Mapping and monitoring of food legumes and dryland cereal production systems
Abstract
Mapping and monitoring of the agricultural production systems on a regular interval provide important
spatial matrix on the status, trend, and options for effective intervention at multiple scales. The recent
advances in agrogeoinformatics
bigdata
enriched with increasing openaccess
protocols become an
integral part of solving the food security equation. This paper demonstrates use of an integrated earth
observation system (EOS) for mapping and monitoring major agricultural production systems. The
approach uses multitemporal
and multiscale
remote sensing data coupled with insitu
observation to
map the legume and cereal production systems. The support vector machine (SVM) classification was
found to be the best with overall classification accuracy of 82%. The insitu
data on crop grain and
straw yields were measured using nested sampling approach. The best fit equation of yield values were
regressed with remote sensing indices (NDVI and EVI). The significant correlation (R ) value of cereal
and lentil crop were 0.74 and 6.9 at p<;0.01 respectively. The R value between observed yield and
predicted yield was 0.80 and 0.97 in cereal and lentil crops respectively. The predicted yield based on
remote sensing data varies from 3,303 to 5,710 kg ha and mean yield is 3,840 kg ha . The
productivity of the cereal crop was varies from 4228 kg ha to 4598 kg ha while lentil crop was
between 304 to 1,500 kg ha . The huge inter and intra field variably was observed through the study
areas. Such information yielded vital information about yield gaps exists within and across the fields.
Study is in progress to develop systematic and semiautomated
algorithms to map and monitor the
agricultural production on regular interval to quantify the changes in the cropping pattern, rotation,
production and impacts of the technological interventions and exante
analysis