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Climate change and its variability are some of the most critical threats to sustainable agriculture, with potentially severe consequences on Indonesia’s agriculture, such as changes in rainfall patterns, especially the onset of the wet season and the temporal distribution of rainfall. Most Indonesian farmers receive support from agricultural extension services, and therefore, design their agricultural calendar based on personal experience without considering global climate phenomena, such as La Niña and El Niño, which difficult to interpret on a local scale. This paper describes the Integrated Cropping Calendar Information System (ICCIS) as a mechanism for adapting to climate variability. The ICCIS contains recommendations on planting time, cropping pattern, planting area, varieties, fertilizers, agricultural machinery, potential livestock feed, and crop damage due to climate extremes for rice, maize, and soybean. To accelerate the dissemination of information, the ICCIS is presented in an integrated web-based information system. The ICCIS is disseminated to extension workers and farmers by Task Force of the Assessment Institute for Agricultural Technology (AIAT) located in each province. Based on the survey results, it is known that the ICCIS adoption rate is moderate to high. The AIAT must actively encourage and support the ICCIS Task Force team in each province. Concerning the technological recommendations, it is necessary to update the recommendations for varieties, fertilizer, and feed to be more compatible with local conditions. More accurate information and more intensive dissemination can enrich farmers’ knowledge, allowing for a better understanding of climate hazards and maintaining agricultural production.
Yayan Apriyana; Elza Surmaini; Woro Estiningtyas; Aris Pramudia; Fadhlullah Ramadhani; Suciantini Suciantini; Erni Susanti; Rima Purnamayani; Haris Syahbuddin. The Integrated Cropping Calendar Information System: A Coping Mechanism to Climate Variability for Sustainable Agriculture in Indonesia. Sustainability 2021, 13, 6495 .
AMA StyleYayan Apriyana, Elza Surmaini, Woro Estiningtyas, Aris Pramudia, Fadhlullah Ramadhani, Suciantini Suciantini, Erni Susanti, Rima Purnamayani, Haris Syahbuddin. The Integrated Cropping Calendar Information System: A Coping Mechanism to Climate Variability for Sustainable Agriculture in Indonesia. Sustainability. 2021; 13 (11):6495.
Chicago/Turabian StyleYayan Apriyana; Elza Surmaini; Woro Estiningtyas; Aris Pramudia; Fadhlullah Ramadhani; Suciantini Suciantini; Erni Susanti; Rima Purnamayani; Haris Syahbuddin. 2021. "The Integrated Cropping Calendar Information System: A Coping Mechanism to Climate Variability for Sustainable Agriculture in Indonesia." Sustainability 13, no. 11: 6495.
Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to produce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.
Fadhlullah Ramadhani; Reddy Pullanagari; Gabor Kereszturi; Jonathan Procter. Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics. Remote Sensing 2021, 13, 1498 .
AMA StyleFadhlullah Ramadhani, Reddy Pullanagari, Gabor Kereszturi, Jonathan Procter. Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics. Remote Sensing. 2021; 13 (8):1498.
Chicago/Turabian StyleFadhlullah Ramadhani; Reddy Pullanagari; Gabor Kereszturi; Jonathan Procter. 2021. "Mapping a Cloud-Free Rice Growth Stages Using the Integration of PROBA-V and Sentinel-1 and Its Temporal Correlation with Sub-District Statistics." Remote Sensing 13, no. 8: 1498.
Sustainability of rice production is a critical issue to ensure food security and needs to be monitored on time. Therefore, the online monitoring system using Sentinel-2 has been introduced to monitor rice fields in Indonesia. However, the system needs to be coupled with precipitation to improve user usage. In this study, the spatial correlation using Pearson's correlation analysis and linear regression between the floods and the vegetative stages area of the rice monitoring maps and the precipitation data from CHIRPS was investigated. The analysis was conducted with the two datasets with 439 regencies in Indonesia on a monthly basis from December 2019 until May 2020. The result shows that 96 regencies have a highly positive correlation (r>0.6, p>0.05, n=6) with 57 regencies have a high R2 (R2>0.6). Also, there are 83 regencies has a profoundly negative correlation (r≤-0.6, p0.6), On the other hand, there are 79 regencies have medium R2 (0.4≤R2<0.6), and 271 regencies have the lowest R2 value (R2≤0.4). These early-stage results show an opportunity to combine two datasets to produce early warning systems or recommended cropping calendar in a timely and accurate manner to the stakeholders or the farmers.
F Ramadhani; Misnawati; H Syahbuddin. An early investigation of spatial correlation between Sentinel-2 based rice growth stages maps with satellite-based precipitation data to support digital agriculture development in Indonesia. IOP Conference Series: Earth and Environmental Science 2021, 648, 012002 .
AMA StyleF Ramadhani, Misnawati, H Syahbuddin. An early investigation of spatial correlation between Sentinel-2 based rice growth stages maps with satellite-based precipitation data to support digital agriculture development in Indonesia. IOP Conference Series: Earth and Environmental Science. 2021; 648 (1):012002.
Chicago/Turabian StyleF Ramadhani; Misnawati; H Syahbuddin. 2021. "An early investigation of spatial correlation between Sentinel-2 based rice growth stages maps with satellite-based precipitation data to support digital agriculture development in Indonesia." IOP Conference Series: Earth and Environmental Science 648, no. 1: 012002.
The rice monitoring based on Sentinel-2 (SC-S2) has been developed for over nine months. It has been observed as the first and only system which generate rice growth stages maps in 10 m spatial resolution using machine learning in Indonesia. However, the SC-S2 use Support Vector Machine to separate the rice growth stages, which may have poor performances. The objective of this study is to investigate the performance of other classifiers to increase the performance of SC-S2. We used survey data from the field campaign in 2018 and synchronized with Sentinel-2 bands. The model dataset was trained using 61 machine learning algorithms to create 61 rice growth stages models. The models were applied to the Sentinel-2 image of part of Indramayu area. The accuracy, computational time and visual inspection score were collected, and the final score was calculated. The results are the highest final score is Shrinkage Discriminant Analysis, with overall accuracy 88.1% (p<0.001) and the average accuracy of all classifiers is 76.2% (p<0.05). The implication of this study is to propose some changes in the classification process into the SC-S2 for increasing the overall performance, which will provide better information for agricultural policymakers.
F Ramadhani; M R S Koswara; Y Apriyana; Harmanto. The comparison of numerous machine learning algorithms performance in classifying rice growth stages based on Sentinel-2 to enhance crop monitoring in national level. IOP Conference Series: Earth and Environmental Science 2021, 648, 012212 .
AMA StyleF Ramadhani, M R S Koswara, Y Apriyana, Harmanto. The comparison of numerous machine learning algorithms performance in classifying rice growth stages based on Sentinel-2 to enhance crop monitoring in national level. IOP Conference Series: Earth and Environmental Science. 2021; 648 (1):012212.
Chicago/Turabian StyleF Ramadhani; M R S Koswara; Y Apriyana; Harmanto. 2021. "The comparison of numerous machine learning algorithms performance in classifying rice growth stages based on Sentinel-2 to enhance crop monitoring in national level." IOP Conference Series: Earth and Environmental Science 648, no. 1: 012212.
Rice (Oryza sativa L.) is a staple food crop for more than half of the world’s population. Rice production is facing a myriad of problems, including water shortage, climate, and land-use change. Accurate maps of rice growth stages are critical for monitoring rice production and assessing its impacts on national and global food security. Rice growth stages are typically monitored by coarse-resolution satellite imagery. However, it is difficult to accurately map due to the occurrence of mixed pixels in fragmented and patchy rice fields, as well as cloud cover, particularly in tropical countries. To solve these problems, we developed an automated mapping workflow to produce near real-time multi-temporal maps of rice growth stages at a 10-m spatial resolution using multisource remote sensing data (Sentinel-2, MOD13Q1, and Sentinel-1). This study was investigated between 1 June and 29 September 2018 in two (wet and dry) areas of Java Island in Indonesia. First, we built prediction models based on Sentinel-2, and fusion of MOD13Q1/Sentinel-1 using the ground truth information. Second, we applied the prediction models on all images in area and time and separation between the non-rice planting class and rice planting class over the cropping pattern. Moreover, the model’s consistency on the multitemporal map with a 5–30-day lag was investigated. The result indicates that the Sentinel-2 based model classification gives a high overall accuracy of 90.6% and the fusion model MOD13Q1/Sentinel-1 shows 78.3%. The performance of multitemporal maps was consistent between time lags with an accuracy of 83.27–90.39% for Sentinel-2 and 84.15% for the integration of Sentinel-2/MOD13Q1/Sentinel-1. The results from this study show that it is possible to integrate multisource remote sensing for regular monitoring of rice phenology, thereby generating spatial information to support local-, national-, and regional-scale food security applications.
Fadhlullah Ramadhani; Reddy Pullanagari; Gabor Kereszturi; Jonathan Procter. Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1. Remote Sensing 2020, 12, 3613 .
AMA StyleFadhlullah Ramadhani, Reddy Pullanagari, Gabor Kereszturi, Jonathan Procter. Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1. Remote Sensing. 2020; 12 (21):3613.
Chicago/Turabian StyleFadhlullah Ramadhani; Reddy Pullanagari; Gabor Kereszturi; Jonathan Procter. 2020. "Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1." Remote Sensing 12, no. 21: 3613.
Regular monitoring and mapping of rice (Oryza Sativa) growth phases are essential for industry stakeholders to ensure food production is on track and to assess the impact of climate change on rice production. In Indonesia, high-cost field surveys have been widely used to monitor the rice growth phases. Alternatively, this research proposes a methodology to retrieve multi-temporal rice phenology (vegetative, reproductive, and ripening) and bare land mapping using medium resolution remote sensing imagery obtained from Landsat-8 Operational Land Imager (OLI) combined with machine learning techniques. In this study, we have used extensive ground validation information collected from 2014 to 2016 for training the models. This ground validation information was obtained from pre-installed webcams across Indonesia. Five different machine learning algorithms were used including random forest (RF), support vector machine (SVM) with three kernel functions (linear, polynomial, and radial) and artificial neural networks (ANN) to classify rice growth phases and bare land. This paper also evaluates the temporal evolution of rice phenology and bare land to check the prediction model consistency between two consecutive dates in 3 years. The results show that the nonlinear SVM algorithm gives the best model accuracy (70.5% with Kappa: 0.66) based on the test dataset and the lowest temporal changes (<11%). Spatial-temporal assessment of rice phenology and bare land from Landsat-8 indicated that the models were reliable and robust over different seasons and years. The distribution of rice phenology maps will enable Indonesian management authorities to supply fertilizer, allocate water resources, harvesting, and marketing facilities more efficiently.
Fadhlullah Ramadhani; Reddy Pullanagari; Gabor Kereszturi; Jonathan Procter. Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning. International Journal of Remote Sensing 2020, 41, 8428 -8452.
AMA StyleFadhlullah Ramadhani, Reddy Pullanagari, Gabor Kereszturi, Jonathan Procter. Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning. International Journal of Remote Sensing. 2020; 41 (21):8428-8452.
Chicago/Turabian StyleFadhlullah Ramadhani; Reddy Pullanagari; Gabor Kereszturi; Jonathan Procter. 2020. "Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning." International Journal of Remote Sensing 41, no. 21: 8428-8452.