Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India

Abstract

Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The interannual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated areas to local water availability. In this study, we have developed and tested a methodology for monitoring these spatiotemporal variations using Sentinel-1 and -2 observations over the Kudaliar catchment, Telangana State (~1000 km 2 ). These free radar and optical data have been acquired since 2015 on a weekly basis over continental areas, at a high spatial resolution (10-20 m) that is well adapted to the small areas of South Indian field crops. A machine learning algorithm, the Random Forest method, was used over three growing seasons (January to March and July to November 2016 and January to March 2017) to classify small patches of inundated rice paddy, maize, and other irrigated crops, as well as surface water stored in the small reservoirs scattered across the landscape. The crop production comprises only irrigated crops (less than 20% of the areas) during the dry season (Rabi, December to March), to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November). Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps. This methodology shows the potential of automatically monitoring, in near real time, the high short term variability of irrigated area totals in South India, as a proxy for Remote Sens. 2017, 9, 1119; doi:10.3390/rs9111119 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 1119 2 of 21 estimating irrigated water and groundwater needs. These are estimated over the study period to range from 49.5 ± 0.78 mm (1.5% uncertainty) in Rabi 2016, and 44.9 ± 2.9 mm (6.5% uncertainty) in the Kharif season, to 226.2 ± 5.8 mm (2.5% uncertainty) in Rabi 2017. This variation must be related to groundwater recharge estimates that range from 10 mm to 160 mm·yr −1 in the Hyderabad region. These dynamic agro-hydrological variables estimated from Sentinel remote sensing data are crucial in calibrating runoff, aquifer recharge, water use and evapotranspiration for the spatially distributed agro-hydrological models employed to quantify the impacts of agriculture on water resources.

Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

“…Zhou et al [41] also combined Sentinel-1 and Landsat-8 but focused on the detection of winter wheat in China. Recently, Sentinel-1 was also shown to improve the detection of irrigated crops in India, especially during the monsoon season [42]. For the agricultural landscape in the United States, recent work by Torbick et al [43] has demonstrated within-season crop inventories based on near real-time (NRT) Sentinel-1, Sentinel-2, and Landsat-8 imagery.…”

Multi-Temporal Monsoon Crop Biomass Estimation Using Hyperspectral Imaging

“…The deviation of predicted and measured FMB did not differ much among the different levels of N and water supply. However, there was a trend of overestimation at the initial stage and underestimation at the later stages of crop growth.to local water availability [2]. Irrigated crop production is a major contributor to the green revolution, which has enabled the country to be self-sufficient [3], accompanied by fertiliser application and other inputs in semi-arid parts of India.…”

“…to local water availability [2]. Irrigated crop production is a major contributor to the green revolution, which has enabled the country to be self-sufficient [3], accompanied by fertiliser application and other inputs in semi-arid parts of India.…”

SENTINEL-1&2 FOR NEAR REAL TIME CROPPING PATTERN MONITORING IN DROUGHT PRONE AREAS. APPLICATION TO IRRIGATION WATER NEEDS IN TELANGANA, SOUTH-INDIA

“…Less Sentinel-2 were used in comparison with the previous work published over the same area for the 3 first growing seasons . Differences between both studies for those 3 seasons are low compare to the uncertainty of EEVs retrieval presented in Ferrant et al, 2017. Small irrigated areas are detected during the dry conditions of R2016 and K2016, with around 5% of irrigated areas, with moderate classification confusion.…”

“…The surface water harvesting system is full, surface water have reached 3.4% of the catchment area, decreasing to 1.4% at the end of April 2017 with the cumulated effect of ground water recharge, evaporation and pumping for irrigation. Rice area reaches the highest extent : 22.73%, table 2, vs 17.11% estimated in Ferrant et al (2017). Rice paddies are mainly located downstream of the tanks, benefiting from this surface water abundance.…”