Response of Rice Ecological Indicators to Water Consumption Based on Multi-source Data in Irrigation District Scale

2020 ◽  
Author(s):  
Junming Yang ◽  
Yunjun Yao ◽  
Ke Shang ◽  
Xiaozheng Guo ◽  
Xiangyi Bei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Water Consumption ◽  
Growth And Development ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Fusion Method ◽  
Development Stages ◽  
The Relationship

<p>The study of law of crop water consumption in small scale such as irrigation area requires remote sensing image data with high spatial and temporal resolution, however, remote sensing images that possess both high temporal and spatial resolution cannot be obtained for technical reasons. To solve the problem, this paper present a multisource remote sensing data spatial and temporal reflectance fusion method based on fuzzy C clustering model (FCMSTRFM) and multisource Vegetation index (VI) data spatial and temporal fusion model (VISTFM), the Landsat8 OLI and MOD09GA data are combined to generate high spatial and temporal resolution reflectance data and the landsat8 OLI, MOD09GA and MOD13Q1 data are combined to generate high spatial and temporal resolution normalized vegetation index (NDVI) and enhanced vegetation index (EVI) data.</p><p>The rice area is mapped by spectral correlation similarity (SCS) between standard series EVI curve that based the EVI generated by VISTFM and average value of each EVI class that generated by classing Multiphase EVI into several class, the extraction results are verified by two methods: ground sample and Google Earth image. high spatial and temporal resolution Leaf area index (LAI) that covered the mainly rice growth and development stages is generated by higher precision method between artificial neural network and equation fitting that establish the relationship between NDVI, EVI and LAI. The yield of rice in the spatial scale is generated by establishing the relationship between yield and LAI of the mainly growth and development stages that has the maximum correlation with yield. Daily high spatial resolution evapotranspiration is generated by using multisource remote sensing data spatial and temporal reflectance fusion method to fusion the MODIS-like scale and Landsat-like scale evapotranspiration that generated by The Surface Energy Balance Algorithm for Land (SEBAL). Based on the data, the evapotranspiration, LAI and yield of rice, obtained by remote sensing methods, rice water growth function is established by Jensen, Blank, Stewart and Singh model.</p>

scholarly journals RELATIONSHIPS BETWEEN RICE GROWTH PARAMETERS AND REMOTE SENSING DATA

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
I Wayan Nuarsa ◽  
Fumihiko Nishio
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristic ◽  
Vegetation Index ◽  
Growth Parameters ◽  
Life Stage ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Rice Growth ◽  
Vegetation Water ◽  
The Relationship

Rice is an agriculture plants that has the specific characteristic in the life stage due to the growth stage having different proportion of vegetation, water, and soil. Vegetation index is one of the satellite remote sensing parameter that is widely used to monitor the global vegetation cover. The objective of the study is to know the spectral characteristic of rice plant in the life stage and find the relationship between the rice growth parameters and the remote sensing data by the Landsat ETM data using the correlation and regression analysis. The result of study shows that the spectral characteristic of the rice before one month of age is defferent comparing after one month. All of the examined vegetation index has close linear relationship with rice coverage. Difference Vegetation Index (DVI) is the best vegetation index which estimates rice coverage with equation y = 1.762x + 2.558 and R degree value was 0.946. Rice age has a high quadratic relationship with all of evaluated vegetation index. Transformed Vegetation Index (TVI) is the best vegetation to predict the age of the rice. Formula y = 0.013x - 1.625x + 145.8 is the relationship form between the rice age and the TVI with R = 0.939. Peak of the vegetation index of rice is in the rice age of 2 months. This period is the transition of vegetative and generative stages. Keywords: Vegetation index, Rice growth, Spectral characteristic, Landsat ETM.

Remote Sensing Satellites for Land Applications: A Review

2019 ◽  
Vol 2 (2) ◽  
pp. 96-104
Author(s):  
Suresh Kumar ◽  
Vijay Bhagat
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Satellite Remote Sensing ◽  
Land Development ◽  
Remote Sensing Data ◽  
Microwave Remote Sensing ◽  
Development Planning ◽  
Sensing Data ◽  
Cloudy Weather

Satellite remote sensing offers a unique opportunity in deriving various components of land information by integrating with ground based observation. Currently several remote sensing satellites are providing multispectral, hyperspectral and microwave data to cater the need of various land applications. Several old age remote sensing satellites have been updated with new generation satellites offering high spatial, spectral and temporal resolution. Microwave remote sensing data is now available with high spatial resolution and providing land information in cloudy weather condition that strengthening availability of remote sensing data in all days. Spatial resolution has significantly improved over the decades and temporal resolution has improved from months to daily. Indian Remote Sensing programs are providing state of the art satellite data in optical and microwave wavelength regions to meet large land applications in the country. Today several remote sensing data is available as open data sources. Upcoming satellite remote sensing data will help in precise characterization and quantification of land resources to support in sustainable land development planning to meet future challenges.

Using 250-m MODIS Data for Enhancing Spatiotemporal Fusion by Sparse Representation

2020 ◽  
Vol 86 (6) ◽  
pp. 383-392
Author(s):  
Liguo Wang ◽  
Xiaoyi Wang ◽  
Qunming Wang
Keyword(s):  
Remote Sensing ◽  
Sparse Representation ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Remote Sensing Data ◽  
Landsat Data ◽  
Sensing Data ◽  
Modis Data ◽  
Fine Spatial Resolution ◽  
Fusion Methods

Spatiotemporal fusion is an important technique to solve the problem of incompatibility between the temporal and spatial resolution of remote sensing data. In this article, we studied the fusion of Landsat data with fine spatial resolution but coarse temporal resolution and Moderate Resolution Imaging Spectroradiometer (MODIS) data with coarse spatial resolution but fine temporal resolution. The goal of fusion is to produce time-series data with the fine spatial resolution of Landsat and the fine temporal resolution of MODIS. In recent years, learning-based spatiotemporal fusion methods, in particular the sparse representation-based spatiotemporal reflectance fusion model (SPSTFM), have gained increasing attention because of their great restoration ability for heterogeneous landscapes. However, remote sensing data from different sensors differ greatly on spatial resolution, which limits the performance of the spatiotemporal fusion methods (including SPSTFM) to some extent. In order to increase the accuracy of spatiotemporal fusion, in this article we used existing 250-m MODISbands (i.e., red and near-infrared bands) to downscale the observed 500-m MODIS bands to 250 m before SPTSFM-based fusion of MODIS and Landsat data. The experimental results show that the fusion accuracy of SPTSFM is increased when using 250-m MODIS data, and the accuracy of SPSTFM coupled with 250-m MODIS data is greater than the compared benchmark methods.

scholarly journals A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

2020 ◽  
Vol 12 (23) ◽  
pp. 3888
Author(s):  
Mingyuan Peng ◽  
Lifu Zhang ◽  
Xuejian Sun ◽  
Yi Cen ◽  
Xiaoyang Zhao
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Temporal Resolution ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
High Temporal Resolution ◽  
Fusion Method ◽  
Sensing Data ◽  
Resolution Data

With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.

scholarly journals Rubber Identification Based on Blended High Spatio-Temporal Resolution Optical Remote Sensing Data: A Case Study in Xishuangbanna

2019 ◽  
Vol 11 (5) ◽  
pp. 496 ◽  
Author(s):  
Shupeng Gao ◽  
Xiaolong Liu ◽  
Yanchen Bo ◽  
Zhengtao Shi ◽  
Hongmin Zhou
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Temporal Resolution ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Rubber Plantation ◽  
Optical Remote Sensing ◽  
Rubber Plantations ◽  
Sensing Data ◽  
Spatio Temporal

As an important economic resource, rubber has rapidly grown in Xishuangbanna of Yunnan Province, China, since the 1990s. Tropical rainforests have been replaced by extensive rubber plantations, which has resulted in ecological problems such as the loss of biodiversity and local water shortages. It is vitally important to accurately map the rubber plantations in this region. Although several rubber mapping methods have been proposed, few studies have investigated methods based on optical remote sensing time series data with high spatio-temporal resolution due to the cloudy and foggy weather conditions in this area. This study presented a rubber plantation identification method that used spatio-temporal optical remote sensing data fusion technology to obtain vegetation index data at high spatio-temporal resolution within the optical remote sensing window in Xishuangbanna. The analysis of the proposed method shows that (1) fused optical remote sensing data with high spatio-temporal resolution could map the rubber distribution with high accuracy (overall accuracy of up to 89.51% and kappa of 0.86). (2) Fused indices have high R2 (R2 greater than 0.8, where R is the correlation coefficient) with the indices that were derived from the Landsat observed data, which indicates that fusion results are dependable. However, the fusion accuracy is affected by terrain factors including elevation, slope, and slope aspects. These factors have obvious negative effects on the fusion accuracy of high spatio-temporal resolution optical remote sensing data: the highest fusion accuracy occurred in areas with elevations between 1201 and 1400 m.a.s.l., and the lowest accuracy occurred in areas with elevations less than 600 m.a.s.l. For the 5 fused time series indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and tasseled cap angle (TCA)), the fusion accuracy decreased with increasing slope, and increasing slope had the least impact on the EVI, but the greatest negative impact on the NDVI; the slope aspect had a limited influence on the fusion accuracies of the 5 time series indices, but fusion accuracy was lowest on the northwest slope. (3) EVI had the highest accuracy of rubber plantation classification among the 5 time series indices, and the overall classification accuracies of the time series EVI for the four different years (2000, 2005, 2010, and 2015) reached 87.20% (kappa 0.82), 86.91% (kappa 0.81), 88.85% (kappa 0.84), and 89.51% (kappa 0.86), respectively. The results indicate that the method is a promising approach for rubber plantation mapping and the detection of changes in rubber plantations in this tropical area.

scholarly journals Features of the dynamics of the vegetation index of ndvi in different soil-climatic zones of the Stavropol territory

2019 ◽  
pp. 12-18
Author(s):  
И СТОРЧАК ◽  
I. STORCHAK ◽  
Ф. Ерошенко ◽  
F. Eroshenko ◽  
Е ШЕСТАКОВА ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Correlation Coefficient ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Climatic Zones ◽  
Earth Remote Sensing ◽  
Sensing Data ◽  
The Relationship

Abstract. Currently, in the agricultural sector, research results are being actively used to predict crop yields using Earth remote sensing data. It is known that the resulting regression models depend on soil and climatic conditions of cultivation. In order to determine the degree of development and condition of plants, you can use the vegetation index NDVI. The advantage of this method is the objectivity of the estimates, and the ability to apply them to large areas. Unfortunately, studies of the influence of soil-climatic zones (CLC) of cultivation on the relationship between the yield of winter wheat and Earth remote sensing data are practically not conducted. The aim of the work was to identify the influence of the conditions of various soil-climatic zones of the Stavropol Territory on the features of the connections of Earth remote sensing data with the productivity of winter wheat crops. The studies were carried out on the basis of the FSUE „North Caucasus Federal Scientific Agrarian Center“. The objects of research were crops of winter wheat of the Stavropol Territory. In the course of work, the statistical data of the Ministry of Agriculture of the Stavropol Territory was used. The NDVI vegetation index was obtained using the VEGA service of the Space Research Institute of the Russian Academy of Sciences. The relationship between NDVI and winter wheat yield for the soil and climatic zones of the Stavropol Territory has been established. The resulting models have a high degree of confidence (the coefficient of approximation is within 0.5–90.82, the correlation coefficient is 0.77–0.90). The regression model of the connection of the average NDVI for the vegetative-generative period and the grain yield of the Stavropol Territory, built using data from soil-climatic zones, has a fairly high accuracy (correlation coefficient 0.82, approximation coefficient 0.72). The use of Earth remote sensing data calculated by soil and climatic zones significantly increases the correlation between the NDVI vegetation index and the productivity of winter wheat sowing. This makes it possible to more accurately predict the yield for the entire Stavropol Territory.

scholarly journals Analysis of Influencing Factors on Winter Wheat Yield Estimations Based on a Multisource Remote Sensing Data Fusion

2021 ◽  
Vol 37 (5) ◽  
pp. 991-1003
Author(s):  
Yan Li ◽  
Yan Zhao Ren ◽  
Wan Lin Gao ◽  
Sha Tao ◽  
Jing Dun Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Time Window ◽  
Wheat Yield ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Winter Wheat Yield ◽  
Window Selection

HighlightsThe potential of fusing GF-1 WFV and MODIS data by the ESTARFM algorithm was demonstrated.A better time window selection method for estimating yields was provided.A better vegetation index suitable for yield estimation based on spatiotemporally fused data was identified.The effect of the spatial resolution of remote sensing data on yield estimations was visualized.Abstract. The accurate estimation of crop yields is very important for crop management and food security. Although many methods have been developed based on single remote sensing data sources, advances are still needed to exploit multisource remote sensing data with higher spatial and temporal resolution. More suitable time window selection methods and vegetation indexes, both of which are critical for yield estimations, have not been fully considered. In this article, the Chinese GaoFen-1 Wide Field View (GF-1 WFV) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data were fused by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to generate time-series data with a high spatial resolution. Then, two time window selection methods involving distinguishing or not distinguishing the growth stages during the monitoring period, and three vegetation indexes, the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2) and wide dynamic range vegetation index (WDRVI), were intercompared. Furthermore, the yield estimations obtained from two different spatial resolutions of fused data and MODIS data were analyzed. The results indicate that taking the growth stage as the time window unit division basis can allow a better estimation of winter wheat yield; and that WDRVI is more suitable for yield estimations than NDVI or EVI2. This study demonstrates that the spatial resolution has a great influence on yield estimations; further, this study identifies a better time window selection method and vegetation index for improving the accuracy of yield estimations based on a multisource remote sensing data fusion. Keywords: Remote sensing, Spatiotemporal data fusion, Winter wheat, Yield estimation.

Association Analysis of NDVI Changes and Topographic Factors

2013 ◽  
Vol 333-335 ◽  
pp. 1205-1208
Author(s):  
De Li Liu ◽  
Ya Shuang Zhang ◽  
Nan Lin
Keyword(s):  
Remote Sensing ◽  
Spatial Analysis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Topographic Factors ◽  
Gis Software ◽  
Relationship Of ◽  
The Relationship ◽  
Slope Direction

Based on the TM remote sensing data of the Huadian city in 1991 and 2011 and based on the DEM data,using the normalized difference vegetation index (NDVI) change classification method,to Extraction the elevation,slope,slope direction data and the vegetation index data of the study area.Then using the spatial analysis function of GIS software to overlay the two different period NDVI data and analysis the NDVI change of area and spatial. Using the same method to overlay and analysis the relationship of NDVI data and elevation,slope,slope direction.Research shows that the variation of NDVI in the study area has relationship with the topographic factors change.

scholarly journals Possibility of determining the nitrogen content in winter wheat plants during the earing phase using remote sensing data

2020 ◽  
Vol 26 (3) ◽  
pp. 199-209
Author(s):  
Fedor Eroshenko ◽  
Irina Storchak ◽  
Irina Engovatova ◽  
Andrey Likhovid
Keyword(s):  
Remote Sensing ◽  
Winter Wheat ◽  
Nitrogen Content ◽  
Growth And Development ◽  
Vegetation Index ◽  
Local Level ◽  
Remote Sensing Data ◽  
Reflection Index ◽  
Sensing Data ◽  
Agricultural Enterprise

The study examined the possibility of using remote sensing Data (RED, NIR, NDVI) for monitoring winter wheat crops in production conditions for nitrogen content in plants. This work is divided into two stages: 1) analysis of the correlation between NDVI indicators and nitrogen content on production crops of the North-Caucasian FNAC; 2) comparative analysis of the correlation between nitrogen content and remote sensing data in the conditions of the “Rodina” agricultural enterprise in the Shpakovsky district of the Stavropol territory. Selection of plant samples (sheaf material) was carried out according to the generally accepted method. Repeatability — 4-fold. The chemical composition of plant organs was determined using the method of V.T. Kurkaev and co-authors, and the chlorophyll content was determined by Y.I. Milaeva and N.P. Primak. We used the earth remote sensing data provided by the Terra satellite and obtained by the Modis scanning Spectroradiometer. At the first stage, the relationships between the nitrogen content in winter wheat plants and the values of the normalized difference vegetation index (NDVI) were studied. At the early stages of growth and development of winter wheat plants, high correlation coefficients between these indicators were obtained. Thus, the correlation coefficient on average for the fields in 2012 was equal to -0.89, and in 2013 and 2014 — -0.82. In later phases of growth and development of winter wheat plants, this relationship was not observed. At the second stage, it was found that it is advisable to use the red reflection index to assess the nitrogen content at the local level (a separate agricultural enterprise) in the earing phase. In this case, there is a stable inverse correlation — the average for three years of research was -0.71. When other remote sensing indicators (NDVI and NIR) are used in the analysis, the links are either absent or less apparent.

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