Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Abstract

Rice is a staple food that feeds nearly half of the world’s population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.

Geospatial Technology for Sustainable Agricultural Water Management in India—A Systematic Review

“…Secondly, the availability of numerous free optical sensors, with significantly improved resolutions in recent years, has bolstered this trend. Lastly, the straightforward and uncomplicated interpretation of the data has also played a significant role in the widespread adoption of this approach [126]. Optical sensors are frequently used because of their simple processing and the ease of interpreting their images.…”

“…The integration of mobile devices, GIS software, GPS, and wireless connectivity enables internet-based GIS access. The amalgamation of RS, GIS, and artificial intelligence promotes the automation of data collection, analysis, and decision making in water management [126]. Drones, which fly lower, are not affected by clouds and can reach remote, hard-to-access areas to collect data.…”