Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data

Nuarsa, I Wayan; Nishio, Fumihiko; Hongo, Chuki

doi:10.5539/jas.v3n1p54

Cited by 19 publications

(14 citation statements)

References 17 publications

(15 reference statements)

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“…Another study also found that NDWI usage lead to a negligible increase in commission errors over studies that use LSWI, but that NDWI was also more sensitive to the presence of water in mixed land cover conditions typical of moderate spatial resolution analyses (Boschetti et al, 2014) which was consistent with . Besides these existing VIs, one study also proposed a Rice Growth Vegetation Index (RGVI) and found it to have a good relationship with rice age and can be used for rice plant mapping (Nuarsa et al, 2011).…”

Section: Major Characteristics Of Evolution Of Paddy Rice Mapping Metmentioning

confidence: 99%

Evolution of regional to global paddy rice mapping methods: A review

Dong

Xiao

2016

ISPRS Journal of Photogrammetry and Remote Sensing

196

127

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a b s t r a c tPaddy rice agriculture plays an important role in various environmental issues including food security, water use, climate change, and disease transmission. However, regional and global paddy rice maps are surprisingly scarce and sporadic despite numerous efforts in paddy rice mapping algorithms and applications. With the increasing need for regional to global paddy rice maps, this paper reviewed the existing paddy rice mapping methods from the literatures ranging from the 1980s to 2015. In particular, we illustrated the evolution of these paddy rice mapping efforts, looking specifically at the future trajectory of paddy rice mapping methodologies. The biophysical features and growth phases of paddy rice were analyzed first, and feature selections for paddy rice mapping were analyzed from spectral, polarimetric, temporal, spatial, and textural aspects. We sorted out paddy rice mapping algorithms into four categories: (1) Reflectance data and image statistic-based approaches, (2) vegetation index (VI) data and enhanced image statistic-based approaches, (3) VI or RADAR backscatter-based temporal analysis approaches, and (4) phenology-based approaches through remote sensing recognition of key growth phases. The phenology-based approaches using unique features of paddy rice (e.g., transplanting) for mapping have been increasingly used in paddy rice mapping. Current applications of these phenologybased approaches generally use coarse resolution MODIS data, which involves mixed pixel issues in Asia where smallholders comprise the majority of paddy rice agriculture. The free release of Landsat archive data and the launch of Landsat 8 and Sentinel-2 are providing unprecedented opportunities to map paddy rice in fragmented landscapes with higher spatial resolution. Based on the literature review, we discussed a series of issues for large scale operational paddy rice mapping. Ó

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Section: Major Characteristics Of Evolution Of Paddy Rice Mapping Metmentioning

confidence: 99%

Evolution of regional to global paddy rice mapping methods: A review

Dong

Xiao

2016

ISPRS Journal of Photogrammetry and Remote Sensing

196

127

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“…This particular issue would be critical as the dimension of some rice fields might be smaller than those of the employed spatial resolution. In this context, the use of Landsat imagery would compensate the spatial resolution [19]; however, it would be very difficult to obtain cloud-free chronology of such images over the entire growing season. In this paper, our overall objective was to develop a methodology for mapping boro rice (i.e., cultivated during the months January to May) using MODIS-derived NDVI values at 250 m spatial resolution and its implementation over Bangladesh during the 2007-2012 period.…”

Section: Introductionmentioning

confidence: 99%

“…In most of the abovementioned studies (see Table 1 [4,[13][14][15][16][17][18][19][20]), the spatial resolution of the input images (i.e., MODIS, AVHRR, and SPOT-VGT) were having relatively low (i.e., in the range 500 m to 1.1 km) [4,[13][14][15][16][17][18]. This particular issue would be critical as the dimension of some rice fields might be smaller than those of the employed spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Development of a Remote Sensing-Based “Boro” Rice Mapping System

Mosleh

Hassan

2014

Remote Sensing

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Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping -boro‖ rice (i.e., cultivated during the months January to May) in a Bangladeshi context. In this paper, we used a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250 m spatial resolution in conjunction with ancillary datasets (i.e., land use map, crop calendar, and ground-based rice production information) during the period 2007-2012. The proposed method consisted of three procedures: (i) ISODATA clustering and determining the boro rice signatures in temporal dimension using data from the period 2007-2009; (ii) formulating a mathematical model for extracting the boro rice areas using data from the period 2007-2009; and (iii) model calibration using data from the period 2007-2009 and its validation using data from the period 2010-2012. The implementation of the abovementioned procedures revealed reasonable agreements between the model (i.e., MODIS-based) and ground-based estimates of boro rice area at both country (i.e., percentage error in the range −0.83-1.42%) and district levels (i.e., r 2 in the range 0.69-0.89) during the period 2010-2012. Our proposed method demonstrated its effectiveness in mapping rice system at the regional/country scale.

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“…According to this limitation of medium resolution imagery, some research is conducted by using Landsat ETM + (Uchida, 2010, Nuarsa et al, 2011. This image with a resolution of 30 m could be used for small scale agricultural land parcel.…”

Section: Introductionmentioning

confidence: 99%

“…In the study was conducted by Nuarsa et al (2011) the spectral identification process was carried out on Ciherang varieties. As in Indonesia, the type of rice planted is vary widely.…”

Section: Introductionmentioning

confidence: 99%

Spectral Characteristics of High-Yielding Varieties of Rice Plants Using Landsat 8 Data

Dewi¹,

Supianto²,

Sutrisno³

2013

Agrivita.J.Agr.Sci

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In applying remote sensing technology for inventory, evaluation and estimation of rice crop production, required the data of spectral characteristics changing of the plants during its growth phase. By identifying spectral characteristics, it will be recognized the objects in the images. This study identifies the spectral characteristics of high-yielding varieties of rice plants during their growth in Malang regency. Based on the results of field survey, the high yielding varieties that are commonly planted consist of IR64, Ciherang and Membramo. Then, from the identification of vegetation index is known thet al.,l these three varieties have different growth patterns, where the most distinct pattern found in IR64.

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Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data

Cited by 19 publications

References 17 publications

Evolution of regional to global paddy rice mapping methods: A review

Evolution of regional to global paddy rice mapping methods: A review

Development of a Remote Sensing-Based “Boro” Rice Mapping System

Spectral Characteristics of High-Yielding Varieties of Rice Plants Using Landsat 8 Data

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