Monitoring irrigation dynamics in paddy fields using spatiotemporal fusion of Sentinel-2 and MODIS

Paddy fields play very important environmental roles in food security, water resource management, biodiversity conservation, and climate change. Therefore, reliable broad-scale paddy field maps are essential for understanding these issues related to rice and paddy fields. Here, we propose a novel paddy field mapping method that uses Sentinel-1 synthetic aperture radar (SAR) time series that are robust for cloud cover, supplemented by Sentinel-2 optical images that are more reliable than SAR data for extracting irrigated paddy fields. Paddy fields were provisionally specified by using the Sentinel-1 SAR data and a conventional decision tree method. Then, an additional mask using water and vegetation indexes based on Sentinel-2 optical images was overlaid to remove non-paddy field areas. We used the proposed method to develop a paddy field map for Japan in 2018 with a 30 m spatial resolution. The producer’s accuracy of this map (92.4%) for non-paddy reference agricultural fields was much higher than that of a map developed by the conventional method (57.0%) using only Sentinel-1 data. Our proposed method also reproduced paddy field areas at the prefecture scale better than existing paddy field maps developed by a remote sensing approach.

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Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine

Geo-spatial Information Science ◽

10.1080/10095020.2021.1984183 ◽

2021 ◽

pp. 1-16

Author(s):

Daniel Marc G. dela Torre ◽

Jay Gao ◽

Cate Macinnis-Ng ◽

Yan Shi

Keyword(s):

Google Earth ◽

Paddy Fields ◽

Google Earth Engine ◽

Sentinel 2

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Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

Geosfera Indonesia ◽

10.19184/geosi.v6i2.24506 ◽

2021 ◽

Vol 6 (2) ◽

pp. 173

Author(s):

Putri Tunjung Sari ◽

Indarto Indarto ◽

Marga Mandala ◽

Bowo Eko Cahyono

Keyword(s):

Principal Component Analysis ◽

Soil Quality ◽

Paddy Field ◽

Quality Index ◽

Principal Component ◽

Component Analysis ◽

Paddy Fields ◽

Organic Fertilizers ◽

Soil Quality Index ◽

Sentinel 2

The use of intensive chemical inputs causes lower availability of nutrients, organic matter, cation exchange capacity, and soil degradation.Therefore, this study aims to assess the soil quality index (SQI) for paddy fields in Jember, East Java, Indonesia. Input data for this study consist of land cover (interpreted from the Sentinel-2 image), soil type, and slope maps. Furthermore, the procedure to calculate soil quality index (SQI) include (1) spatial analysis to create the land unit, (2) preparation of soil sampling, (3) soil chemical analysis, (4) principal component analysis (PCA), and (5) reclassifying soil quality index (SQI). The PCA results showed that three variables i.e., % sand, total- P, and % silt were strongly correlated to SQI, while three classes namely very low, low, and medium of SQI were sufficiently used to describe the spatial variability of the paddy field. Furthermore, approximately 41.14% of the paddy field area were classed as very low while 52.23%, and 6.63% were categorized as low and medium SQI respectively. Based on the results, about 93.37% of paddy fields in Jember Regency still require improvement in soil quality via the addition of ameliorants such as organic fertilizers to increase quality and productivity. This application needs to focus on areas with very low-low quality hence, the quality increased to the medium category. Keywords : Mapping; Soil Quality Index (SQI); PCA; Paddy field Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

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Soil Salinity Assessment in Irrigated Paddy Fields of the Niger Valley Using a Four-Year Time Series of Sentinel-2 Satellite Images

Remote Sensing ◽

10.3390/rs12203399 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3399

Author(s):

Issaka Moussa ◽

Christian Walter ◽

Didier Michot ◽

Issifou Adam Boukary ◽

Hervé Nicolas ◽

...

Keyword(s):

Grain Yield ◽

Temporal Resolution ◽

Soil Salinity ◽

Bare Soil ◽

Paddy Fields ◽

Soil Conditions ◽

High Temporal Resolution ◽

Total Biomass ◽

Growing Seasons ◽

Sentinel 2

Salinization is a major soil degradation threat in irrigated systems worldwide. Irrigated systems in the Niger River basin are also affected by salinity, but its spatial distribution and intensity are not currently known. The aim of this study was to develop a method to detect salt-affected soils in irrigated systems. Two complementary approaches were tested: salinity assessment of bare soils using a salinity index (SI) and monitoring of indirect effects of salinity on rice growth using temporal series of a vegetation index (NDVI). The study area was located south of Niamey (Niger) in two irrigated systems of rice paddy fields that cover 6.5 km2. We used remote-sensing and ground-truth data to relate vegetation behavior and reflectance to soil characteristics. We explored all existing Sentinel-2 images from January 2016 to December 2019 and selected cloud-free images on 157 dates that covered eight successive rice-growing seasons. In the dry season of 2019, we also sampled 44 rice fields, collecting 147 biomass samples and 180 topsoil samples from January to June. For each field and growing season, time-integrated NDVI (TI-NDVI) was estimated, and the SI was calculated for dates on which bare soil conditions (NDVI < 0.21) prevailed. Results showed that since there were few periods of bare soil, SI could not differentiate salinity classes. In contrast, the high temporal resolution of Sentinel-2 images enabled us to describe rice-growing conditions over time. In 2019, TI-NDVI and crop yields were strongly correlated (r = 0.77 with total biomass yield and 0.82 with grain yield), while soil electrical conductivity was negatively correlated with both TI-NDVI (r = −0.38) and crop yield (r = −0.23 with total biomass and r = −0.29 with grain yield). Considering the TI-NDVI data from 2016–2019, principal component analysis followed by ascending hierarchical classification identified a typology of five clusters with different patterns of TI-NDVI during the eight growing seasons. When applied to the entire study area, this classification clearly identified the extreme classes (i.e., areas with high or no salinity). Other classes with low TI-NDVI (i.e., during dry seasons) may be related to areas with moderate or seasonal soil salinity. Finally, the high temporal resolution of Sentinel-2 images enabled us to detect stresses on vegetation that occurred repeatedly over the growing seasons, which may be good indicators of soil constraints due to salinity in the context of the irrigated paddy systems of Niger. Further research will validate the ability of the method developed to detect moderate soil salinity constraints over large areas.

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