Mapping paddy rice agriculture in a highly fragmented area using a geographic information system object-based post classification process

Shiu, Yi-Shiang; Lin, Meng‐Lung; Huang, Chun-Jen; Chu, Tzu-How

doi:10.1117/1.jrs.6.063526

Cited by 11 publications

(5 citation statements)

References 4 publications

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“…Several object-based classification studies use field boundary information obtained from either high-resolution (meter-scale) commercial satellites or local agricultural administrations [41]. However, the cost of commercial data is high and the accessibility of official cadastral data is low.…”

Section: Object Extractionmentioning

confidence: 99%

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

Xiao

2021

Remote Sensing

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In tropical/subtropical monsoon regions, accurate rice mapping is hampered by the following factors: (1) The frequent occurrence of clouds in such areas during the rice-growing season interferes strongly with optical remote sensing observations; (2) The agro-landscape in such regions is fragmented and scattered. Rice maps produced using low spatial resolution data cannot well delineate the detailed distribution of rice, while pixel-based mapping using medium and high resolutions has significant salt-and-pepper noise. (3) The cropping system is complex, and rice has a rotation schedule with other crops. Therefore, the Phenology-, Object- and Double Source-based (PODS) paddy rice mapping algorithm is implemented, which consists of three steps: (1) object extraction from multi-temporal 10-m Sentinel-2 images where the extracted objects (fields) are the basic classification units; (2) specifying the phenological stage of transplanting from Savitzky–Golay filtered enhanced vegetation index (EVI) time series using the PhenoRice algorithm; and (3) the identification of rice objects based on flood signal detection from time-series microwave and optical signals of the Sentinel-1/2. This study evaluated the potential of the combined use of the Sentinel-1/2 mission on paddy rice mapping in monsoon regions with the Hangzhou-Jiaxin-Huzhou (HJH) plain in China as the case study. A cloud computing approach was used to process the available Sentinel-1/2 imagery from 2019 and MODIS images from 2018 to 2020 in the HJH plain on the Google Earth Engine (GEE) platform. An accuracy assessment showed that the resultant object-based paddy rice map has a high accuracy with a producer (user) accuracy of 0.937 (0.926). The resultant 10-m paddy rice map is expected to provide unprecedented detail, spatial distribution, and landscape patterns for paddy rice fields in monsoon regions.

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Section: Object Extractionmentioning

confidence: 99%

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

Xiao

2021

Remote Sensing

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“…Previous studies have proposed the use of the DL function to fit MODIS NDVI observations from a single season, which allows for less homogeneity in the temporal distribution while preserving important cloud-free observations [23,24]. In this study, we applied the DL model to fit Landsat EVI observations for one season using Equation (1), resulting in the generation of daily EVI data (dEVI DL ) (as shown in Figure 1).…”

Section: The DL Functionmentioning

confidence: 99%

“…In the 2000s, land holdings per family in China were recorded at 0.53 ha [1]. In Korea, approximately 69% of farms have small land parcels measuring less than 1.0 ha [2].…”

Section: Introductionmentioning

confidence: 99%

“…In order to improve the fusion accuracy, algorithms have progressed from directly merging multiple satellite photos on distinct forecast dates to the weighted fusion of partially cloud-free images [32,36,37]. However, there are four critical issues that these fusion methods encounter, which need to be addressed: (1) The EVI, which can reduce the saturating effects of dense canopy as much as possible, performs better than the NDVI [38]. (2) The spatial resolution of MODIS pixels (500/250 m) covers fragmented land parcels with multi-crop planting systems, resulting in week spatial autocorrelation between the MODIS and Landsat VI temporal shapes.…”

Section: Introductionmentioning

confidence: 99%

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Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes

Xue,

Ko,

Cao

et al. 2023

Remote Sensing

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Although the Landsat 30 m Enhanced Vegetation Index (EVI) products are important input variables in land surface models, recurring Landsat 5/7 EVI time series over cloud-prone, fragmented, and mosaic agricultural landscapes is still a great challenge. In this study, we put forward a simple, but effective “Light and Temperature-Driven Growth model and Double Logistic function fusion algorithm” (LTDG_DL). The empirical basis of the LTDG_DL algorithm was traced from the de Wit crop growth simulation model and the commonly observed nonlinear correlation between the EVI and the Leaf Area Index (LAI). It assimilates the ground daily solar radiation and air temperature to generate seasonal profiles of the empirical LAI and EVI and conducts the within-season calibration of the empirical EVI by adjusting crop growth using cloud-free Landsat EVI observations. The initial date of seedling emergence (DOYini) and the accumulated Growing Degree Days for completion of the vegetative and Flowering stage (FGDDs) were variables to which the algorithm’s accuracy was most sensitive. The variable-constrained optimization of the LTDG_DL algorithm was performed by loading the seedling emergence calendar of local prevailing crops and establishing an FGDD lookup table with an exhaustive sampling without replication method. Compared to temporal interpolation functions and Landsat–MODIS spatiotemporal fusion algorithms, the LTDG_DL algorithm had superior performance in the predictions of the EVI increment slope at the vegetative growth stage, the timing of the peak EVI, and the protection of key Landsat EVI observations over cloud-contaminated and complex landscape agricultural systems. Finally, the advantages and limitations of the LTDG_DL algorithm are discussed.

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“…In the meantime, researchers have been using both optical and SAR data with spatial extents for crop mapping (mainly rice). Some of their approaches have been: unsupervised and supervised (Cheema and Bastiaanssen 2010;Konishi et al 2007;Lin 2012;Turner and Congalton 1998), rule-based (Boschetti et al 2017), phenology-based (Dong et al 2015(Dong et al , 2016, and time-series classification algorithms (Dong et al 2016;Shew and Ghosh 2019). Besides, MODIS too has been effectively used for rice mapping and monitoring application scales (Burchfield et al 2016;Nelson et al 2014;Shapla et al 2015).…”

Section: Rice Crop In Bangladesh and Recent Efforts In Mappingmentioning

confidence: 99%

In-Season Crop-Area Mapping for Wheat and Rice in Afghanistan and Bangladesh

Tiwari

Qamer

Matin

et al. 2021

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region

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Cereal grains are the most commonly grown crops in the world. Wheat and rice are important commodities which contribute to 50% of the world’s food-calorie intake (McKevith Nutr Bull 29(2): 111–142, 2004). These two cereals are critical to food security in the developing regions. In this context, crop-mapping services can be used for detailed monitoring of the cultivated areas; it can also provide the area statistics of specific crops and the data on their intensity across the landscape. This mapping process is also valuable for government agencies since it provides them with critical information that can be used to manage their stocks (for imports and exports). This chapter dwells on a crop-mapping service developed under the SERVIR-HKH program. In this regard, the needs assessment was carried out with the assistance of the governments of Bangladesh and Afghanistan through a consultation workshop. Wheat mapping in Afghanistan and rice mapping in Bangladesh were the top priorities for the respective governments. Here, we discuss two particular mapping exercises that were undertaken in these two countries: wheat mapping in Afghanistan at a national level and the mapping of Boro rice in selected districts of Bangladesh.

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Mapping paddy rice agriculture in a highly fragmented area using a geographic information system object-based post classification process

Cited by 11 publications

References 4 publications

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

Mapping Paddy Rice with Sentinel-1/2 and Phenology-, Object-Based Algorithm—A Implementation in Hangjiahu Plain in China Using GEE Platform

Improving Reliability in Reconstruction of Landsat EVI Seasonal Trajectory over Cloud-Prone, Fragmented, and Mosaic Agricultural Landscapes

In-Season Crop-Area Mapping for Wheat and Rice in Afghanistan and Bangladesh

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