scholarly journals Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China

2020 ◽  
Vol 12 (6) ◽  
pp. 1022 ◽  
Author(s):  
Mingjun Ding ◽  
Qihui Guan ◽  
Lanhui Li ◽  
Huamin Zhang ◽  
Chong Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Poyang Lake ◽  
Southern China ◽  
Rice Paddy ◽  
Kappa Coefficient ◽  
Paddy Fields ◽  
Series Data ◽  
Landsat Data ◽  
Rice Paddies ◽  
Rice Paddy Fields

Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.

scholarly journals Urbanization dramatically altered the water balances of a paddy field-dominated basin in southern China

2015 ◽  
Vol 19 (7) ◽  
pp. 3319-3331 ◽  
Author(s):  
L. Hao ◽  
G. Sun ◽  
Y. Liu ◽  
J. Wan ◽  
M. Qin ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Stream Flow ◽  
Southern China ◽  
Rice Paddy ◽  
Paddy Fields ◽  
Land Use Land Cover ◽  
Water Balances ◽  
Rice Paddy Fields

Abstract. Rice paddy fields provide important ecosystem services (e.g., food production, water retention, carbon sequestration) to a large population globally. However, these benefits are diminishing as a result of rapid environmental and socioeconomic transformations, characterized by population growth, urbanization, and climate change in many Asian countries. This case study examined the responses of stream flow and watershed water balances to the decline of rice paddy fields due to urbanization in the Qinhuai River basin in southern China, where massive industrialization has occurred during the past 3 decades. We found that stream flow increased by 58 % and evapotranspiration (ET) decreased by 23 % during 1986–2013 as a result of a three-fold increase in urban areas and a reduction of rice paddy fields by 27 %. Both high flows and low flows increased significantly by about 28 % from 2002 to 2013. The increases in stream flow were consistent with the decreases in ET and leaf area index monitored by independent remote sensing MODIS (Moderate Resolution Imaging Spectroradiometer) data. Attribution analysis, based on two empirical models, indicated that land-use/land-cover change contributed about 82–108 % of the observed increase in stream flow from 353 ± 287 mm yr−1 during 1986–2002 to 556 ± 145 during 2003–2013. We concluded that the reduction in ET was largely attributed to the conversion of cropland to urban use. The effects of land-use change overwhelmed the effects of regional climate warming and climate variability. Converting traditional rice paddy fields to urban use dramatically altered land surface conditions from an artificial wetland-dominated landscape to an urban land-use- dominated one, and thus was considered an extreme type of contemporary hydrologic disturbance. The ongoing large-scale urbanization of the rice paddy-dominated regions, in humid southern China and East Asia, will likely elevate storm-flow volume, aggravate flood risks, and intensify urban heat island effects. Understanding the connection between land-use/land-cover change and changes in hydrological processes is essential for better management of urbanizing watersheds in the rice paddy-dominated landscape.

scholarly journals Heat-Mitigation Effects of Irrigated Rice-Paddy Fields Under Changing Atmospheric Carbon Dioxide Based on a Coupled Atmosphere and Crop Energy-Balance Model

2021 ◽  
Author(s):  
Hiroki Ikawa ◽  
Tsuneo Kuwagata ◽  
Shigenori Haginoya ◽  
Yasushi Ishigooka ◽  
Keisuke Ono ◽  
...  
Keyword(s):  
Land Use ◽  
Energy Balance ◽  
Air Temperature ◽  
Rice Paddy ◽  
Paddy Fields ◽  
Balance Model ◽  
Rice Paddies ◽  
Irrigated Rice ◽  
Rice Paddy Fields ◽  
Crop Energy

AbstractKnown as the heat-mitigation effect, irrigated rice-paddy fields distribute a large fraction of their received energy to the latent heat during the growing season. The present hypothesis is that increased atmospheric CO2 concentration decreases the stomatal conductance of rice plants and increases the air temperature by means of an increased sensible heat flux. To test this hypothesis, a coupled regional atmospheric and crop energy-balance model is developed and applied to a 300 × 300 km2 region in Japan. Downscaling meteorological variables from grid-mean values of mixed land use (3 × 3 km2) generates realistic typical diurnal cycles of air temperature in rice paddies and adjacent residential areas. The model simulation shows that, on a typical sunny day in summer, doubling the CO2 concentration increases the daily maximum grid-mean air temperature, particularly where rice paddies are present, by up to 0.7 °C. This CO2 effect on the grid-mean air temperature is approximately half the effect of the reduction in rice-paddy area that is postulated to occur on a time scale similar to that of the atmospheric CO2 change. However, within the internal atmospheric boundary layer of the rice paddies, the CO2 effect on the air temperature (+ 0.44 °C) still exceeds the effects of the land-use change (+ 0.11 °C). These results show a potentially important interplay of plant physiological responses regarding atmospheric CO2 in the heat-mitigation effect of rice-paddy fields under a changing climate.

scholarly journals Urbanization dramatically altered the water balances of a paddy field dominated basin in Southern China

2015 ◽  
Vol 12 (2) ◽  
pp. 1941-1972 ◽  
Author(s):  
L. Hao ◽  
G. Sun ◽  
Y. Liu ◽  
J. Wan ◽  
M. Qin ◽  
...  
Keyword(s):  
Paddy Field ◽  
Land Surface ◽  
Urban Areas ◽  
Southern China ◽  
Large Population ◽  
Rice Paddy ◽  
Paddy Fields ◽  
Area Index ◽  
Water Balances ◽  
Rice Paddy Fields

Abstract. Rice paddy fields provide important ecosystem services (e.g., food production, water retention, carbon sequestration) to a large population globally. However, these benefits are declining as a result of rapid environmental and socioeconomic transformations characterized by population growth, urbanization, and climate change in many Asian countries. This case study examined the responses of streamflow and watershed water balances to the decline of rice paddy fields due to urbanization in the Qinhuai River Basin in southern China where massive industrialization has occurred in the region during the past three decades. We found that streamflow increased by 58% and evapotranspiration (ET) decreased by 23% during 1986–2013 as a result of an increase in urban areas of three folds and reduction of rice paddy field by 27%. Both highflows and lowflows increased significantly by about 28% from 2002 to 2013. The increases in streamflow were consistent with the decreases in ET and leaf area index monitored by independent remote sensing MODIS data. The reduction in ET and increase in streamflow was attributed to the large cropland conversion that overwhelmed the effects of regional climate warming and climate variability. Converting traditional rice paddy fields to urban use dramatically altered land surface conditions from a water-dominated to a human-dominated landscape, and thus was considered as one of the extreme types of contemporary hydrologic disturbances. The ongoing large-scale urbanization in the rice paddy-dominated regions in the humid southern China, and East Asia, will likely elevate stormflow volume, aggravate flood risks, and intensify urban heat island effects. Understanding the linkage between land use change and changes in hydrological processes is essential for better management of urbanizing watersheds.

scholarly journals Dry matter accumulation and plant type of the high yielding soybean grown under converted rice paddy fields.

1984 ◽  
Vol 53 (4) ◽  
pp. 510-518 ◽  
Author(s):  
Kimio NAKASEKO ◽  
Humio NOMURA ◽  
Kanji GOTOH ◽  
Takeshi OHNUMA ◽  
Yoshikatsu ABE ◽  
...  
Keyword(s):  
Dry Matter ◽  
Rice Paddy ◽  
Paddy Fields ◽  
Dry Matter Accumulation ◽  
Plant Type ◽  
Rice Paddy Fields

scholarly journals Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data

2018 ◽  
Vol 7 (11) ◽  
pp. 418 ◽  
Author(s):  
Tian Jiang ◽  
Xiangnan Liu ◽  
Ling Wu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Time Series ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Vegetation Index ◽  
Southern China ◽  
Series Data ◽  
Support Vector ◽  
Complex Landscape

Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classification approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classification. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to fine tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classification by remote sensing and deep learning technique in the future study.

Sign in / Sign up

Export Citation Format

Share Document