Mapping Irrigated Areas Based on Remotely Sensed Crop Phenology and Soil Moisture

Abstract

Artificial irrigation is critical for improving soil moisture conditions and ensuring crop growth. Its irrational deployment can lead to ecological and environmental issues. Mapping and understanding the changes in irrigated areas are vital to effectively managing limited water. However, most researchers map irrigated areas with a single data resource, which makes it hard to detect irrigated signals in complex situations. The case study area for this paper was China’s winter wheat region, and an irrigated area map was generated by analyzing the effects of artificial irrigation on crop phenological characteristics and soil moisture time series. The mapping process involved three steps: (1) generating a basic irrigated map by employing the ISODATA classification method on the Kolmogorov–Smirnov test irrigation signals from the microwave remote sensing data and reanalysis data; (2) creating the other map with the maximum likelihood ratio classification and zoning scheme on the phenological parameters extracted from the NDVI time series; and (3) fusing these two maps at the decision level to obtain the final map with a higher spatial resolution of 1 km. The map was evaluated against existing irrigated area data and was highly compatible with GMIA 5.0. The overall accuracy (OA) was 73.49%.

A Comparative Analysis of Random Forest and Support Vector Machines for Classifying Irrigated Cropping Areas in The Upper-Comoé Basin, Burkina Faso

“…It is therefore difficult to know a priori whether a given classifier will systematically deliver the best results. Numerous comparisons of classifiers exist in the literature, (9,10,(42)(43)(44) and show that the supremacy of one classifier over another is not always guaranteed, due to application conditions that are not always respected, and quite simply to the difference between the environments to be classified.…”

“…The mapping of irrigated agricultural areas, combined with a study of water needs, enables an improved management of agricultural water (8) .Remote sensing-based approaches for mapping irrigated agricultural areas are cost-effective and are particularly useful in low-income countries where resources are limited for field surveys. (9)(10)(11) The exploitation of remote sensing images can be done in several ways, ranging from photo-interpretation to classification. Image classification is the process of categorizing and labeling groups of pixels within an image based on userdefined rules (12) .…”