Objectives Where should we disseminate short duration chickpea varieties and where could we possibly expand chickpea cultivation? An accurate and up-to-date chickpea area map is essential for supporting dryland agriculture. To answer these questions as part of the CRP-grain legumes, the ICRISAT RS-GIS lab has developed a methodology to (i) accurately map major cropland areas in Andhra Pradesh, (ii) classify these areas as either rainfed or irrigated, and (iii) identify the cropping patterns.

Datasets • Satellite data: MODIS 16-day composites (MOD13Q1 product) were used to calculate two indices—the Normalized Difference Vegetation Indices

(NDVI) and the NDVI Monthly Maximum Value Composites (NDVI-MVC)—using surface reflectance values from the red (620–670 nm) and NIR1 (841–875 nm) bands. The NDVI-MVC was used for classification and the NDVI 16-day dataset was used for identifying and labeling seasonal rice classes.

• Extensive ground survey data: Ground-survey information was collected between 12 to 22 January 2012 (during the rabi season) for 449 sample sites covering the major crop-growing areas (which includes groundwater, irrigated surface water, and rainfed areas) across Andhra Pradesh. In addition, field crop observations were made extensively while driving, by capturing other locations for additional information at 365 locations for accuracy assessment.

• Agriculture census data. • Secondary data (e.g., Google Earth, rainfall data, etc.).

MODIS 16-day 250-m composites of surface reflectance product

(MOD13Q1)

Unsupervised classification (NDVI-MVC)

Class spectra

Ideal spectra using Ground survey data

Grouping of similar classes by decision tree algorithms and

spectral matching techniques (SMTs)

Class identification and labeling process

Class spectra (NDVI-MVC)

Is class identified? Chickpea crop extent

No Mixed class

Accuracy assessment

Mask mixed over NDVI-MVC

NDVI-MVC and NDVI 16-day composites

Class spectra (NDVI 8-day)

Ground survey data

Google Earth data

Yes

Area calculations and comparisons

Methods A comprehensive methodology for mapping seasonal rice areas using MODIS 16-day 250m data was developed (Fig. 1).

Fig. 1. Methodology flowchart.

Land use / land cover

Groundnut / sorghum / fallows

Maize / mixed crops

Cotton-chilli

Orchards / mixed crops

Rice-mixed crops

Other LULC

Chickpea

Year 2012-13

We observed and mapped the distinct NDVI phenological signatures in Andhra Pradesh for Irrigated rice-rice, irrigated rice-fallow, Rainfed fallow-chickpea (rabi chickpea) and other major crops. We developed a 6-major crop dominance classes (Fig. 2) for 2012-13. The chickpea areas were compared against the official chickpea areas provided by the Andhra Pradesh Bureau of Statistics for each of the districts in the state. There was a very strong correspondence between the MODIS-derived area and the reported area—R2 values of 84%. A quantitative accuracy assessment across the 6 major crop classes estimated an overall accuracy of 86%, but this varied from 54% to 100% across classes. Almost all of the intermixing or misclassification was between various rainfed classes. This is the most recent and accurate map of the crop dominance in Andhra Pradesh. Mapping major cropland areas is the first step in characterizing important chickpea-growing environments for sustainable grain-legumes development and livelihoods. Detailed and up-to-date maps are important inputs for assessing the impact of droughts, heat stress and yield predictions which regularly affect the region.

Fig. 2. Spatial distribution of major crops cultivation for 2012-13.

RS-GIS unit, RDS

Crop identification and mapping using moderate resolution imagery is usually not possible, unless the temporal resolution is high

or spectral resolution is very high. The growing season of a short duration chickpea typically fits in a temporal window where

NDVI signatures are used to identify the crop easily. Similarly crops can be identified using different methods depending on their

growing period, growth habits and biomass.