Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model

Wei, Pengliang; Chai, Douglas; Lin, Tao; Tang, Chao; Du, Meiqi; Huang, Jingfeng

doi:10.1016/j.isprsjprs.2021.02.011

Cited by 70 publications

(39 citation statements)

References 65 publications

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“…The hydrological cycle of this coastal wetland is practically identical to the one observed in the Ebro Delta (Catalonia, Spain), where two different periods of rice field cultivation can be distinguished: the first, from April to September, which includes sowing, growing and harvesting; and the second, from September to February, when the fields remain inactive and flooded [78]. In general, rice cultivation in Mediterranean region has the same main stages as in other climate regions such as the Mekong River Delta (South Vietnam), where the rice production is controlled by the dry season (December to April) and rainy season (May to November) [79], or the Arkansas River Basin (United States), where different crops (rice, cotton, corn and soybean) are developed sharing similar phenological cycles [80].…”

Section: Discussionmentioning

confidence: 99%

Long-Term Hydrological Regime Monitoring of a Mediterranean Agro-Ecological Wetland Using Landsat Imagery: Correlation with the Water Renewal Rate of a Shallow Lake

et al. 2021

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The Natural Park of Albufera (Valencia, Spain) is one of the Spanish Mediterranean wetlands where rice is cultivated intensively. The hydrology of the Albufera Lake, located in the center, combines natural contributions with complex human management. The aim of our study was to develop a new methodology to accurately detect the volume of flood water in complex natural environments which experience significant seasonal changes due to climate and agriculture. The study included 132 Landsat images, covering a 15-year period. The algorithm was adjusted using the NDWI index and simultaneous measurements of water levels in the rice fields. The NDVI index was applied to monitor the cultivated area during the summer. Lake inflows and residence times were also evaluated to quantify how the hydrodynamic of the lake is conditioned by the agricultural management. The algorithm developed is confirmed as a useful ecological tool to monitor the flood cycle of the wetland, being able to detect even the lowest water levels. The flood dynamics are consistent over the fifteen years, being in line with the rice cultivation cycle. Water renewal in Albufera lake is altered with respect to that expected according to the rainfall recorded in the study area, so an improvement in the water management of the hydrological basin is required to optimize the runoff during the rainiest months.

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Section: Discussionmentioning

confidence: 99%

Long-Term Hydrological Regime Monitoring of a Mediterranean Agro-Ecological Wetland Using Landsat Imagery: Correlation with the Water Renewal Rate of a Shallow Lake

et al. 2021

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“…Both the classification method based on deep learning and the traditional machine learning method need a certain amount of rice sample data. Most existing studies used the open land cover classification map drawn by government agencies as the ground truth value of rice extraction research [32,47,48], but the coverage of these land cover classification maps is limited and cannot be updated in time to meet the research needs. In addition, researchers could obtain the basic truth value of rice distribution through field investigations [43].…”

Section: Discussionmentioning

confidence: 99%

“…However, at present, many studies on rice extraction based on multitemporal SAR use public datasets [32,47,48], and the coverage of the public datasets is limited. In addition, tropical or subtropical rice is a year-round active multi-cropping system with a complex planting cycle.…”

Section: Introductionmentioning

confidence: 99%

Rice Mapping Using a BiLSTM-Attention Model from Multitemporal Sentinel-1 Data

Sun

Zhang

et al. 2021

Agriculture

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Timely and accurate rice distribution information is needed to ensure the sustainable development of food production and food security. With its unique advantages, synthetic aperture radar (SAR) can monitor the rice distribution in tropical and subtropical areas under any type of weather condition. This study proposes an accurate rice extraction and mapping framework that can solve the issues of low sample production efficiency and fragmented rice plots when prior information on rice distribution is insufficient. The experiment was carried out using multitemporal Sentinel-1A Data in Zhanjiang, China. First, the temporal characteristic map was used for the visualization of rice distribution to improve the efficiency of rice sample production. Second, rice classification was carried out based on the BiLSTM-Attention model, which focuses on learning the key information of rice and non-rice in the backscattering coefficient curve and gives different types of attention to rice and non-rice features. Finally, the rice classification results were optimized based on the high-precision global land cover classification map. The experimental results showed that the classification accuracy of the proposed framework on the test dataset was 0.9351, the kappa coefficient was 0.8703, and the extracted plots maintained good integrity. Compared with the statistical data, the consistency reached 94.6%. Therefore, the framework proposed in this study can be used to extract rice distribution information accurately and efficiently.

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“…Traditionally, monitoring and assessment of late frost damage in wine grapes have been based on meteorological stations and field investigations [4,5], but this progress is time-consuming, labor-intensive, and very expensive. Large-area and full-coverage remote sensing technology provide the promising potential for the monitoring and assessment of late frost damage as the remote sensing data can be used to extract the crop planting area [6][7][8][9][10], estimate the air and soil temperature [11,12], monitor the crop growth [13,14], assess the agro-meteorological disasters [15][16][17], and predict crop yields [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

“…The wine grape planting area is the essential information for accurately monitoring and assessing late frost damage in wine grapes. Satellite data have been widely used to extract the planting area of crops, such as paddy rice [6,10,21,22], corn [7], wheat [23], and soybean [24] with high to medium spatial resolution [25,26]. However, little research has been carried out on extracting the wine grape planting area using high and medium spatial resolution satellite data, and the few studies mainly focused on small-scale research, such as vineyards [27] or at county scales [28].…”

Section: Introductionmentioning

confidence: 99%

A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data

Huang

Yang

et al. 2021

Remote Sensing

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Late frost damage is one of the main meteorological disasters that affect the growth of wine grapes in spring, causing a decline in wine grapes quality and a reduction in yield in Northwest China. At present, remote sensing technology has been widely used in the field of crop meteorological disasters monitoring and loss assessments, but little research has been carried out on late frost damage in wine grapes. To monitor the impact of late frost in wine grapes accurately and quickly, in this research, we selected the Ningxia planting area as the study area. A practical framework of late frost damage on wine grapes by integrating visible, near-infrared, and thermal infrared satellite data is proposed. This framework includes: (1) Wine grape planting area extraction using Gaofen-1 (GF-1), Landsat-8, and Sentinel-2 based on optimal feature selection and Random Forest (RF) algorithm; (2) retrieval of the land surface temperature (LST) using Landsat-8 thermal infrared data; (3) data fusion using Landsat-8 LST and MODIS LST for a high spatiotemporal resolution of LST with the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM); (4) the estimation of daily minimum air temperature (Tmin) using downscaled LST and meteorological station data; (5) monitoring and evaluation of the degree of late frost damage in wine grapes in April 2020 by combining satellite-derived data and late frost indicators. The results show that the total area of wine grapes extracted in Ningxia was about 39,837 ha. The overall accuracy was 90.47%, the producer’s accuracy was 91.09%, and the user’s accuracy was 90.22%. The root mean square (RMSE) and the coefficient of determination (R2) of the Tmin estimation model were 1.67 ℃ and 0.91, respectively. About 41.12% of the vineyards suffered severe late frost damage, and the total affected area was about 16,381 ha during April 20–25, 2020. This suggests the satellite data can accurately monitor late frost damage in wine grapes by mapping the wine grape area and estimating Tmin. The results can help farmers to take remedial measures to reduce late frost damage in wine grapes, and provide an objective evaluation of late frost damage insurance claims for wine grapes. With the increasing weather extremes, this study has an important reference value for standardized global wine grape management and food security planning.

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Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model

Cited by 70 publications

References 65 publications

Long-Term Hydrological Regime Monitoring of a Mediterranean Agro-Ecological Wetland Using Landsat Imagery: Correlation with the Water Renewal Rate of a Shallow Lake

Long-Term Hydrological Regime Monitoring of a Mediterranean Agro-Ecological Wetland Using Landsat Imagery: Correlation with the Water Renewal Rate of a Shallow Lake

Rice Mapping Using a BiLSTM-Attention Model from Multitemporal Sentinel-1 Data

A Practical Remote Sensing Monitoring Framework for Late Frost Damage in Wine Grapes Using Multi-Source Satellite Data

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