based estimates of CWU were evaluated with respect to ground reference that showed underestimation of the order of 15–32% but with a strong Pearson’s correlation coefficient (r = 0.80–0.99) and coefficient of determination (R 2 = 0.64–0.98). The reasons for this difference and uncertainties in satellite-based inputs have been explained. The decadal mean of CWU at annual scale showed wide spatial variation over India with 84% and 16% share of CWUg and CWUb, respectively, in kharif season and 27% and 73%, respectively, in rabi season. A non-significant increasing trend in CWUg,kharif (0.57%) and a decreasing trend in CWUb,kharif (–5.18%), CWUg,rabi (–2.81%), and CWUb,rabi (–1.77%) were observed over 10 years. A decreasing trend in green AWP (AWPg) (–0.18%) in kharif season reveals a lack of sustainable adoption of green water management practices while a significantly increasing trend in AWPb (2.65%) in rabi season reveals sustainable adoption of efficient irrigation management practices over 10 years. These long-term estimates would help in smoothing out trade-offs of water use versus water productivity, reducing the vulnerability and aiding in decision-making for water savings, controlled water allocation, strategic policy formulations for water, and food security especially through sustainable management practices in rainfed agriculture.

Geocarto International, 2021

Agricultural water demand (AWD) and irrigation water demand (IWD) were assessed (2009-2018) over ... more Agricultural water demand (AWD) and irrigation water demand (IWD) were assessed (2009-2018) over India using geostationary and polar orbiting satellites. A novel concept of satellite based composite crop-coefficient was introduced to address bulk AWD from mixed agricultural landscape. Significant spatio-temporal variation of AWD was observed over India. The decadal mean of annual AWD was found to be 1521 km 3 contributing around 52% (789 km 3) and 48% (732 km 3) in kharif and rabi seasons, respectively. The decadal average IWD over India was found to be 360 km 3. At annual scale, around 75% of AWD was found fulfilled by effective rainfall and the rest 25% is the IWD. The decadal trend of AWD and IWD showed significant increasing trend over Indian region. The study provides a baseline reference for regional agricultural water management policy over diverse agro-climatic regions of India with an opportunity to optimize AWD and IWD at different locations.

International Journal of Remote Sensing, 2018

The preliminary analysis of agricultural water productivity (AWP) over India using satellite data... more The preliminary analysis of agricultural water productivity (AWP) over India using satellite data were investigated through productivity mapping, water use (actual evapotranspiration (ET a)/effective rainfall (R eff) mapping and water productivity mapping. Moderate Resolution Imaging Spectroradiometer data was used for generating agricultural land cover (MCD12Q1 at 500 m), gross primary productivity (GPP; MOD17A2 at 1 km), and ET a (MOD16A2 at 1 km). R eff was estimated at 10 km using the United States Department of Agriculture soil conservation service method from daily National Oceanic and Atmospheric Administration Climate Prediction Center rainfall data. Six years' (2007-2012) data were analysed from June to October. The seasonal AWP and rainwater productivity (RWP) were estimated using the ratios of seasonal GPP (kg C m −2) and water use (mm) maps. The average AWP and RWP ranges from 1.10-1.30 kg Cm −3 and 0.94-1.0 kg C m −3 , respectively, with no significant annual variability but a wide spatial variability over India. The highest AWP was observed in northern India (1.22-1.80 kg C m −3) and lowest in western India (0.81-1.0 kg C m −3). Large variations in AWP (0.69-1.80 kg C m −3) were observed in Himachal Pradesh, Jammu and Kashmir, northeastern states (except Assam), Kerala, and Uttaranchal. The low GPP of these areas (0.0013-0.13 kg C m −2) with low seasonal total ET a (<101 mm) and R eff (<72 mm) making the AWP high that do not correspond to high productivity but possible water stress. Gujarat, Rajasthan, Maharashtra, Madhya Pradesh, Jharkhand, and Karnataka showed low AWP (0.73-1.13 kg C m −3) despite having high ET a (261-558 mm) and high R eff (287-469 mm), indicating significant scope for improving productivity. The highest RWP was observed in northern parts and Indo-Gangetic plains (0.80-1.6 kg C m −3). The 6 years' analysis reveals the status of AWP, leading to appropriate interventions to better manage land and water resources, which have great importance in global food security analysis.

International Journal of Remote Sensing, 2015

ABSTRACT Understanding changes in monsoon variability over a decade requires thorough knowledge o... more ABSTRACT Understanding changes in monsoon variability over a decade requires thorough knowledge of the seasonal and inter-annual variability in surface energy flux and its forcing parameters (land surface and meteorology) in response to climate change. In the present study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua climate model gridded global products (0.05° × 0.05° spatial resolution) of land surface temperature (LST; Ts), normalized difference vegetation index (NDVI), and surface albedo (α) were used to generate seasonal (June-September) and inter-annual (2003-2012) variation in surface energy flux and its forcing parameters over different agro-climatic regions (ACRs) of India. Energy fluxes were retrieved using a single-source surface energy balance model (here vegetation and soil is considered as a single unit). Energy flux observations over different ACRs allowed comparison of the seasonal transition of latent heat flux (LE), net radiation (Rn), soil heat flux (G), available energy (Q = Rn - G), and evaporative fraction (EF) as terrestrial links to the atmosphere. The seasonal and inter-annual variation in EF was investigated by plotting against the soil moisture information retrieved from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) global monthly data product (1° × 1° spatial resolution). Decadal and seasonal analysis showed that energy fluxes vary widely in time and space due to variability in surface radiation parameters (Ts, α), vegetation cover, soil moisture, and air temperature (Ta), which influence the seasonal transition of monsoon through LE and EF. Among the ACRs, LE and EF were found lowest in the Western Dry Region (WDR) and highest in the Western Himalayan Region (WHR). The spatiotemporal depiction of MODIS LE and MODIS EF over a span of 10 years can identify the hotspots and monsoon intensity over different ACRs. Climatic parameters that are susceptible to changes resulting from climate change are thoroughly studied in the present analysis.

Characterization of precipitation feedback system based on the spatiotemporal coupling of monsoon... more Characterization of precipitation feedback system based on the spatiotemporal coupling of monsoon rainfall with land surface, meteorological and surface energy fluxes is important for understanding hydrological, climatological and agricultural aspects at regional and global scales. The global data products of MODIS and AIRS were archived during monsoon season (June-September) from 2009- 2011 for obtaining land surface, meteorological and energy balance (EB) parameters, which were coupled with IMD gridded rainfall data. The representative sites were selected over Indian region to incorporate the spatiotemporal variability among different parameters. Surface energy balance model was used to retrieve EB components to estimate latent heat flux (LE) and evaporative fraction (EF), an indicator of precipitation estimate. Three precipitation feedback systems were designed viz. landsurface-precipitation (LPFS), meteorological-precipitation (MPFS) and EB-precipitation (EBPFS) through statisti...

Journal of the Indian Society of Remote Sensing, 2004

Abstract This study investigates the potential of multi-temporal signature analysis of satellite ... more Abstract This study investigates the potential of multi-temporal signature analysis of satellite imagery to map rice area in South 24 Paraganas district of West Bengal. Two optical data (IRS ID LISS III) and three RADARSAT SAR data of different dates were acquired during 2001. Multi-temporal SAR backscatter signatures of different landcovers were incorporated into knowledge based decision rules and kharif landcover map was generated. Based on the spectral variation in signature, the optical data acquired during rabi (January) and ...

International Journal of Remote Sensing, 2014

ABSTRACT This research paper focuses on the spatio-temporal coupling of monsoon rainfall with lan... more ABSTRACT This research paper focuses on the spatio-temporal coupling of monsoon rainfall with land-surface and energy balance parameters, which are important for understanding hydrological, climatological, and agricultural aspects at local, regional, and global scales. The dynamics of land-surface and energy balance parameters influence summer monsoon over India. Time scales of the land-surface response to monsoon forcing are different for different land-surface conditions due to different physical processes governing the landsurface– atmosphere exchange through energy balance components. A synergy of satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) (0.05° × 0.05°) for obtaining land-surface and energy balance parameters, and the Atmospheric Infrared Sounder (AIRS) (1° × 1°) for obtaining atmospheric parameter and gridded rainfall data (1° × 1°) from the IndianMeteorological Department (IMD) during June to September for three consecutive years (2009–2011) representing low to normal rainfall, were used to develop a coupling model in the spatio-temporal domain. Surface energy fluxes were estimated using a surface energy balance model by partitioning available energy at the surface into latent heat flux (LE) and sensible heat flux (H) through the evaporative fraction (EF) concept of a 2D land-surface temperature (LST)-albedo scatter plot. The coupling models were based on statistical methods developed at both temporal and spatial scales to explain the linking of various parameters with monsoon rainfall. A significant positive relationship was obtained between rainfall and land-surface parameters such as normalized difference vegetation indices (NDVIs), and soil wetness/energy balance parameters such as LE and EF, whereas a strong negative relationship was obtained between rainfall and surface radiation parameters (LSTand albedo)/energy balance parameters such as soil heat flux (G) and net radiation (Rn). This approach has demonstrated its simplicity with remote sensing technology and could identify ‘at risk’ regions at spatio-temporal scales based on coupling models.

International Journal of Remote Sensing, 2011

This study presents a methodology to classify rice cultural types based on water regimes using mu... more This study presents a methodology to classify rice cultural types based on water regimes using multi-temporal synthetic aperture radar (SAR) data. The methodology was developed based on the theoretical understanding of radar scattering mechanisms with rice crop canopy, considering crop phenology and variation in water depth in the rice field, emphasizing the sensitivity of SAR to crop geometry and water. The logic used was the characteristic decrease in SAR backscatter that is associated with the puddled or ...

Journal of the Indian …, Jan 1, 2006

lnIbrmation on various agricultural resource parameters at various levels is essential for proper... more lnIbrmation on various agricultural resource parameters at various levels is essential for proper management and efficient resource allocation for sustainable agricultural development. Limitations in ground-based method have encouraged the use of satellite data coupled with geographical information system (GIS) in providing spatial as well as temporal information over large and inaccessible areas. In the present study, an attempt has been made to generate raster maps using remote sensing and GIS techniques to characterize the agroecosystem of South 24 Paraganas district of West Bengal, based on land utilization indices. Information on multi-season landcover derived from the analysis of the multi-temporal RADARSAT-1 SAR and 1RS-ID LISS III data as well as other ancillary information in GIS environment are the basic inputs used in the study. The present analysis shows that northern and northwestern parts are more diverse in terms of agricultural intensification as compared to the southern and northeastern pans whereas the central parts show moderate density. In terms of carrying capacity, the high carrying capacity has been observed in the southern to northeastern parts whereas the northwestern and central parts show moderate and northern parts show low carrying capacity. Overall, the characterization of agroecosystem using land utilization indices can be identified as major input to formulate a management plan for sustainable agriculture with concerns for the environment.