This document outlines a methodology developed by ICRISAT's RS-GIS lab to map major cropland areas and cropping patterns in Andhra Pradesh, India using satellite imagery. The methodology uses MODIS 16-day composite data to calculate NDVI and NDVI-MVC indices, along with ground survey data, to classify areas as rainfed or irrigated and identify dominant crops. The methodology was able to accurately map the spatial distribution of major crops in 2012-13, including rainfed fallow-chickpea areas, with 86% overall accuracy. The MODIS-derived chickpea areas showed very strong correspondence to reported statistics.

1. 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.