Influence of Resolution in Irrigated Area Mapping and Area Estimation

Velpuri, Naga Manohar; Thenkabail, Prasad S.; Gumma, Murali Krishna; Biradar, Chandrashekhar; Dheeravath, Venkateswarlu; Noojipady, Praveen; Yuanjie, Li

doi:10.14358/pers.75.12.1383

Cited by 82 publications

(77 citation statements)

References 23 publications

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“…However, these metrics are always constrained by the high demand of simultaneity about high spatial and temporal resolution of remote sensing images which is difficult to be reached currently. Image fusion [23][24][25] and mixed pixel decomposition [9,20,26] are other ways to solve this problem, but the inconsistency of multi-source image data and the reliability of mixed pixel decomposition model would have impacts on crop identification.…”

Section: Introductionmentioning

confidence: 99%

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

et al. 2018

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Accurate data about the spatial distribution and planting area of maize is important for policy making, economic development, environmental protection and food security under climate change. This paper proposes a new identification method for spring maize based on spectral and phenological features derived from the moderate resolution imaging spectroradiometer (MODIS) land surface reflectance time-series data. The method focused on the spectral differences of different land cover types in the specific phenological phases of spring maize by testing the selections and combinations of classification metrics, feature extraction methods and classifiers. Taking Liaoning province, a representative planting region of spring maize in Northeast China, as the study area, the results indicated that the combined multiple metrics, including the red reflectance, near-infrared reflectance and normalized difference vegetation index (NDVI), were conducive to the maize identification and were better than any single metric. With regard to the feature extraction and selection, maize identification based on different phenological features selected with prior knowledge was more efficient than that based on statistical features derived from the principal component analysis. Compared with the maximum likelihood classification method, the decision tree classification based on expert knowledge was more suitable for phenological features selected from some prior knowledge. In summary, discriminant rules were defined with those phenological features from multiple metrics, and the decision tree classification was used to identify maize in the study area. The producer's accuracy of maize identification was 98.57%, and the user's accuracy was 81.18%. This method can be potentially applied to an operational identification of maize at large scales based on remote sensing time-series data.

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Section: Introductionmentioning

confidence: 99%

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

et al. 2018

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“…Several studies have been conducted on how to map agricultural areas [7][8][9][10][11][12] using advanced techniques in satellite image analysis. However, mapping of irrigated areas proved to be a challenge due to the diverse range of irrigated plot sizes, crops and water sources used by farmers [13,14].…”

Section: Introductionmentioning

confidence: 99%

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

et al. 2011

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Maps of irrigated areas are essential for Ghana's agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m OPEN ACCESSRemote Sens. 2011, 3 817 time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20-57% higher than irrigated areas reported by Ghana's Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs.

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“…Identifying small or fragmented irrigated areas on a large scale remains complicated due to the spatial resolution of the imagery available for operational applications (i.e., free data with high repeatability over large areas cannot always be obtained) [110,121]. In these areas, the use of metric or sub-metric spatial resolution data coupled with an OBIA approach can better identify cultivated fields using segmentation.…”

Section: Irrigationmentioning

confidence: 99%

“…In these areas, the use of metric or sub-metric spatial resolution data coupled with an OBIA approach can better identify cultivated fields using segmentation. Velpuri et al [121] studied the influence of spatial resolution in irrigated area mapping and estimation. Due to their ability to detect small and fragmented irrigated areas, high spatial resolution imagery (Landsat 30 m in this study) provided more precise location and area estimation of irrigated areas (84%) compared to MODIS (250 m: 79%; 500 m: 77%) or AVHHR (1 km: 63%).…”

Section: Irrigationmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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Influence of Resolution in Irrigated Area Mapping and Area Estimation

Cited by 82 publications

References 23 publications

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

Mapping Irrigated Areas of Ghana Using Fusion of 30 m and 250 m Resolution Remote-Sensing Data

Remote Sensing and Cropping Practices: A Review

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