MODIFICATION OF INPUT IMAGES FOR IMPROVING THE ACCURACY OF RICE FIELD CLASSIFICATION USING MODIS DATA

The standard image classification method typically uses multispectral imageryon one acquisition date as an input for classification. Rice fields exhibit high variability inland cover states, which influences their reflectance. Using the existing standard method forrice field classification may increase errors of commission and omission, thereby reducingclassification accuracy. This study utilised temporal variance in a vegetation index as amodified input image for rice field classification. The results showed that classification ofrice fields using modified input images provided a better result. Using the modifiedclassification input improved the correspondence between rice field area obtained from theclassification result and reference data (R2 increased from 0.2557 to 0.9656 for regencylevelcomparisons and from 0.5045 to 0.8698 for district-level comparisons). Theclassification accuracy and the estimated Kappa value also increased when using themodified classification input compared to the standard method, from 66.33 to 83.73 andfrom 0.49 to 0.77, respectively. The commission error, omission error, and Kappa variancedecreased from 68.11 to 42.36, 28.48 to 27.97, and 0.00159 to 0.00039, respectively, whenusing modified input images compared to the standard method. The Kappa analysisconcluded that there are significant differences between the procedure developed in thisstudy and the standard method for rice field classification. Consequently, the modifiedclassification method developed here is significant improvement over the standardprocedure

Application of RGB UAV images to identify spectral patterns and estimate rice production

Monitoring rice plant growth is crucial for evaluating rice field management and yield production. RGB images are generated from Unmanned Aerial Vehicles (UAV) with RGB cameras. UAVs produce high spatial and temporal resolution, while RGB cameras are commonly used and cheap. The objectives of this study were to identify the spectral pattern of rice plant growth and to estimate yield production based on the spectral value of RGB images. The spectral pattern and yield estimation were analyzed using confidence interval (CI) and regression, respectively. Results show that spectral pattern during the vegetative until ripening stage forms a concave with minimum value in the generative stage and decreases towards the harvest stage. Based on the CI value, the high interval between upper and lower happened in the vegetative and ripening stages while the low interval happened in the generative stage. The high CI in the vegetative and ripening stages was due to the soil background and complexity of the rice plant canopy, respectively while the low CI in the generative stage was due to the homogeneous response of the leaf canopy. The best rice yield estimation based on the spectral value occurs in the ripening stage with an R2 of 0.84. Keywords: chlorophyll content, confidence interval, drone images, rice plant, regressio

IDENTIFICATION OF AGE CLASS AND VARIETIES OF RICE PLANT USING SPECTRORADIOMETRY AND CHLOROPHYLL CONTENT INDEX: (Identifikasi Kelas Umur dan Varietas Tanaman Padi Menggunakan Spektroradiometri dan Indeks Kandungan Klorofil)

Rice is the staple food for Indonesian society because more than 90% population eat rice every day. Estimation of the rice production can be monitored from the plant growth phase by utilizing remote sensing data. Spectroradiometry can be used to validate the remote sensing spectral because it has a wide wavelength range. Research objectives are to identify transplanting age class and varieties of rice plant based on spectroradiometry and its vegetation index, to analyze the relationship between spectroradiometry and chlorophyll content index (CCI). The results show that the transplanting date of 14 days, 21-32 days, and 56-68 days in three varieties (Inpari32; Padjadjaran Agritan; Siliwangi Agritan) are difficult to be distinguished at visible wavelength but it easy at infrared wavelength. The plant age class for the Siliwangi Agritan can be distinguished well on NDVI, SAVI, EVI while the Pajajaran Agritan is only on NDVI and EVI. All vegetation indexes, where the plant age of 14 days and 21-32 days for the Inpari32 are difficult to be distinguished between them, but easy to be distinguished with 56-68 days. This is due to the high sensitivity of chlorophyll to infrared wavelengths and the characteristics of rice plants itself (many tillers and plant height). Meanwhile, rice plants of every veriety are difficult to be distinguished, either on visible wavelength, infrared wavelength or on all vegetation indexes. Spectroradiometry has a high correlation with chlorophyll content index (CCI) (R2=0,88). This shows that the higher chlorophyll content in rice plants, the higher spectroradiometry for infrared wavelength.&nbsp

Rice Productivity Growth During Nine Years in Badung Regency, Bali Province

The trend of rice productivity and its stability in Badung Regency are presented in this study. The area is a tourism-based economy without leaving the role of rice production to feed the people.  Time series data were collected from five sub districts covering 53 villages during 2008-2016 due to the completeness of the data. Variability of the data was observed from the coefficient of variance (C.V.) to decide rice productivity stability. This study also observed correlation between rainfall and rice productivity in the area. Result of the study shows that rice productivity trend in Badung Regency tend to decline during nine years of observation especially in 2014-2016. Three sub-districts had stable condition, while two sub-districts in tourism area contributed to the decline of this matter. Analysis using bi-plot revealed that there is no significant correlation between rainfall in sub-district and rice productivity, implying that water is available throughout the year.  In terms of stability, majority of villages (69.81%) had stable condition of rice productivity ranging from middle and high category. Other 30.19% villages were categorized as unstable ranging from low to high productivity. This condition showed that Badung Regency were able to maintain stability of rice productivity during nine years of observation. Implication of this study is to pay more attention to two sub districts in tourism area to improve their rice productivity such as implementation of good agricultural practices

MODIFICATION OF INPUT IMAGES FOR IMPROVING THE ACCURACY OF RICE FIELD CLASSIFICATION USING MODIS DATA

The standard image classification method typically uses multispectral imageryon one acquisition date as an input for classification. Rice fields exhibit high variability inland cover states, which influences their reflectance. Using the existing standard method forrice field classification may increase errors of commission and omission, thereby reducingclassification accuracy. This study utilised temporal variance in a vegetation index as amodified input image for rice field classification. The results showed that classification ofrice fields using modified input images provided a better result. Using the modifiedclassification input improved the correspondence between rice field area obtained from theclassification result and reference data (R2 increased from 0.2557 to 0.9656 for regencylevelcomparisons and from 0.5045 to 0.8698 for district-level comparisons). Theclassification accuracy and the estimated Kappa value also increased when using themodified classification input compared to the standard method, from 66.33 to 83.73 andfrom 0.49 to 0.77, respectively. The commission error, omission error, and Kappa variancedecreased from 68.11 to 42.36, 28.48 to 27.97, and 0.00159 to 0.00039, respectively, whenusing modified input images compared to the standard method. The Kappa analysisconcluded that there are significant differences between the procedure developed in thisstudy and the standard method for rice field classification. Consequently, the modifiedclassification method developed here is significant improvement over the standardprocedure

Transplanting Date Estimation Using Sentinel-1 Satellite Data for Paddy Rice Damage Assessment in Indonesia

In Indonesia, there is a need to improve the efficiency of damage assessments of the agricultural insurance system for paddy rice producers affected by floods, droughts, pests, and diseases. In this study, we develop a method to estimate the transplanting date required for damage assessments of paddy rice fields. The study area is the Cihea irrigation district in West Java, Republic of Indonesia. Backscattering coefficients of VH polarization measured by a synthetic aperture radar onboard the Sentinel-1 satellite were used for the estimations. We investigated the accuracy of the estimations of the proposed method by smoothing out the time-series data, applying a speckle filter, and by signal synthesis of the surrounding fields. It was found that these variations effectively improved the estimation accuracy. To further improve the estimation accuracy, the data for all incident angles were used after correcting the incident angle dependence of the backscattering coefficients for three types of data with different incident angles (32°, 41°, and 45°) obtained in the study area. The estimated transplanting date for each field in the test site was compared with the transplanting date obtained through interviews. The standard deviations of the estimation errors for the four cropping periods from March 2018 to February 2020 were found to be ~5–6 days, and the percentages of estimation errors in transplanting dates within 5, 10, and 15 days were estimated to be 69%, 92%, and 97%, respectively. It was confirmed that a sufficiently reliable transplanting date estimation can be obtained ~10–15 days after transplantation