V. K. Dadhwal

Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status

High‐resolution soil moisture holds the key to improving weather forecast, drought monitoring and hydrological modelling. Therefore, the present study investigates the potential of the temperature/vegetation dryness index (TVDI) from the MODIS to assess soil moisture status in sub‐humid parts of India (western Uttar Pradesh). The TVDI was calculated by parameterizing the normalized difference vegetation index–surface temperature space from 8 day MODIS reflectance and surface temperature products. Correlation and regression analysis was carried out to relate the TVDI against in‐situ measured soil moisture during early (April) and peak (October) stages of growth in sugarcane crop. Spatio‐temporal patterns in the TVDI shows that northern areas had more surface wetness compared to southern areas. The results further reveal that a significantly strong and negative relationship exists between the TVDI and in‐situ soil moisture, particularly when vegetation cover is sparse. The dryness index was also found satisfactory to capture the temporal variation in the surface moisture status in terms of antecedent precipitation index.

Growing stock-based forest biomass estimate for India

The total standing biomass (including above ground and below ground) in Indian forests for the year 1992–93 was estimated using information on state and union-territory field inventory based growing stock volume and the corresponding area under three different crown density classes (very dense forests with crown cover 70 percent and above, dense forest with crown cover 40 percent but <70 percent and open forests with crown cover between 10 and 40 percent) grouped under four major forest categories (hardwood, spruce-fir, pine and bamboo) by Forest Survey of India. The growing stock volume was converted to total biomass using biomass expansion factors as function of growing stock volume density. The average growing stock volume density in Indian forests for the study year 1992–93 was but it varied amongst states, with a range of in Punjab to in Jammu and Kashmir. The total standing biomass (above ground and below ground) was estimated as . The aboveground and belowground biomass was estimated as 6865.1 and , contributing 79 and 21 percent to the total biomass, respectively. The mean biomass density in Indian forests was estimated as and amongst the states it varied from in Punjab to in Jammu and Kashmir, respectively. The estimates have been compared with previous studies, which had estimated biomass in the range of 4400– for the corresponding period. Our results are an improvement over previous estimates as these incorporate biomass expansion factors which relate wood volume to biomass as a function of growing stock volume density, four forest types and three crown density classes of Indian forests. These improved biomass estimates are crucial to assess the total C pool of forests and further for use as inputs to models to estimate net C flux to atmosphere from Indian forests due to deforestation and landuse changes.

Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India

This paper reports results on the LAI Retrieval and Validation Experiment (LRVE) that was conducted for two agricultural areas in Central India during the winter season of 2001-2002. The study aimed at relating field measurements of leaf area index (LAI) to spaceborne Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor-III (LISS-III) data, preparation of site-level LAI maps, and validation of Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km LAI global fields. Measurements of field-level LAI, aerosol optical thickness and water vapor were carried out on the day of LISS-III overpasses. Empirical models based on the site-specific LAI-vegetation index relation were developed and used to generate 23-m resolution LAI maps for two sites (Indore and Bhopal) covering 30 kmtimes30 km. These LAI images were degraded to 1-km spatial resolution and used for validation of the version 3 and 4 MODIS LAI products (MOD15A2). The results indicate a positive correlation (r=0.78) between LAI derived from LISS-III data and MODIS data. However an overestimate by a factor of 1.6 to 2.5 in the version 3 MODIS product is observed with root mean square error (RMSE) ranging from 0.20 to 1.26. The factor of overestimation reduces significantly by 50% and RMSE by 40% when version 4 MODIS LAI was analyzed. The improvement in accuracy was observed to be associated with the change in algorithm path adopted for retrieving version 3 and 4 MODIS LAI. Analysis of the MODIS land cover product that is an input in the MODIS LAI retrieval algorithm indicated errors in assigning land cover classes for the study sites, which could be one of the sources of error in MODIS LAI product

Wheat production forecasting for a predominantly unirrigated region in Madhya Pradesh )India)

Abstract The area under wheat was estimated and a forecast of production made in a predominantly un-irrigated region (36 per cent irrigated wheal crop, geographical area 5-61 Mha) of Madhya Pradesh (India) using digital data from LISS-I (Linear Imaging Self Scanner) onboard Indian Remote Sensing Satellite (IRS-IB), for the crop season 1991-92. A stratified sampling approach based on 5 km by 5 km sample segments, 10 per cent sampling fraction in conjunction with supervised maximum likelihood (MXL) classification was used for wheat acreage estimation. Yield forecasts were based on an optimal combination of forecasts from two different methodologies, viz., wheat yield-spectral relationship and time series analysis using ARIMA (Auloregressive Integrated Moving Average) approach. In the former, a two-year (1989-90, 1990-91) pooled regression relating LISS-I derived Near Infrared/Red (NIR/R) radiance ratio to district wheat yields was developed and used to forecast wheat yields for the year 1991-92 based on cla...

Land Use/Land Cover Change Mapping In Mahi Canal Command Area, Gujarat, Using Multi-temporal Satellite Data

The temporal changes (1988-89 to 1997) in land-use/land cover were studied using multi-temporal satellite data in Mahi Right Bank Canal (MRBC) command area in Kheda district of Gujarat state. The canal command area is affected by waterlogging and salinity. The land-use/land cover change is maximum in a distributary (Lambhvel) situated in highly urbanised zone around Anand city, where built-up area increased from 205 ha to 868 ha. In Nadiad command area also there is an increase in urban area from 281 ha to 460 ha, causing a decrease in agricultural area. Waterlogging is significant in Pansora command area with 586 ha of waterlogged area in 1997. Water logging has also increased in commands of other distributaries. The salt affected area has increased in Chikhaliya command whereas it has decreased in Manej command.

Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data

The most commonly used normalized difference vegetation index (NDVI) from remote sensing often fall short in real-time drought monitoring due to a lagged vegetation response to drought. Therefore, research recently emphasized on the use of combination of surface temperature and NDVI which provides vegetation and moisture conditions simultaneously. Since drought stress effects on agriculture are closely linked to actual evapotranspiration, we used a vegetation temperature condition index (VTCI) which is more closely related to crop water status and holds a key place in real-time drought monitoring and assessment. In this study, NDVI and land surface temperature (Ts) from MODIS 8-day composite data during cloud-free period (September–October) were adopted to construct an NDVI–Ts space, from which the VTCI was computed. The crop moisture index (based on estimates of potential evapotranspiration and soil moisture depletion) was calculated to represent soil moisture stress on weekly basis for 20 weather monitoring stations. Correlation and regression analysis were attempted to relate VTCI with crop moisture status and crop performance. VTCI was found to accurately access the degree and spatial extent of drought stress in all years (2000, 2002, and 2004). The temporal variation of VTCI also provides drought pattern changes over space and time. Results showed significant and positive relations between CMI (crop moisture index) and VTCI observed particularly during prominent drought periods which proved VTCI as an ideal index to monitor terminal drought at regional scale. VTCI had significant positive relationship with yield but weakly related to crop anomalies. Duration of terminal drought stress derived from VTCI has a significant negative relationship with yields of major grain and oilseeds crops, particularly, groundnut.

Estimation of leaf total chlorophyll and nitrogen concentrations using hyperspectral satellite imagery

SUMMARY Remotely sensed estimates of biochemical parameters of agricultural crops are central to the precision management of agricultural crops (precision farming). Past research using in situ and airborne spectral reflectance measurements of various vegetation species has proved the usefulness of hyperspectral data for the estimation of various biochemical parameters of vegetation. In order to exploit the vast spectral and radiometric resources offered by space-borne hyperspectral remote sensing for the improved estimation of plant biochemical parameters, the relationships observed between spectral reflectance and various biochemical parameters at in situ and airborne levels needed to be evaluated in order to establish the existence of a reliable and stable relationship between spectral reflectance and plant biochemical parameters at the pixel scale. The potential of the EO-1 Hyperion hyperspectral sensor was investigated for the estimation of total chlorophyll and nitrogen concentrations of cotton crops in India by developing regression models between hyperspectral reflectance and laboratory measurements of leaf total chlorophyll and nitrogen concentrations. A comprehensive and rigorous analysis was carried out to identify the spectral bands and spectral indices for accurate retrieval of leaf total chlorophyll and nitrogen concentrations of cotton crop. The performance of these critical spectral reflectance indices was validated using independent samples. A new vegetation index, named the plant biochemical index (PBI), is proposed for improved estimation of the plant biochemicals from space-borne hyperspectral data ; it is simply the ratio of reflectance at 810 and 560 nm. Further, the applicability of PBI to a different crop and at a different geographical location was also assessed. The present results suggest the use of space-borne hyperspectral data for accurate retrieval of leaf total chlorophyll and nitrogen concentrations and the proposed PBI has the potential to retrieve leaf total chlorophyll and nitrogen concentrations of various crops and at different geographical locations.

Spatial and temporal patterns of surface soil moisture over India estimated using surface wetness index from SSM/I microwave radiometer

Results from an approach to infer surface soil moisture from time series analysis of surface wetness index derived using the Special Sensor Microwave/Imager (SSM/I) are presented. Soil moisture quantification was based on the study of temporal changes in surface wetness index and its scaling to maximum and air‐dry limits of soil in each grid cell (0.33°). The estimated soil moisture of Illinois, USA was compared with field measured soil moisture (0–10 cm) obtained from the Global Soil Moisture Data Bank. A root mean square error of 7.18% was found between estimated and measured volumetric soil moisture. A consistency in soil moisture and rainfall pattern was found in the un‐irrigated areas of northern India (Jodhpur, Varanasi) and southern India (Madurai), influenced by southwest and northeast monsoons, respectively. Soil moisture of more than 0.30 m3m−3 was observed in the absence of rainfall due to the irrigation of rice crop in (Punjab) during the pre‐southwest monsoon period (May).

Wheat acreage estimation for Haryana using satellite digital data

This paper summarizes the procedures adopted and results obtained since 1985–86 for wheat inventory for Haryana using satellite digital data (MSS: 1985–86 to 1987–88, LISS-I: 1988–89 onwards). The approach followed is based on sample segments (10 × 10 km during 1985–86 to 1988–89, 7.5 × 7.5 km during 1989–90) and 10 percent sampling fraction and stratified sample design. There has been consistent improvement in accuracy over the years as judged from lower biases when compared with Bureau of Economics and Statistics (BES) acreage estimates and higher precision. In 1989–90, the state-level estimate achieved an accuracy goal of 90 percent at 90 percent confidence interval. A number of studies which have been carried out to study effect of choice of sensor, acquisition date, stratification approach, classification procedure on wheat inventory are also mentioned.

Performance evaluation of an irrigation command area using remote sensing: a case study of Mahi command, Gujarat, India

Multi-temporal remote sensing (RS) data-based crop inventory, generation of vegetation spectral index profiles and crop evapotranspiration estimation were carried out over the Mahi Right Bank Canal (MHRC) command (212,000 ha) in Gujarat, India, using Indian Remote Sensing Satellite (IRS)-1C Linear Imaging and Self Scanning-III (LISS-III) and Wide Field Sensor (WiFS) data. Distributary-wise, three RS-based performance indices, namely, adequacy (AI), equity (EI) and water use efficiency(WUE) were computed. AI was computed by comparing the crop water requirement with the water release data. EI was evaluated by observing the head-to-tail difference in two distributaries. It was found that water availabilty was in excess along main canals and branch canals. In cropped area, it was less and crop condition was poor towards the tail ends of the command area. WUE was computed as the ratio between the area under the vegetation index profile and the water applied. The three RS-based indices could rank the performance of the distributaries and also identify those having problems in water allocation and utilization.