scholarly journals Rice growth phase analysis in Pidie regency, Indonesia using multitemporal Sentinel-2 image data: a spectral angle mapper approach

2022 ◽  
Vol 951 (1) ◽  
pp. 012068
Author(s):  
N Lisviananda ◽  
S Sugianto ◽  
M Rusdi
Keyword(s):  
Spectral Analysis ◽  
Plant Growth ◽  
Growth Phase ◽  
Growing Season ◽  
Growth Stages ◽  
Spectral Angle Mapper ◽  
Rice Growth ◽  
Spectral Angle ◽  
Rice Growing Season ◽  
Sentinel 2

Abstract Remote sensing data provides fast and relatively accurate information to retrieve the plant growth phase using spectral analysis. Spectral analysis of plants is the critical point of identifying the stages of rice growth using Sentinel-2 data. Sentinel-2 satellite images were utilized for this study. This study aims to analyze the growth phase of rice in Pidie regency, Aceh Province, Indonesia, as a sample area of the rice-growing site. The Spectral Angle Mapper (SAM) approach was performed to describe the plant growth stages. The results show variations in the rice growth phase across the study area for 2019, 2020, and 2021 growing seasons from vegetative, generative, wet fallow, and dry fallow. The most extensive vegetative phase is for April 2021 data, counting for 1,278.16 Ha. The most extensive generative phase was identified of June 2020 data, counting for 1,107.55 Ha. For wet fallow, counting for 949,30 Ha is the largest in this category. A total of 1,311.94 Ha of dry fallow is identified in 2019. The different growth phases and the total area for different years indicate variation in starting for the growing season of the sample location. In this paper, multitemporal Sentinel-2 data analyzed with the SAM approach has demonstrated identifying rice-growing season phases. This finding can help predict the total area along the year for a change of the pattern of the rice-growing season in the last three years of the study area.

scholarly journals Automatic Mapping of Rice Growth Stages Using the Integration of SENTINEL-2, MOD13Q1, and SENTINEL-1

2020 ◽  
Vol 12 (21) ◽  
pp. 3613 ◽  
Author(s):  
Fadhlullah Ramadhani ◽  
Reddy Pullanagari ◽  
Gabor Kereszturi ◽  
Jonathan Procter
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Prediction Models ◽  
Spatial Information ◽  
Rice Production ◽  
Growth Stages ◽  
Time Lags ◽  
Cropping Pattern ◽  
Rice Growth ◽  
Sentinel 2

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.

scholarly journals Cartografía del aguacate en el sur del estado de México mediante tratamiento digital de imágenes sentinel-2

2020 ◽  
Vol 11 (4) ◽  
pp. 865-879
Author(s):  
Dulce Karen Figueroa-Figueroa ◽  
Jose Francisco Ramírez Dávila ◽  
Xanat Antonio-Némiga ◽  
Andrés González Huerta
Keyword(s):  
Persea Americana ◽  
Spectral Information ◽  
Spectral Angle Mapper ◽  
Information Divergence ◽  
Spectral Angle ◽  
Sentinel 2

El cultivo de aguacate (Persea americana Mill.) es uno de los más importantes en México, entre los estados con mayor producción se encuentra el Estado de México, que es el tercer estado productor a nivel nacional. Coatepec Harinas y Donato Guerra son dos de los municipios más representativos en lo respectivo a esta actividad; sin embargo, no existe un censo que especifique la superficie del cultivo, por lo que el objetivo de esta investigación fue probar métodos de índices de vegetación, algoritmos spectral angle mapper (SAM) y spectral information divergence (SID) y la combinación de estos en las imágenes del sensor Sentinel-2 para evaluar su desempeño en la identificación de áreas plantadas con el cultivo de aguacate. Los resultados se validaron con una matriz de confusión y la comparación de los datos de referencia de entrenamiento y validación. El algoritmo SID alcanzó una precisión de 97.5% para detectar aguacate, mientras que el tratamiento SAM obtuvo una precisión de 63.1%. La combinación de SID con el índice Anthocyanin Reflectance Index 1 (ARI1), proporcionó un mejor resultado sobre la cartografía de validación regional con 85% de precisión. Otras combinaciones de índices y tratamientos dieron resultados inferiores al 50% de la precisión por lo que no se recomiendan. Esta metodología podría ser probada para la detección de otros cultivos de interés comercial, dado que Sentinel-2 muestra ser una alternativa viable para este tipo de estudios, teniendo una buena resolución espectral, además de ser de fácil acceso y manipulación.

scholarly journals Effect of Rice Growth Stages and Silicon on Sheath Blight Development

2003 ◽  
Vol 93 (3) ◽  
pp. 256-261 ◽  
Author(s):  
Fabrício Á. Rodrigues ◽  
Francisco X. R. Vale ◽  
Lawrence E. Datnoff ◽  
Anne S. Prabhu ◽  
Gaspar H. Korndörfer
Keyword(s):  
Plant Growth ◽  
Dry Weight ◽  
Sheath Blight ◽  
Growth Stages ◽  
Leaf Stage ◽  
Commercial Use ◽  
Rice Growth ◽  
Extension Curve ◽  
Plant Growth Stages ◽  
Lesion Extension

The objective of this study was to determine the effect of silicon (Si) and rice growth stages on tissue susceptibility to sheath blight (Rhizoctonia solani Kühn) under controlled conditions. Rice plants (cv. Rio Formoso) were grown in pots containing low-Si soil amended with Si at 0, 0.48, 0.96, 1.44, and 1.92 g pot-1 and inoculated with R. solani at the following days after emergence: 45 (four-leaf stage), 65 (eight-leaf stage), 85 (tillering), 117 (booting), and 130 (panicle exsertion). For plants inoculated with R. solani at all growth stages, Si concentration in straw increased as rate of Si increased from 0 to 1.92 g pot-1. Concentration of calcium in the straw did not differ among plant growth stages. Although incubation period was not affected by the amount of Si added to the soil, this variable was shorter at booting and panicle exsertion stages. As the rates of Si increased in the soil, the total number of sheath blight lesions on sheaths and total area under the relative lesion extension curve decreased at all plant growth stages. The severity of sheath blight was lower at booting and panicle exsertion stages as the rates of Si increased in the soil. In general, plants grown in Si-nonamended pots and inoculated with R. solani were more vulnerable to infection at all growth stages, but especially at 45 days after emergence. Plant dry weights for inoculated plants increased as the Si rates increased from 0 to 1.92 g pot-1. The greatest dry weight increases occurred for plants inoculated at booting and panicle exsertion stages. Si fertilization is a promising method for controlling sheath blight in areas where soil is Si deficient and when cultivars that exhibit an acceptable level of resistance to sheath blight are not available for commercial use.

scholarly journals An Application of the “Traffic Lights” Idea to Crop Control in Integrated Administration Control System

2021 ◽  
Vol 15 (4) ◽  
pp. 129-152
Author(s):  
Beata Hejmanowska ◽  
Mariusz Twardowski ◽  
Anna Żądło
Keyword(s):  
Automated Classification ◽  
Spectral Angle Mapper ◽  
Trade Off ◽  
Traffic Lights ◽  
The Status ◽  
Direct Payments ◽  
Using Data ◽  
Clear Information ◽  
Spectral Angle ◽  
Sentinel 2

The aim of the paper is to discuss the idea of marking agricultural parcels in the control of direct payments to agriculture. The method of using remote sensing to monitor crops and mark them according to the idea of “traffic lights” is introduced. Classification into a given “traffic lights” color gives clear information about the status of the parcel. The image classification was done on Sentinel-1 and Sentinel-2 datasets by calculating the NDVI and SIGMA time series in the season from autumn 2016 to autumn 2017. Two approaches are presented: semi-automated and automated classifications. Semi-automated classification based on NDVI_index and SIGMA_index. Automated classification was performed on NDVI by Spectral Angle Mapper method and on SIGMA by Artificial Neural Network (Multilayer Perceptron, MLP method). The following overall accuracy was obtained for NDVI_SAM: 70.35%, while for SIGMA_CNN it was: 62.01%. User accuracy (UA) values were adopted for traffic lights analysis, in machine learning: positive predictive value (PPV). The UA/PPV for rapeseed were in NDVI_index method: 88.1% (6,986 plots), NDVI_SAM: 85.0% (199 plots), SIGMA_index: 61.3% (4,165 plots) and in SIGMA_CNN: 88.9% (2,035 plots). In order to present the idea of “traffic lights”, a website was prepared using data from the NDVI_index method, which is a trade-off between the number of plots and UA/PPV accuracy.

scholarly journals Vegetation Response to Goats Grazing Intensity in Semiarid Hilly Grassland of the Loess Plateau, Lanzhou, China

2021 ◽  
Vol 13 (6) ◽  
pp. 3569
Author(s):  
Hua Cheng ◽  
Baocheng Jin ◽  
Kai Luo ◽  
Jiuying Pei ◽  
Xueli Zhang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Community Structure ◽  
Plant Growth ◽  
Loess Plateau ◽  
Grazing Intensity ◽  
Gps Tracking ◽  
Growth Stages ◽  
The Loess Plateau ◽  
Vegetation Community ◽  
Natural Grasslands

Quantitatively estimating the grazing intensity (GI) effects on vegetation in semiarid hilly grassland of the Loess Plateau can help to develop safe utilization levels for natural grasslands, which is a necessity of maintaining livestock production and sustainable development of grasslands. Normalized difference vegetation index (NDVI), field vegetation data, and 181 days (one goat per day) of GPS tracking were combined to quantify the spatial pattern of GI, and its effects on the vegetation community structure. The spatial distribution of GI was uneven, with a mean value of 0.50 goats/ha, and 95% of the study area had less than 1.30 goats/ha. The areas with utilization rates of rangeland (July) lower than 45% and 20% made up about 95% and 60% of the study area, respectively. Grazing significantly reduced monthly aboveground biomass, but the grazing effects on plant growth rate were complex across the different plant growth stages. Grazing impaired plant growth in general, but the intermediate GI appeared to facilitate plant growth rate at the end of the growing seasons. Grazing had minimal relationship with vegetation community structure characteristics, though Importance Value of forbs increased with increasing GI. Flexibility in the number of goats and conservatively defining utilization rate, according to the inter-annual variation of utilization biomass, would be beneficial to achieve ecologically healthy and economically sustainable GI.

STICS crop model and Sentinel-2 images for monitoring rice growth and yield in the Camargue region

2021 ◽  
Vol 41 (4) ◽  
Author(s):  
Dominique Courault ◽  
Laure Hossard ◽  
Valérie Demarez ◽  
Hélène Dechatre ◽  
Kamran Irfan ◽  
...  
Keyword(s):  
Crop Model ◽  
Growth And Yield ◽  
Rice Growth ◽  
Sentinel 2
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