Mapping Irrigated Areas of Northeast China in Comparison to Natural Vegetation

Xiang, Kunlun; Ma, Mengqing; Liu, Wei; Dong, Jie; Zhu, Xiufang; Yuan, Wenping

doi:10.3390/rs11070825

Cited by 26 publications

(17 citation statements)

References 47 publications

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“…Satellite remote sensing has proven its high capability and effectiveness for mapping and monitoring irrigated areas [8][9][10]. Recent studies have shown that irrigated areas could be spatially quantified over large scale using either passive optical sensors [11][12][13] or active radar sensors [14][15][16]. Optical images has been widely used to map irrigated areas using the difference between the spectral signature of irrigated crops and that of non-irrigated crops in the time series domain.…”

Section: Introductionmentioning

confidence: 99%

Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data

et al. 2020

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Better management of water consumption and irrigation schedule in irrigated agriculture is essential in order to save water resources, especially at regional scales and under changing climatic conditions. In the context of water management, the aim of this study is to monitor irrigation activities by detecting the irrigation episodes at plot scale using the Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) time series over intensively irrigated grassland plots located in the Crau plain of southeast France. The method consisted of assessing the newly developed irrigation detection model (IDM) at plot scale over the irrigated grassland plots. First, four S1-SAR time series acquired from four different S1-SAR acquisitions (different S1 orbits), each at six-day revisit time, were obtained over the study site. Next, the IDM was applied at each available SAR image from each S1-SAR series to obtain an irrigation indicator at each SAR image (no, low, medium, or high irrigation possibility). Then, the irrigation indicators obtained at each image from each S1-SAR time series (four series) were added and combined by threshold value criteria to determine the existence or absence of an irrigation event. Finally, the performance of the IDM for irrigation detection was assessed by comparing the in situ recorded irrigation events at each plot and the detected irrigation events. The results show that using only the VV polarization, 82.4% of the in situ registered irrigation events are correctly detected with an F_score value reaching 73.8%. Less accuracy is obtained using only the VH polarization, where 79.9% of the in situ irrigation events are correctly detected with an F_score of 72.2%. The combined use of the VV and VH polarization showed that 74.1% of the irrigation events are detected with a higher F_score value of 76.4%. The analysis of the undetected irrigation events revealed that, in the presence of very well-developed vegetation cover (normalized difference of vegetation index (NDVI) ≥ 0.8); higher uncertainty in irrigation detection is observed, where 80% of the undetected events correspond to an NDVI value greater than 0.8. The results also showed that small-sized plots encounter more false irrigation detections than large-sized plots certainly because the pixel spacing of S1 data (10 m × 10 m) is not adapted to small size plots. The obtained results prove the efficiency of the S1 C-band data and the IDM for detecting irrigation events at the plot scale, which would help in improving the irrigation water management at large scales especially with availability and global coverage of the S1 product.

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Section: Introductionmentioning

confidence: 99%

Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data

et al. 2020

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“…The overall accuracy for detecting irrigation water supplements reached 87%. Recently, Xiang et al [19] mapped irrigated areas of northeast China by comparing the MODIS derived LSWI (Land Surface Water Index) of the agricultural areas to the surrounding natural vegetation such as forests. They assumed that the canopy moisture indicated by the LSWI is higher for irrigated crops than the adjacent forest.…”

Section: Introductionmentioning

confidence: 99%

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

et al. 2019

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Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

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“…The lack of accurate ground-truth measurements and high spatio-temporal EO data prevent the researchers from examining environmental objectives, such us the irrigation activities, the organic farming, the soil properties and the nitrogen balance in terms of land degradation risk, or the assessment of the greenhouse gas (GHG) and ammonia (NH3) emissions. Exceptions are the studies of Paredes-Gómez et al 16 , Xiang et al 25 and Alexandridis et al 26 , who tried to provide accurate information about the location and the extent of irrigated areas, leveraging both satellite-based multispectral data (e.g. ASTER, SPOT-4, Landsat 7 ETM+, MODIS, and Sentinel-2A & -2B) and other ancillary data, such as cumulative precipitation measurements, and digital elevation models (DEM).…”

Section: Earth Observation Contribution In the Modernized Cap Objectivesmentioning

confidence: 99%

An integrated service-based solution addressing the modernised common agriculture policy regulations and environmental perspectives

Karagiannopoulou¹,

Tsiakos²,

Tsimiklis³

et al. 2020

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII

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The EU-funded DIONE project (grant agreement No. 870378) offers an innovative close-to-market (TRL7) solution seeking to improve the traditional methods of agricultural monitoring. The project introduces a cloud-based Software as a Service (SaaS) system architecture, building on a fusion of novel technologies that will support the forthcoming needs of the modernized Common Agriculture Policy (CAP) and the "Greening" perspectives, with an automated area-based monitoring system. In particular, an interoperable and harmonized system is designed, connecting large volumes of Earth Observation data (Satellite, UAV, and in-situ) and user-generated highly precise geolocated data (geo-tagged photos, soil measurements, etc.). DIONE's system architecture encompasses customized and third-party frameworks, where heterogeneous and multi-source data are stored, processed and managed using Artificial Intelligence (AI) algorithms. These harmonized, curated and open accessed data are then provided as Open Geospatial Consortium (OGC)-compliant, web-service layers (WMS, WFS, and WCS). Furthermore, the proposed solution formulates a scalable, flexible, interoperable, and semantically enriched environment, taking advantage of a Spatial Data Infrastructure (SDI) framework capabilities, whilst allowing an interactive connection among different tools and components through RESTful APIs. Our approach establishes a novel, cloud-based, accurate and inexpensive agriculture monitoring solution, enabling the real-time provision of multi-source data to relevant stakeholders such as Paying Agencies, Policy Officers and Control & Certification Bodies, and other domain experts. The system architecture was formulated exploiting a codesign methodology, aiming to ensure a long-term and sustainable solution. Two large scale demonstration will take place in Lithuania and Cyprus, evaluating the system capabilities in real-life and operational conditions.

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Mapping Irrigated Areas of Northeast China in Comparison to Natural Vegetation

Cited by 26 publications

References 47 publications

Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data

Irrigation Events Detection over Intensively Irrigated Grassland Plots Using Sentinel-1 Data

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

An integrated service-based solution addressing the modernised common agriculture policy regulations and environmental perspectives

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