Framework for agricultural performance assessment based on MODIS multitemporal data

Published Date
June 14, 2019
Type
Journal Article
Framework for agricultural performance assessment based on MODIS multitemporal data
Authors:
Dimo Dimov
Fabian Loew, Johannes H. Uhl, Shavkat Kenjabaev, Olena Dubovyk, Mirzahayot Ibrakhimov, Chandrashekhar Biradar

We present a hierarchical classification framework for automated detection and
mapping of spatial patterns of agricultural performance using satellite-based Earth observation
data exemplified for the Aral Sea Basin (ASB) in Central Asia. The core element of the framework
is the derivation of a composite agricultural performance index which is composed of
different subindicators taking into account cropping intensity, crop diversity, crop rotations, fallow
land frequency, land utilization, water use efficiency, and water availability.We derive these
subindicators from net primary productivity and evapotranspiration data obtained from the
MODIS sensor on board the Terra satellite during the observation period from 2000 to 2016,
as well as from cropland maps created through multiannual classification of normalized difference
vegetation index (NDVI). We classified pixel-based NDVI time series covering more than
8 × 106 ha of irrigated cropland based on a hierarchical approach concatenating unsupervised
and supervised classification techniques to automatically generate and refine training labels,
which are then used to train a decision fusion classifier, achieving an average overall accuracy
of 78%. The results give unprecedented insights into spatial patterns of agricultural performance
in the ASB. The proposed method is transferable and applicable for global-scale mapping, and
the results of this remote sensing-aided assessment can provide important information for
regional agricultural planning purposes.

Citation:
Dimo Dimov, Fabian Loew, Johannes Uhl, Shavkat Kenjabaev, Olena Dubovyk, Mirzahayot Ibrakhimov, Chandrashekhar Biradar. (14/6/2019). Framework for agricultural performance assessment based on MODIS multitemporal data. Journal of Applied Remote Sensing, 13 (2).
Keywords:
clustering;
crop diversity
cropland mapping
cropping intensity
land use indicators
agricultural performance
supervised classification
unsupervised classification
cropland mapping; land use indicators; clustering; supervised classification; cropping intensity; crop diversity; agricultural performance