Mapping and monitoring of biophysical and socio economic characteristics of dryland cereals and grain legumes producing areas is key for developing effective targeting strategies, dissemination of new technologies and sustainable crop management and diversification options. This can help in the allocation of limited resources to achieve potential benefits and provide actionable information for decision makers.

1. Mapping and monitoring of biophysical and socio economic characteristics of dryland cereals and
grain legumes producing areas is key for developing effective targeting strategies, dissemination
of new technologies and sustainable crop management and diversification options. This can help
in the allocation of limited resources to achieve potential benefits and provide actionable
information for decision makers.
1.Maps of ICRISAT mandate
crops
2.Ground information website for
open use as a public good.
3.Database on actual yields,
potential yield estimates and
yield gaps
• Develop methods and
approaches for mapping areas
affected by drought, heat and
pest/diseases using remote
sensing imagery (NDVI) and GIS
based tools.
Seasonally updated information on
croplands
Mapping abiotic and biotic stresses in
dryland agriculture areas
• Mapping ICRISAT mandate crops
using satellite images of
moderate resolution at global
scale and high resolution at
country level.
• Develop a database on ground
survey used in remote sensing
analysis for dryland crop eco-
systems.
• Estimate crop yields using crop
simulation models in the spatial
domain using inputs from remote
sensing imagery.
1.Maps of drought and heat prone
areas and their characteristics
2.Assessment of vulnerability of
current and potential crop
environments to diseases and
pests
1. Characterization of target
domains for new crop
technologies
2. Multi-scale approach to link
characterizations to livelihood
and production constraints
3. Assessment of how markets will
change under different
scenarios
• Identify dissemination areas for
new varieties/technologies
• Develop methods for defining
dryland cereals / grain legumes
mega environments
Kharif (2010-11) Rabi (2010-11) Summer (2010-11)
Characterization of Dryland cereals & grain
legumes mega-environments for effective
technology targeting
RS-GIS unit, RDS

2. Despite growing demands for food production because of an increasing population, in South Asia there are vast areas of land left
fallow. When there is insufficient irrigation for year-round cropping, lands are left fallow after the cultivation of rainfed rice, and even
after irrigated rice. After the rice harvest there is often adequate stored soil moisture, along with any subsequent rainfall, to support a
following crop, at least one of short duration. Legume yields are often poor and, as a result large areas with the potential for
cultivating a post-rice crop are left fallow. The main reasons for this are the suite of agronomic difficulties in establishing and growing
a crop after rice, where a hard plough-pan is deliberately created to retain water for rice culture. However, there are now proven
technologies available (e.g., short-duration legume varieties, seed treatments, mechanization) that would make it feasible and
profitable to increase the area of land to grow post-rice crops. Successful demonstration and extension of these technologies would
open the way for greater income generation by the economically disadvantaged rice cultivators of South Asia, who presently have
few options for enterprise diversification. Mapping post-rice fallows and rabi fallows using remote sensing imagery will help identify
areas for demonstration and dissemination of this technology which are potential areas.
Cropping pattern changesLand use change dynamics
1. Land use changes are a regular phenomenon due to agro-ecological, socio-economic and policy decisions.
Some changes are so significant that they make a large difference in the agro-economic scenario of the
place, specifically when water availability affects critically the decision making of the small holder farmers.
Chickpea expansion in some districts of Andhra Pradesh is a good example of such change. Chickpea
growing areas expanded in these districts gradually replacing another crop since there was a good
economic gain in growing it. Less farm operations and mechanization also promoted this crop.
2. Remotely sensed imagery can be effectively used to monitor cropland changes and identify the niches for
present changes in cropping pattern and its impact.
2000-01 2012-13
Land use / land cover
Groundnut / sorghum / fallows
Maize / mixed crops
Cotton-chilli
Orchards / mixed crops
Rice-mixed crops
Other LULC
Chickpea
RS-GIS unit, RDS
Tracking of crop expansion / yield estimations and impact
Andhra Pradesh

3. The use of remote sensing, GPS and GIS as tools to understand tracking the adoption of NRM technologies such as RHS, provides a spatial
dimension to the method. Remotely sensed imagery provides temporal spatial information on the change dynamics of LULC an important
characteristic variable which depends on the adoption of RHS. A ground information collection mission for LULC mapping provides first-hand
information of the cropping pattern and also facilitates interaction with the village level extension officers and farmers. Global Positioning System
(GPS) instrument is used to collect the location coordinates of the RHS which is the most economical when compared to using remote sensing
imagery. It is also constrained by the spatial resolution of the remote sensing imagery and cloud cover during the rainy season when it is the ideal
time. Whereas the very high resolution imagery (GeoEye) becomes very costly to identify a RHS and can also be contaminated by cloud cover, it
yields similar information on LULC for higher cost than the normal resolution imagery (LANDSAT, IRS LISS IV etc) which is very economical.
An integration of two sources of information from the ground using GPS and farmer interviews along with spatial information on LULC using remote
sensing imagery will not only track the adoption of RHS but also provides the impact of adoption of this technology. It can also be said that this
method of using spatial information along with ground information will make it easier to scale up and scale out such methods over larger domains.
Base maps (Drainage, watershed
and settlements)
Monitoring rainwater harvesting structures
• Use of open datasets such as SRTM DEM (90m)
to delineate and do basic morphometric analysis
• Use of IRS-LISS IV, Landsat imagery and MODIS
were used to prepare Land use/ Landover (LULC)
maps and changes in LULC during selected years.
• Data on source of irrigation before and after
adoption of RHS identifies the relationship
between RHS adoption and availability of
groundwater for irrigation.
• Analyze and understand the reasons for adoption
and inturn impact of adoption as a result of LULC
changes.
Impact of rainwater harvesting structures
RS-GIS unit, RDS

4. 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.
• 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
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.