Abstract
Rice (Oryza sativa L.) is a staple food crop for more than half of the world’s population. Rice production is facing a myriad of problems, including water shortage, climate, and land-use change. Accurate maps of rice growth stages are critical for monitoring rice production and assessing its impacts on national and global food security. Rice growth stages are typically monitored by coarse-resolution satellite imagery. However, it is difficult to accurately map due to the occurrence of mixed pixels in fragmented and patchy rice fields, as well as cloud cover, particularly in tropical countries. To solve these problems, we developed an automated mapping workflow to produce near real-time multi-temporal maps of rice growth stages at a 10-m spatial resolution using multisource remote sensing data (Sentinel-2, MOD13Q1, and Sentinel-1). This study was investigated between 1 June and 29 September 2018 in two (wet and dry) areas of Java Island in Indonesia. First, we built prediction models based on Sentinel-2, and fusion of MOD13Q1/Sentinel-1 using the ground truth information. Second, we applied the prediction models on all images in area and time and separation between the non-rice planting class and rice planting class over the cropping pattern. Moreover, the model’s consistency on the multitemporal map with a 5–30-day lag was investigated. The result indicates that the Sentinel-2 based model classification gives a high overall accuracy of 90.6% and the fusion model MOD13Q1/Sentinel-1 shows 78.3%. The performance of multitemporal maps was consistent between time lags with an accuracy of 83.27–90.39% for Sentinel-2 and 84.15% for the integration of Sentinel-2/MOD13Q1/Sentinel-1. The results from this study show that it is possible to integrate multisource remote sensing for regular monitoring of rice phenology, thereby generating spatial information to support local-, national-, and regional-scale food security applications.
Highlights
Regular monitoring of the paddy area is vital as rice production supports rural livelihoods in Asia, where more than 1.21 billion tonnes of rice were harvested to feed 4.56 billion people in 2018 [1,2]
The spectral bands of rice growth stages are concurrent with a multi-angle spectrometer from Sun et al [87], except
This paper has provided an automatic process to build multitemporal maps for rice growth stages with a 10-m spatial resolution, which is sufficient for crop monitoring at the local and national level in developing countries
Summary
Regular monitoring of the paddy area is vital as rice production supports rural livelihoods in Asia, where more than 1.21 billion tonnes of rice were harvested to feed 4.56 billion people in 2018 [1,2]. The common practice of monitoring rice crops in Indonesia is by using local government officers at a sub-district level to collect data based on field visits and information provided by the farmers. This data is expensive to collect, non real-time, and inefficient to handle spatio-temporal changes in rice-producing areas [9,10,11]. The paddy area needs to be monitored in near-time due to the need for a continuous water supply from irrigation canals and fertiliser inputs at critical stages [12]. Timely and accurate information on rice growth stages is vital for planning and management of the rice farming system, which is critical for sustainable food security at the regional and national scale [13,14]
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