Irrigation History Estimation Using Multitemporal Landsat Satellite Images: Application to an Intensive Groundwater Irrigated Agricultural Watershed in India

Abstract

Groundwater has rapidly evolved as a primary source for irrigation in Indian agriculture. Over-exploitation of the groundwater substantially depletes the natural water table and has negative impacts on the water resource availability. The overarching goal of the proposed research is to identify the historical evolution of irrigated cropland for the post-monsoon (rabi) and summer cropping seasons in the Berambadi watershed (Area = 89 km 2 ) of Kabini River basin, southern India. Approximately five-year interval irrigated area maps were generated using 30 m spatial resolution Landsat satellite images for the period from 1990 to 2016. The potential of Support Vector Machine (SVM) was assessed to discriminate irrigated and non-irrigated croplands. Three indices, Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI) and Enhanced Vegetation Index (EVI), were derived from multi-temporal Landsat satellite images. Spatially distributed intensive ground observations were collected for training and validation of the SVM models. The irrigated and non-irrigated croplands were estimated with high classification accuracy (kappa coefficient greater than 0.9). At the watershed scale, this approach allowed highlighting the contrasted evolution of multiple-cropping (two successive crops in rabi and summer seasons that often imply dual irrigation) with a steady increase in the upstream and a recent decrease in the downstream of the watershed. Moreover, the multiple-cropping was found to be much more frequent in the valleys. These intensive practices were found to have significant impacts on the water resources, with a drastic decline in the water table level (more than 50 m). It also impacted the ecosystem: Groundwater level decline was more pronounced in the valleys and the rivers are no more fed by the base flow.

An Unsupervised Classification Algorithm for Multi-Temporal Irrigated Area Mapping in Central Asia

“…However, ground-based information on acreages of irrigated land is scarce and often unreliable, while the adoption of remote sensing technology in the planning and operational management of irrigated agricultural systems still represents a challenge [5]. Existing cropland mapping algorithms usually require detailed reference cropland layers (e.g., [6][7][8]), large number of sample training points (e.g., [9,10]) or empirical look-up tables (e.g., [11,12]) to build rule-based classifiers. The transferability in time and space of these supervised classification methods is therefore not always guaranteed.…”

Potash fertilizer promotes incipient salinization in groundwater irrigated semi-arid agriculture

“…Potential evapotranspiration is 1100 mm (aridity index P/PET of 0.7). Agricultural cropland and forest cover are the major land use in the catchment with 52% and 32%, respectively 64 . The development of tube well irrigation since 30 years has induced a shift from the rainfed to irrigated agriculture.…”

“…As groundwater availability for irrigation is limited by the low transmissivity of the aquifer, and by the fact that electricity for submersible pumps, although freely provided by the government, is only available for 3-4 h/day, the tube well density is increasing in the catchment 37 . About 5000 farms exist in the catchment, with an average size of about 1 ha, divided into cultivated plots with an average size of 0.2 ha 64,65 . This lead to a large diversity of agricultural practices, depending on crop and farm types.…”

Extracting Khmer Rouge Irrigation Networks from Pre-Landsat 4 Satellite Imagery Using Vegetation Indices

“…It is one of the most commonly used vegetation indices and is efficient at detecting irrigated land. It has the advantage of providing a small range in values, −1.0 to 1.0, and a clear cutoff value, indicating that all values below 0 identify non-vegetated land or water bodies [34,40]. Both the corrected transformed vegetation index (CTVI) and the Thiam's transformed vegetation index (TTVI) expand upon the NDVI, and are part of the same family of slope-based vegetation indexes.…”