Genotypic difference in canopy diffusive conductance measured by a new remote-sensing method and its association with the difference in rice yield potential

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

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Estimation of rice yield by SIMRIW-RS, a model that integrates remote sensing data into a crop growth model

Journal of Agricultural Meteorology ◽

10.2480/agrmet.d-14-00023 ◽

2017 ◽

Vol 73 (1) ◽

pp. 2-8 ◽

Cited By ~ 16

Author(s):

Masayasu MAKI ◽

Kosuke SEKIGUCHI ◽

Koki HOMMA ◽

Yoshihiro HIROOKA ◽

Kazuo OKI

Keyword(s):

Remote Sensing ◽

Growth Model ◽

Rice Yield ◽

Remote Sensing Data ◽

Crop Growth ◽

Crop Growth Model ◽

Sensing Data

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A NOVEL METHOD OF CLOUD AND SHADOW EXTRACTION WITH MULTI-TEMPORAL LANDSAT8 OLI IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-1301-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 1301-1305

Author(s):

Y. Ni ◽

G. He ◽

W. Jiang

Keyword(s):

Remote Sensing ◽

Large Change ◽

Remote Sensing Image ◽

Image Process ◽

Actual Application ◽

Significant Step ◽

Multi Temporal ◽

Ground Object ◽

Novel Method ◽

The Difference

Cloud and Shadow removal is a significant step in remote sensing image process. As we all know, the ground object coverage type of the same area of the remote sensing image has little change in the short term. But for cloud and shadow coverage areas, the ground object coverage type has large change. Therefore, according to the difference between the two Landsat / OLI images caused by changes in the cover, this paper presents a method of extracting clouds and shadows based on differences in luminance values. This method selects two thresholds for the difference of brightness values, and extracts the clouds and shadows respectively, and validates them with random point method, which can obtain high precision of extracting cloud and shadow and satisfy the actual application needs.

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Development of a remote sensing-based rice yield forecasting model

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2016143-8347 ◽

2016 ◽

Vol 14 (3) ◽

pp. e0907 ◽

Cited By ~ 14

Author(s):

Mostafa K. Mosleh ◽

Quazi K. Hassan ◽

Ehsan H. Chowdhury

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Rice Yield ◽

Decision Makers ◽

Coefficient Of Determination ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

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MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13224528 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4528

Author(s):

Xin Yang ◽

Lei Hu ◽

Yongmei Zhang ◽

Yunqing Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Change Detection ◽

Remote Sensing Image ◽

Image Features ◽

Remote Sensing Images ◽

The Rich ◽

The Difference ◽

Basic Network ◽

And Performance

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.

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‘B-M MODEL’ FOR FARMERS’ KNOWLEDGE MANAGEMENT IN INCREASING RICE PRODUCTION

Knowex food and agriculture ◽

10.17501/2682728x.2021.2104 ◽

2021 ◽

Vol 2 (1) ◽

pp. 30-45

Author(s):

BK Mahalder ◽

◽

MB Ahmed ◽

H Bhandari ◽

MU Salam ◽

...

Keyword(s):

Knowledge Management ◽

Field Data ◽

Production Systems ◽

Rice Yield ◽

Rice Production ◽

Practice Change ◽

Clear Understanding ◽

Data Set ◽

Production Practices ◽

The Difference

Quantifying knowledge on agriculture can have many benefits to stakeholders. While many knowledge-based systems exist in modern days for farmers’ decision support, specific models are lacking on how knowledge traits can impact on agricultural production systems. This study employed modelling technique, supported by field data, to provide a clear understanding and quantifying how knowledge management in production practices can contribute to rice productivity in the environmentally stressed southwest Bangladesh. This research accounted for ‘Boro’ rice as the target crop and ‘BRRI dhan28’ as the test variety. The ‘B-M Model’ was developed following the principle and procedure from published literature, ‘brainstorming’ and data from field surveys. Three knowledge management traits (KMT) were defined and quantified as the inputs of the model. Those are: self-experience and observation (SEO), extension advisory services (EAS) and accessed information sources (AIS). The yield influencing process (YIP), the intermediate state variable of the model, was deduced by accounting for the two dominant agronomic practices, seedling age for transplanting and triple superphosphate (TSP) application. ‘Knowledge drives farmers’ practice change which in turn influences yield’ was composed as the theoretical framework of the ‘B-M Model’. The model performed strongly against an independently collected field data set. Across the 180 farmers’ data, the average relative rice yield (RRY) predicted by the model (0.705) and observed in the field (0.716) was close (root mean squared deviation (RMSD) = 0.018). The difference between predicted and observed RRY was not statistically different (LSD = 0.03), indicating the model fully captured the field data. A regression of predicted and observed RRY explained 96% variance in observation, further proving the model’s strength in estimating RRY in a wider range of farmers’ rice yield. In a normative analysis, the practicality and usefulness of the model to stakeholders were simulated for the understanding of how much achievable yield could be expected by changing farmers’ knowledge pool (the sum of three KMT) on rice production practices, and at what combination(s) of KMT to be considered at strategic hierarchy to materialize a targeted achievable yield. To the best of the knowledge, a model quantifying rice yield in relation to knowledge management trait does not exist in literature. Upon successful testing under diverse yield scenarios using multiple and sophisticated statistical tools that enhanced the credibility of the model, it is concluded that the model has the potential to be used for identifying quantitative pathways of farmers’ knowledge acquisition for practice change leading to improved productivity of rice in the southwest region of Bangladesh.

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Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

Forum Geografi ◽

10.23917/forgeo.v19i1.4567 ◽

2017 ◽

Vol 19 (1) ◽

pp. 1

Author(s):

Beny Harjadi

Keyword(s):

Remote Sensing ◽

Field Survey ◽

Critical Level ◽

Satellite Image ◽

Geographic Information ◽

Level Data ◽

The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

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Vegetation Height Estimation using Satellite Remote Sensing in Peat Land of Central Kalimantan

Journal of Environmental Analysis and Progress ◽

10.24221/jeap.6.1.2021.3001.024-034 ◽

2021 ◽

Vol 6 (1) ◽

pp. 024-034

Author(s):

Atriyon Julzarika ◽

Harintaka Harintaka ◽

Tatik Kartika

Keyword(s):

Remote Sensing ◽

Spatial Information ◽

Geoid Undulation ◽

Height Estimation ◽

Central Kalimantan ◽

Vegetation Height ◽

Accuracy Test ◽

Sensing Technology ◽

The Difference ◽

Difference Test

Vegetation height is an important parameter in monitoring peatlands. Vegetation height can be estimated using remote sensing. Vegetation height can be estimated by utilizing DSM and DTM. The data that can be used are LiDAR, X-SAR, and SRTM C. In this study, LiDAR data is used for DSM2018 and DTM2018 extraction. The purpose of this research is to detect the vegetation height in Central Kalimantan peatlands using remote sensing technology. The research location is in Bakengbongkei, Kalampangan, Central Kalimantan. The integration of X-SAR and SRTM C is used for DSM2000 and DTM2000 extraction. DSM2000, DTM2000, DSM2018, and DTM2018 performed height error correction with tolerance of 1.96? (95%). Then do the geoid undulation correction to EGM2008. The results obtained are DSM and DTM with a similar height reference field. If it meets these conditions it can be calculated the vegetation height estimation. Vegetation height can be obtained using the Differential DEM method. The Changing in vegetation height from 2000 to 2018 can be estimated from the difference in vegetation height from 2000 to vegetation height in 2018. Results of spatial information on vegetation height and its changes need to be tested for the accuracy. This accuracy-test includes a cross section test, height difference test, and comparison with measurements of vegetation height in the field. The results of this research can be used to monitor the changing the vegetation height in peatlands.

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Emerging technologies for integrated nematode management: remote sensing or proximal sensing as a potential tool to detect and identify nematode infestation.

10.1079/9781789247541.0058 ◽

2021 ◽

pp. 414-420

Author(s):

Matheus T. Kuska ◽

Matthias Daub ◽

Anne-Katrin Mahlein

Keyword(s):

Remote Sensing ◽

Optical Sensors ◽

Developmental Stages ◽

Remote Sensing Data ◽

Bursaphelenchus Xylophilus ◽

Heterodera Schachtii ◽

Proximal Sensing ◽

The Difference ◽

Sugarbeet Cyst Nematode ◽

Physiological Reactions

Abstract Remote or proximal sensing defines the use of optical sensors, in combination with a carrier platform, to obtain information from objects in a non-invasive manner. Optical properties of plants provide valuable information on the health status, vitality or developmental stages of plants. The difference among remote-sensing and proximal-sensing technologies is mainly characterized by the distance between the measurement system and the object of interest. This chapter discusses physiological reactions influencing optical characteristics in nematode infested plants, remote sensing with satellites, the use of robots and drones for a more flexible infield assessment, as well as the analysis and interpretation of remote-sensing data. Some case studies with pine wood nematode (Bursaphelenchus xylophilus) and sugarbeet cyst nematode (Heterodera schachtii) are presented. Further use of remote and proximal sensing for the advancement of agriculture is also mentioned.

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