scholarly journals Research Progress on Remote Sensing Classification Methods for Farmland Vegetation

2021 ◽  
Vol 3 (4) ◽  
pp. 971-989
Author(s):  
Dongliang Fan ◽  
Xiaoyun Su ◽  
Bo Weng ◽  
Tianshu Wang ◽  
Feiyun Yang
Keyword(s):  
Remote Sensing ◽  
Agricultural Development ◽  
Vegetation Index ◽  
Crop Yields ◽  
Dynamic Change ◽  
Practical Significance ◽  
Research Progress ◽  
Remote Sensing Classification ◽  
Advantages And Disadvantages ◽  
Crop Planting

Crop planting area and spatial distribution information have important practical significance for food security, global change, and sustainable agricultural development. How to efficiently and accurately identify crops in a timely manner by remote sensing in order to determine the crop planting area and its temporal–spatial dynamic change information is a core issue of monitoring crop growth and estimating regional crop yields. Based on hundreds of relevant documents from the past 25 years, in this paper, we summarize research progress in relation to farmland vegetation identification and classification by remote sensing. The classification and identification of farmland vegetation includes classification based on vegetation index, spectral bands, multi-source data fusion, artificial intelligence learning, and drone remote sensing. Representative studies of remote sensing methods are collated, the main content of each technology is summarized, and the advantages and disadvantages of each method are analyzed. Current problems related to crop remote sensing identification are then identified and future development directions are proposed.

scholarly journals COMPARISON OF SEVERAL REMOTE SENSING IMAGE CLASSIFICATION METHODS BASED ON ENVI

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 605-611
Author(s):  
X. C. Li ◽  
L. L. Liu ◽  
L. K. Huang
Keyword(s):  
Remote Sensing ◽  
Maximum Likelihood ◽  
Classification Accuracy ◽  
Supervised Classification ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Landsat Images ◽  
Distance Method ◽  
Remote Sensing Classification ◽  
Advantages And Disadvantages

Abstract. With the development of remote sensing technology and the increasing accuracy of remote sensing images, research on the accuracy of remote sensing classification is becoming more and more important. However, the classification accuracy obtained by different classification algorithms is also different. To this end, this paper selects the maximum likelihood method and the minimum distance method in the traditional supervised classification, the ISODATA method and the k-means algorithm in the unsupervised classification, and uses these four algorithms to classify the Landsat images in the research area of Heze City. The classification results are obtained and the results are evaluated. Then the four algorithms are compared separately, and the advantages and disadvantages of each algorithm are analyzed. The results show that the classification accuracy of the maximum likelihood method in the supervised classification is relatively high, and the classification accuracy is 82.3281%. The ISODATA algorithm in the supervised classification is superior to the K-means algorithm in clustering effect.

scholarly journals Multi-Year Mapping of Major Crop Yields in an Irrigation District from High Spatial and Temporal Resolution Vegetation Index

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3787 ◽  
Author(s):  
Bing Yu ◽  
Songhao Shang
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Irrigation District ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Distribution Maps

Crop yield estimation is important for formulating informed regional and national food trade policies. The introduction of remote sensing in agricultural monitoring makes accurate estimation of regional crop yields possible. However, remote sensing images and crop distribution maps with coarse spatial resolution usually cause inaccuracy in yield estimation due to the existence of mixed pixels. This study aimed to estimate the annual yields of maize and sunflower in Hetao Irrigation District in North China using 30 m spatial resolution HJ-1A/1B CCD images and high accuracy multi-year crop distribution maps. The Normalized Difference Vegetation Index (NDVI) time series obtained from HJ-1A/1B CCD images was fitted with an asymmetric logistic curve to calculate daily NDVI and phenological characteristics. Eight random forest (RF) models using different predictors were developed for maize and sunflower yield estimation, respectively, where predictors of each model were a combination of NDVI series and/or phenological characteristics. We calibrated all RF models with measured crop yields at sampling points in two years (2014 and 2015), and validated the RF models with statistical yields of four counties in six years. Results showed that the optimal model for maize yield estimation was the model using NDVI series from the 120th to the 210th day in a year with 10 days’ interval as predictors, while that for sunflower was the model using the combination of three NDVI characteristics, three phenological characteristics, and two curve parameters as predictors. The selected RF models could estimate multi-year regional crop yields accurately, with the average values of root-mean-square error and the relative error of 0.75 t/ha and 6.1% for maize, and 0.40 t/ha and 10.1% for sunflower, respectively. Moreover, the yields of maize and sunflower can be estimated fairly well with NDVI series 50 days before crop harvest, which implicated the possibility of crop yield forecast before harvest.

scholarly journals Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evapotranspiration

2021 ◽  
Vol 11 (18) ◽  
pp. 8649
Author(s):  
Yan Liu ◽  
Sha Zhang ◽  
Jiahua Zhang ◽  
Lili Tang ◽  
Yun Bai
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Agricultural Development ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Physical Models ◽  
Accurate Estimation ◽  
Surface Conductance ◽  
Artificial Neural

Accurate estimation of evapotranspiration (ET) can provide useful information for water management and sustainable agricultural development. However, most of the existing studies used physical models, which are not accurate enough due to our limited ability to represent the ET process accurately or rarely focused on cropland. In this study, we trained two models of estimating croplands ET. The first is Medlyn–Penman–Monteith (Medlyn–PM) model. It uses artificial neural network (ANN)-derived gross primary production along with Medlyn’s stomatal conductance to compute surface conductance (Gs), and the computed Gs is used to estimate ET using the PM equation. The second model, termed ANN-PM, directly uses ANN to construct Gs and simulate ET using the PM equation. The results showed that the two models can reasonably reproduce ET with ANN-PM showing a better performance, as indicated by the lower error and higher determination coefficients. The results also showed that the performances of ANN-PM without the facilitation of any remote sensing (RS) factors degraded significantly compared to the versions that used RS factors. We also evidenced that ANN-PM can reasonably characterize the time-series changes of ET at sites having a dry climate. The ANN-PM method can reasonably estimate the ET of croplands under different environmental conditions.

scholarly journals Use of remote methods for monitoring formation of yield of spring barley

2020 ◽  
pp. 58-61
Author(s):  
Irina Gennadyevna Storchak ◽  
Fedor Vladimirovich Eroshenko
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Agricultural Sector ◽  
Crop Yields ◽  
Spring Barley ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Yield Data ◽  
Sensing Data ◽  
The Earth

When cultivating barley, there is a need to monitor the condition of crops and forecast yields using objective and inexpensive methods. Remote sensing data of the Earth is used to solve various problems in the agricultural sector related to monitoring vegetation, including monitoring the condition of agricultural crops throughout the growing season. The main advantages of this observation are: efficiency, objectivity, multi-scale and cost-effective. The question of the possibility of predicting crop yields in the scientific literature has not yet been adequately reflected. Therefore, the purpose of the research was to identify the relationship between the data of remote sensing of the Earth and the yield of spring barley for the conditions of the Stavropol Territory. The studies used data from the VEGA IKI RAS service (averaged NDVI values of spring crops in the Stavropol Territory) and the statistical office of the Stavropol Territory. In the analysis of materials, NDVI values were tied to the stages of organogenesis. It was found that the closest correlation between (0.64) NDVI and spring barley yield corresponds to the phase of the formation of the caryopsis. When analyzing yield data and values of the NDVI vegetation index on fixed calendar dates (weeks) of the year, it was shown that a statistically significant correlation appears between the 13th and 26th calendar weeks of the year. Therefore, the Stavropol Territory is characterized by the dependence of barley productivity on NDVI values of spring crops. The closest it is observed in the phase of the formation of the seed. Thus, for the conditions of the Stavropol Territory, it is possible to predict the yield of spring barley according to remote sensing data of the Earth.

scholarly journals Research Progress of Stress Measurement Technologies for Optical Elements

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shan Wei ◽  
Yajun Pang ◽  
Zhenxu Bai ◽  
Yulei Wang ◽  
Zhiwei Lu
Keyword(s):  
Remote Sensing ◽  
Semiconductor Manufacturing ◽  
Stress Measurement ◽  
Residual Stress Distribution ◽  
Research Progress ◽  
Polarization Method ◽  
Optical Elements ◽  
Main Method ◽  
Optical Instruments ◽  
Advantages And Disadvantages

It is of great significance to measure the residual stress distribution accurately for optical elements and evaluate its influence on the performance of optical instruments in optical imaging, aviation remote sensing, semiconductor manufacturing, and other fields. The stress of optical elements can be closely related to birefringence based on photoelasticity. Thus, the method of quantifying birefringence to obtain the stress becomes the main method of stress measurement technologies for optical elements. This paper first introduces the basic principle of stress measurement based on photoelasticity. Then, the research progress of stress measurement technologies based on this principle is reviewed, which can be classified into two methods: polarization method and interference method. Meanwhile, the advantages and disadvantages of various stress measurement technologies are analyzed and compared. Finally, the developing trend of stress measurement technologies for optical elements is summarized and prospected.

scholarly journals Estimating Crop Yields at the Field Level Using Landsat and Modis Products

2018 ◽  
Vol 66 (5) ◽  
pp. 1141-1150 ◽  
Author(s):  
František Jurečka ◽  
Vojtěch Lukas ◽  
Petr Hlavinka ◽  
Daniela Semerádová ◽  
Zdeněk Žalud ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Spring Barley ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Yield Data ◽  
Field Level

Remote sensing can be used for yield estimation prior to harvest at the field level to provide helpful information for agricultural decision making. This study was undertaken in Polkovice, located at low elevations in the Czech Republic. From 2014–2016, two datasets of satellite imagery were used: the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 datasets. Satellite data were compared with yields and other observations at the level of land blocks. Winter oilseed rape, winter wheat and spring barley yield data, representing the crops planted over the analyzed period, were used for comparison. In 2016, a more detailed analysis was conducted. We tested a relationship between remote sensing data and the spatial yield variability measured by a yield monitor from a combine harvester. Correlations varied from approximately r = 0.4 to r = 0.7 with the highest correlation (r = 0.74) between yield and the Green Normalized Difference Vegetation Index collected from a drone. Vegetation indices from both Landsat 8 and the MODIS showed a positive relationship with yields for the compared period. The highest correlation was between yield and the Enhanced Vegetation Index (r = 0.8) while the lowest was between yield and the Normalized Difference Vegetation Index from MODIS (r = 0.1).

scholarly journals Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery

2018 ◽  
Vol 10 (9) ◽  
pp. 1489 ◽  
Author(s):  
Feng Gao ◽  
Martha Anderson ◽  
Craig Daughtry ◽  
David Johnson
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Crop Yields ◽  
Remote Sensing Data ◽  
Coefficient Of Determination ◽  
Spatial And Temporal Variability ◽  
Spatiotemporal Resolution ◽  
Sensing Data ◽  
Temporal Sampling ◽  
Medium Resolution

The utility of remote sensing data in crop yield modeling has typically been evaluated at the regional or state level using coarse resolution (>250 m) data. The use of medium resolution data (10–100 m) for yield estimation at field scales has been limited due to the low temporal sampling frequency characteristics of these sensors. Temporal sampling at a medium resolution can be significantly improved, however, when multiple remote sensing data sources are used in combination. Furthermore, data fusion approaches have been developed to blend data from different spatial and temporal resolutions. This paper investigates the impacts of improved temporal sampling afforded by multi-source datasets on our ability to explain spatial and temporal variability in crop yields in central Iowa (part of the U.S. Corn Belt). Several metrics derived from vegetation index (VI) time-series were evaluated using Landsat-MODIS fused data from 2001 to 2015 and Landsat-Sentinel2-MODIS fused data from 2016 and 2017. The fused data explained the yield variability better, with a higher coefficient of determination (R2) and a smaller relative mean absolute error than using a single data source alone. In this study area, the best period for the yield prediction for corn and soybean was during the middle of the growing season from day 192 to 236 (early July to late August, 1–3 months before harvest). These findings emphasize the importance of high temporal and spatial resolution remote sensing data in agricultural applications.

scholarly journals Downscaling Regional Crop Yields to Local Scale Using Remote Sensing

Agriculture ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 58 ◽  
Author(s):  
Paresh B. Shirsath ◽  
Vinay Kumar Sehgal ◽  
Pramod K. Aggarwal
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Vegetation Index ◽  
Crop Yields ◽  
Winter Season ◽  
Local Scale ◽  
District Level ◽  
Good Correspondence ◽  
Simple Method ◽  
Methodology Development

Local-scale crop yield datasets are not readily available in most of the developing world. Local-scale crop yield datasets are of great use for risk transfer and risk management in agriculture. In this article, we present a simple method for disaggregation of district-level production statistics over crop pixels by using a remote sensing approach. We also quantified the error in the disaggregated statistics to ascertain its usefulness for crop insurance purposes. The methodology development was attempted in Parbhani district of Maharashtra state with wheat and sorghum crops in the winter season. The methodology uses the ratio of Enhanced Vegetation Index (EVI) of pixel to total EVI of the crop pixels in that district corresponding to the growth phase of the crop. It resulted in the generation of crop yield maps at the 500 m resolution pixel (grid) level. The methodology was repeated to generate time-series maps of crop yield. In general, there was a good correspondence between disaggregated crop yield and sub-district level crop yields with a correlation coefficient of 0.9.

scholarly journals Climate-mediated spatiotemporal variability in terrestrial productivity across Europe

2014 ◽  
Vol 11 (11) ◽  
pp. 3057-3068 ◽  
Author(s):  
X. Wu ◽  
F. Babst ◽  
P. Ciais ◽  
D. Frank ◽  
M. Reichstein ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Patterns ◽  
Water Availability ◽  
Vegetation Index ◽  
Crop Yields ◽  
Spatiotemporal Patterns ◽  
Spatiotemporal Variability ◽  
Simultaneous Increase ◽  
Ecosystem Productivity ◽  
Terrestrial Productivity

Abstract. Quantifying the interannual variability (IAV) of the terrestrial ecosystem productivity and its sensitivity to climate is crucial for improving carbon budget predictions. In this context it is necessary to disentangle the influence of climate from impacts of other mechanisms underlying the spatiotemporal patterns of IAV of the ecosystem productivity. In this study we investigated the spatiotemporal patterns of IAV of historical observations of European crop yields in tandem with a set of climate variables. We further evaluated if relevant remote-sensing retrievals of NDVI (normalized difference vegetation index) and FAPAR (fraction of absorbed photosynthetically active radiation) depict a similar behaviour. Our results reveal distinct spatial patterns in the IAV of the analysed proxies linked to terrestrial productivity. In particular, we find higher IAV in water-limited regions of Europe (Mediterranean and temperate continental Europe) compared to other regions in both crop yield and remote-sensing observations. Our results further indicate that variations in the water balance during the active growing season exert a more pronounced and direct effect than variations of temperature on explaining the spatial patterns in IAV of productivity-related variables in temperate Europe. Overall, we observe a temporally increasing trend in the IAV of terrestrial productivity and an increasing sensitivity of productivity to water availability in dry regions of Europe during the 1975–2009 period. In the same regions, a simultaneous increase in the IAV of water availability was detected. These findings suggest intricate responses of carbon fluxes to climate variability in Europe and that the IAV of terrestrial productivity has become potentially more sensitive to changes in water availability in the dry regions in Europe. The changing sensitivity of terrestrial productivity accompanied by the changing IAV of climate is expected to impact carbon stocks and the net carbon balance of European ecosystems.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Sign in / Sign up

Export Citation Format

Share Document