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

Torre, Daniel Marc G. dela; Gao, Jay; Macinnis‐Ng, Cate; Yan, Shi

doi:10.1080/10095020.2021.1984183

Cited by 14 publications

(6 citation statements)

References 60 publications

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“…The delineation of the rice cropping calendar hinged on the representative profiles of VH and NDVI time-series data. This calendar encompassed rice cropping intensity and growth stages, including soil tillage, planting, as well as vegetative, reproductive, and maturity phases 15 . Initial identification of these stages commenced with VH backscatter highlighting soil tillage and planting, followed by subsequent growth stages.…”

Section: Identification Of Rice Cropping Calendarmentioning

confidence: 99%

Mapping rice paddy fields in semiarid northeastern Thailand using Sentinel-1 and -2 time series data and phenological clustering

Moukomla,

Lawawirojwong,

Deeudomchan

et al. 2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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Rice paddy fields in semiarid northeastern Thailand are a unique feature of traditional agriculture in the region, presenting unique agricultural practices and challenges, particularly in water management in semiarid conditions. This study aims to improve rice production efficiency and sustainability by understanding how these methods arose and how they could be enhanced to have an impact on agricultural practices in other regions with similar problems. This study developing a phenology-based method using the unsupervised classification of Sentinel-1 and 2 time-series data to identify rice paddy fields. Vegetation Index (VI) time series were used for identifying variations in the canopy of rice fields during growth stages. Monthly Sentinel-1 and 2 time series data between January 2020 and December 2022 were classified using k-means clustering to identify regions with similar phenological patterns. This methodology produced maps with a 10-meter resolution of rice field extent, intensity, and crop calendar. Validation was performed using the MS-700 Spectroradiometer and time-series image observed from the top of an automatic weather station. The results indicate that the proposed method based on phenology is cost-effective and capable of accurately mapping rice fields and growth stages across large areas. This study also highlights the importance of rice paddy fields in semiarid northeastern Thailand and how the developed methodology can help improve holistic water resources management and sustainability in the region and other similar areas.

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Section: Identification Of Rice Cropping Calendarmentioning

confidence: 99%

Mapping rice paddy fields in semiarid northeastern Thailand using Sentinel-1 and -2 time series data and phenological clustering

Moukomla,

Lawawirojwong,

Deeudomchan

et al. 2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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“…Vegetation Index (GCVI), Enhanced Vegetation Index (EVI), and others (Shahriar Pervez et al, 2014;Lu et al, 2021;Chen et al, 2018;Xiang et al, 2019;Dela Torre et al, 2021). The discrimination between irrigated and rain-fed croplands is typically accomplished through thresholding or decision tree classification, relying on the selected vegetation indices.…”

Section: Introductionmentioning

confidence: 99%

GMIE-100: a global maximum irrigation extent and irrigation type dataset derived through irrigation performance during drought stress and machine learning method

Tian,

Wu,

Zeng

et al. 2024

Preprint

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Abstract. Irrigation stands as the primary sector of human water consumption and plays a pivotal role in enhancing crop yields and mitigating drought effects. The precise distribution of irrigation is crucial for effective water resource management and the assessment of food security. However, the existing global irrigated cropland map is characterized by a coarse resolution, typically around 10 kilometers, and is often not regularly updated. In our study, we present a robust methodology that leverages irrigation performance during drought stress as an indicator of crop productivity and water consumption to identify global irrigated cropland. Within each irrigation mapping zone (IMZ), we identified the dry months occurring during the growing season from 2017 to 2019 or the driest months from 2010 to 2019. To delineate irrigated cropland, we utilized collected samples to calculate normalized difference vegetation index (NDVI) thresholds for the dry months of 2017 to 2019 and the NDVI deviation from the ten-year average for the driest month. By combining the results with the higher accuracy between these two methods, we generated the Global Maximum Irrigation Extent dataset at 100-meter resolution (GMIE-100), achieving an overall accuracy of 83.6 %. GMIE-100 reveals that the maximum extent of irrigated cropland encompasses 403.17 million hectares, accounting for 23.4 % of the global cropland. Concentrated in fertile plains and regions adjacent to major rivers, the largest irrigated cropland area is found in India, followed by China, the United States, and Pakistan, ranking 2nd to 4th, respectively. Importantly, the spatial resolution of GMIE-100, at 100 meters, surpasses that of the dominant irrigation map, offering more detailed information essential for supporting estimates of agricultural water use and regional food security assessments. Furthermore, with the help of the DL method, the global central pivot irrigation system (CPIS) was identified using Pivot-Net. We found that there are 11.5 million hectares of CPIS, accounting for about 2.9 % of total irrigated cropland. In Namibia, the US, Saudi Arabia, South Africa, Canada, and Zambia, the CPIS proportion was larger than 10 %. To our best knowledge, this is the first effort to identify irrigation methods globally. The GMIE-100 dataset containing both or irrigated extent and CPIS distribution is accessible on Harvard Dataverse at: https://doi.org/10.7910/DVN/HKBAQQ (Tian et al., 2023a).

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“…Essentially, machine learning modelling equations cannot be observed, limiting their ability to reliably map results for the whole study area (Ali et al, 2014; Jin et al, 2020; Lee et al, 2020). Recently, use of Google earth engine (GEE)‐assisted with the Tensorflow platform enables mapping of vegetation AGB (de la Torre et al, 2021; Gorelick et al, 2017; Hancher, 2017). As yet, such procedures have not been tested for machine learning regression models to map modelled AGB over a large area.…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of models to map alpine grassland above‐ground biomass using Google earth engine

Yan

Gao

Brierley

et al. 2023

Grass and Forage Science

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Accurate modelling and mapping of alpine grassland aboveground biomass (AGB) are crucial for pastoral agriculture planning and management on the Qinghai Tibet Plateau (QTP). This study assessed the effectiveness of four popular models (traditional multiple linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), and deep neural network (DNN)) with various input combinations (geospatial variables [GV], vegetation types [VT], field measurements [FM], meteorological variables [MV] and observation time [OT]) for AGB estimation based on a new framework for AGB modelling and mapping using Google Earth Engine. The results showed that the input feature of GV had a poor performance in AGB estimation (0.121 < R2 < 0.591). FM improved the accuracy the most when incorporated with GV (0.815 < R2 < 0.833). Although MV, VT and OT improved the accuracy (R2) only by 0.112–0.216 with an importance rank order of MV > VT > OT for machine learning models, their outputs could be used to map AGB. Grass AGB was less accurately predicted than shrub AGB, but the pooling of both VTs improved estimation accuracy (R2) by 0.171–0.269. The performance of the models followed the ranked order of DNN > ANN > SVM > MLR. DNN had the highest accuracy (R2 = 0.818) using all non‐field measured variables (excluding FM) as the inputs, and it was successfully applied to a new dataset (not associated with the data used in the training and testing) with a R2 of 0.676. This study presents an effective and operational framework for modelling and mapping grassland AGB. Accordingly, it provides the scientific foundations to determine of sustainable grazing carrying capacity in alpine grasslands.

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

Cited by 14 publications

References 60 publications

Mapping rice paddy fields in semiarid northeastern Thailand using Sentinel-1 and -2 time series data and phenological clustering

Mapping rice paddy fields in semiarid northeastern Thailand using Sentinel-1 and -2 time series data and phenological clustering

GMIE-100: a global maximum irrigation extent and irrigation type dataset derived through irrigation performance during drought stress and machine learning method

Improving the accuracy of models to map alpine grassland above‐ground biomass using Google earth engine

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