Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and  Land Based Observations

Ajith, K. K.; Geethalakshmi, V.; Ragunath, K.P.; Pazhanivelan, S.; Dheebakaran, Ga.

doi:10.20546/ijcmas.2017.612.263

Cited by 11 publications

(4 citation statements)

References 14 publications

(15 reference statements)

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“…It might occur because paddy plants have grown with high density thus it raised NDVI value. is notion was consistent with a nding of Ajith et al (2017) who found the NDVI value in paddy elds during growing season was around 0,81. For harvest period, sparse vegetation class (NDVI 0,2-0,4) was a predominant in study area.…”

Section: Normalized Difference Vegetation Index (Ndvi) Classi Cationsupporting

confidence: 91%

Potential of Normalized Difference Vegetation Index for Mapping of Soft Clay Area in Paddy Fields of Kedah, Malaysia

Rendana

Idris

Rahim

et al. 2020

IJG

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Mapping of soft clay area in paddy fields uses remote sensing and GIS technique is the fastest way to obtain an accurate location of soft clay in a large scale area. It can be an alternative way to change conventional method like in-situ observation that is expensive and labor intensive. Therefore, this study aimed to investigate the normalized difference vegetation index (NDVI) to map soft clay area in paddy fields Kedah, Malaysia. To analyze soft clay area comprehensively, the study was carried out in three different periods; before paddy planting, after paddy planting and harvest. Ground-truth data of soft clay area was collected from study area during fieldwork activity and compared with NDVI values that produced from Landsat 8 image. Result of study showed NDVI map in period of before paddy planting could be a good indicator for mapping soft clay area because it gave a higher accuracy value than the other periods, with overall accuracy (85%) and kappa coefficient (0,84). Total area of soft clay from the highest value was showed in period of before paddy planting (1.856,97 ha), followed by after paddy planting (656,73 ha) and harvest (401,85 ha) periods, respectively.

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Section: Normalized Difference Vegetation Index (Ndvi) Classi Cationsupporting

confidence: 91%

Potential of Normalized Difference Vegetation Index for Mapping of Soft Clay Area in Paddy Fields of Kedah, Malaysia

Rendana

Idris

Rahim

et al. 2020

IJG

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“…Although rather high, these numbers are in accordance with other crop biomass/yield estimations that used remote sensing and more complex approaches or models. Ajith et al (2017) [64] used an integrated NDVI for rice and found error percentages ranging from 11 to 21%. Benedetti and Rossini (1993) [65] found an error from 10 to 19% using cumulative NDVI profiles in a regression model for wheat, and in a similar approach, Doraiswamy and Cook (1995) [59] found much higher and variable errors at the county/regional levels in two regions of the USA.…”

Section: Discussionmentioning

confidence: 99%

The Second Derivative of the NDVI Time Series as an Estimator of Fresh Biomass: A Case Study of Eight Forage Associations Monitored via UAS

Sánchez

Plaza

Criado

et al. 2023

Drones

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The estimation of crop yield is a compelling and highly relevant task in the scenario of the challenging climate change we are facing. With this aim, a reinterpretation and a simplification of the Food and Agriculture Organization (FAO) fundamentals are presented to calculate the fresh biomass of forage crops. A normalized difference vegetation index (NDVI) series observed from a multispectral camera on board an unmanned aircraft system (UAS) was the basis for the estimation. Eight fields in Spain of different rainfed intercropping forages were flown over simultaneously, with eight field measurements from February to June 2020. The second derivative applied to the NDVI time series determined the key points of the growing cycle, whereas the NDVI values themselves were integrated and multiplied by a standardized value of the normalized water productivity (WP*). The scalability of the method was tested using two scales of the NDVI values: the point scale (at the precise field measurement location) and the plot scale (mean of 400 m2). The resulting fresh biomass and, therefore, the proposal were validated against a dataset of field-observed benchmarks during the field campaign. The agreement between the estimated and the observed fresh biomass afforded a very good prediction in terms of the determination coefficient (R2, that ranged from 0.17 to 0.85) and the agreement index (AI, that ranged from 0.55 to 0.90), with acceptable estimation errors between 10 and 30%. The best period to estimate fresh biomass was found to be between the second fortnight of April and the first fortnight of May.

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“…On the other hand, in the planting phase determination, the plant's age must be calculated using the obtained equation. Result research Ajith et al, (2017), shows that the best form of relationship between NDVI and rice age uses a quadratic equation. Estimation of rice growth phase in Harau District based on NDVI value is presented in Equation ( 4):…”

Section: Nsementioning

confidence: 99%

Rice Productivity Estimation Using Remote Sensing Method

Yanti

Safitri²,

Rusnam³

et al. 2022

JTEP-L

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The calculation of crop productivity has now been facilitated by technological development using remote sensing technology or data generated by satellites. Determining the value of productivity using images will shorten the time and does not require much effort. A remote sensing model that connects satellite image reflectance data with rice plant parameters will be handy for monitoring biomass growth and predicting crop yields more quickly and efficiently. This study aimed to determine the regression equation to estimate the productivity of regional rice in Harau District, Lima Puluh Kota Regency. This study consisted of several stages: data collection, data processing, and calculation of rice productivity, NDVI regression analysis with rice productivity, and Nash-Sutcliffe Efficiency (NSE) test against the obtained equations. The regression equation obtained from the results of data analysis to estimate rice productivity in Harau District, Lima Puluh Kota Regency is y = - 82152x4 + 208465x3 - 197654x2 + 82986x - 13014, with an NSE value of 0.64 which is categorized as ‘sufficient’.Keywords: MODIS, NDVI, Rice, Remote sensing; Productivity

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Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

Cited by 11 publications

References 14 publications

Potential of Normalized Difference Vegetation Index for Mapping of Soft Clay Area in Paddy Fields of Kedah, Malaysia

Potential of Normalized Difference Vegetation Index for Mapping of Soft Clay Area in Paddy Fields of Kedah, Malaysia

The Second Derivative of the NDVI Time Series as an Estimator of Fresh Biomass: A Case Study of Eight Forage Associations Monitored via UAS

Rice Productivity Estimation Using Remote Sensing Method

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