Markand P. Oza

Monitoring Cropping Pattern Changes Using Multi-temporal WiFS Data

Abstract Cropping pattern is a basic element of cropping system. For the better management of cropping systems, up‐to‐date information on present cropping pattern and changes in cropping pattern is essential. Remote sensing data has shown its great potential in agricultural applications. Wide Field Sensor (WiFS) data available from IRS‐1C, ‐1D and ‐P3 satellites provide an excellent opportunity for spatio‐temporal monitoring of crops on a regional scale because of its large swath and high revisit capability. The present study demonstrates the use of multi‐year multi‐date WiFS data for monitoring changes in cropping pattern in Kota‐Baran districts in Rajatsthan (India) from 1997-98 to 1999-2000. Depending upon the availability of satellite data two approaches have been developed. Change in cropping pattern from mustard to wheat (of the order of 45,140 ha) from 1997-1998 season to 1998-1999 season was observed; while in 1999-2000 season change from wheat to mustard acreage (of the order of 69,867 ha) has been detected and mapped. The cropping pattern changes (from 1998-99 to 1999-2000 season) mapped using only two date WiFS data (for each season) are 11% higher than those mapped using complete data set spanning from early November to March.

Multiple production forecasts of wheat in India using remote sensing and weather data

This paper describes the methodology adopted and results obtained during forecasting of national level wheat production in India using multi-date medium resolution Advanced Wide Field Sensor (AWiFS) data. Multidate, geometrically registered and radiometrically normalized Resourcesat-1 AWiFS data were classified using hierarchical decision rules, which exploited differential crop spectral profiles of various crops in winter season. Wheat acreage estimates were arrived by aggregation of stratified samples. Wheat yields were predicted for meteorological sub-divisions by correlation weighted weather regression models developed using fortnightly temperatures. Multiple preharvest wheat acreage and production forecasts were made with additional information on spatio-temporal crop growth performance in comparison to previous / normal season.

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.

Spectral wheat growth profile in Punjab using IRS WiFS data

A functional form of crop spectral profile suggested by Badhwar was applied to district-wise wheat Normalised Difference Vegetation Index (NDVI) values relatively normalised by Pseudo-Invariant Feature (urban and built-up) NDVI values, derived from Wide Field Sensor (WiFS) onboard Indian Remote Sensing Satellites (IRS) for 17 dates during 1999–2000 rabi season. The goodness of overall profile fitting and the three basic parameters i.e., crop emergence date (To), and crop specific parameters (a and P) was found to be statistically significant. While a corresponds to profile progressive growth rate, β corresponds to profile decay rate. A comparison with earlier studies in Punjab using NOAA-AVHRR indicated improvement in relation between peak NDVI and wheat yield. The estimated time of spectral emergence and profile-derived peak NDVI follow the observed behaviour of shortened crop pre-anthesis period with delayed sowing.

Selection of band combination for IRS data

Selection of band combination for generating a colour composite image using IRS data is discussed from statistical considerations. Most suitable three band combination turns out to be bands 1, 3 and 4. It is also shown that intrinsic dimensionality of IRS data is approximately two.

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.

Quantification of improvements by using AWiFS over WiFS data

The Advanced WiFS sensor of RESOURCESAT- 1 satellite offers significantly improved specifications compared to the WiFS sensor onboard IRS IC, P3 and ID satellites. The improvements are in terms of spatial resolution, radiometry (quantisation levels) and number of spectral bands. In the present study, an attempt has been made to quantify the gains due to these enhanced specifications. The study has been carried out in a predominantly agricultural area. For the study reported here, one set of overlapping data acquired on the same day by WiFS and AWiFS sensors has been selected. This eliminates the need of atmospheric correction/ normalization for comparison. The effect of spatial resolution has been studied by applying ISODATA spectral clustering algorithm with number of clusters set at three different levels, viz., 10, 20 and 30. They are assumed to mimic first, second and third level classification, respectively. Output images were filtered using 3 × 3 majority filter. Homogeneous polygons having area less than 1/2 and 1 pixel of WiFS were recorded. This indicates the minimum loss by using WiFS data. A relative gain of 10 – 15 % is observed due to improvement in spatial resolution. For comparison of radiometry, local variance measure was used. It was observed that local variance is much larger for AWiFS data in comparison with WiFS data. This indicates presence of enhanced local contrast, hence heterogeneity, in AWiFS data over WiFS data. Separability analysis has been carried out to demonstrate improvements due to two additional spectral bands (Green and SWIR).

Preparation of volume table of SAL (Shorea robusta) - an approach using satellite data

Abstract Sal (Shorea robusta) is an important forest tree species in north and north-eastern India. Large-scale plantations of this species have been raised there under taungya and coppice system of management. The conventional volume table prepared for high sal forest is referred to infer the volume of production of this species. Earlier workers have used aerial remote sensing data to develop volume tables of this species. In the present study a volume table for sal is developed based on remotely sensed satellite data using a regression technique. A two-step method was developed to estimate mean tree volume from satellite data. In step 1, mean crown diameter — an intermediate variable - was estimated from satellite data. In step 2, the estimated mean crown diameter was used to estimate the mean tree volume. Addition of age of the crop as an independent variable improved the predictive ability of the regression equation.

Choosing optimal spatial resolution —Study of two agriculture dominated areas

The optimal selection of spatial resolution for a remote sensing based study is very important. Two methods, one based on local variance and the other based on semi-variance at lag 1, available for arriving at a solution of this problem are tried out in this study. The areas covered are agriculture-dominated regions of North Gujarat and Western Uttar Pradesh. SPOT-1 HRV multispectral data were analysed. It was found that the optimal spatial resolution is about 60–80 m.