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 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.

Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

2019 ◽  
Vol 55 (9) ◽  
pp. 1329-1337
Author(s):  
N. V. Gopp ◽  
T. V. Nechaeva ◽  
O. A. Savenkov ◽  
N. V. Smirnova ◽  
V. V. Smirnov
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Soil Properties ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals Statistical assessment of vegetation dynamics within protected areas using remote sensing data

2013 ◽  
Vol 43 (4) ◽  
pp. 5
Author(s):  
Maria Elena Menconi ◽  
David Grohmann
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Photosynthetic Activity ◽  
Remote Sensing Data ◽  
Ratio Method ◽  
Vegetation Development ◽  
Sensing Data ◽  
Landscape Factors

This study aimed to test the effectiveness of protected areas to preserve vegetation. The first step was to identify vegetation suitable areas, designed as areas with optimal morphological terrain features for a good photosynthetic activity. These areas were defined according to the following landscape factors: slope, altitude, aspect and land use. Enhanced vegetation index (EVI) was chosen as vegetation dynamics indicator. This method is based on a statistical approach using remote sensing data in a geographic information system (GIS) environment. The correlation between EVI and landscape factor was evaluated using the frequency ratio method. Classes of landscape factors that show good correlation with a high EVI were combined to obtain vegetation suitable areas. Once identified, these areas and their vegetation dynamics were analysed by comparing the results obtained whenever these areas are included or not included in protected areas. A second EVI dataset was used to verify the accuracy in identifying vegetation suitable areas and the influence of each landscape factor considered in their identification. This validation process showed that vegetation suitable areas are significant in identifying areas with good photosynthetic activity. The effects analysis showed a positive influence of all landscape factors in determining suitability. This methodology, applied to central regions of Italy, shows that the vegetation suitable areas located inside protected areas are <em>greener</em> than those outside protected areas. This suggests that the protective measures established by the institution of the parks have proved to be effective, at least as far as the status of vegetation development is concerned.

scholarly journals Tree height inversion combining light detection and ranging and optical remote sensing data

2020 ◽  
Vol 165 ◽  
pp. 03020
Author(s):  
Kunlin Wang ◽  
Yi Ma ◽  
Fangrong Zhou
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Edge Length ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Inversion Model ◽  
Feature Parameters

Tree barriers in transmission line corridors are an important safety hazard.Scientific prediction of tree height and monitoring tree height changes are essential to solve this hidden danger. In this paper, the advantages of airborne lidar and optical remote sensing data are combined to research the method of tree height inversion. Based on glas data of lidar,waveform parameters such as waveform length, waveform leading edge length and waveform trailing edge length were extracted from waveform data by gaussian decomposition method.Terrain feature parameters were extracted from aster gdem data.The tree crown information was extracted from the optical remote sensing image by means of the mean shift algorithm. Then extract the vegetation index with high correlation with tree height.Finally, the extracted waveform feature parameters, topographic feature parameters, and crown index and vegetation index with high correlation are used as model input variables. The tree height inversion model was established using four regression methods, including multiple linear regression (mlr), support vector machine (svm), random forest (rf), and bp neural network (bpnn). The accuracy evaluation was conducted, and it was concluded that the tree height inversion model based on random forest obtained the best accuracy effect.

scholarly journals OPEN: A New Estimation of Global Ocean Heat Content for Upper 2000 Meters from Remote Sensing Data

2020 ◽  
Vol 12 (14) ◽  
pp. 2294
Author(s):  
Hua Su ◽  
Haojie Zhang ◽  
Xupu Geng ◽  
Tian Qin ◽  
Wenfang Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Network Architecture ◽  
Climate Changes ◽  
Heat Content ◽  
Remote Sensing Data ◽  
Ocean Heat Content ◽  
Global Ocean ◽  
Coefficient Of Determination ◽  
Sensing Data

Retrieving information concerning the interior of the ocean using satellite remote sensing data has a major impact on studies of ocean dynamic and climate changes; however, the lack of information within the ocean limits such studies about the global ocean. In this paper, an artificial neural network, combined with satellite data and gridded Argo product, is used to estimate the ocean heat content (OHC) anomalies over four different depths down to 2000 m covering the near-global ocean, excluding the polar regions. Our method allows for the temporal hindcast of the OHC to other periods beyond the 2005–2018 training period. By applying an ensemble technique, the hindcasting uncertainty could also be estimated by using different 9-year periods for training and then calculating the standard deviation across six ensemble members. This new OHC product is called the Ocean Projection and Extension neural Network (OPEN) product. The accuracy of the product is accessed using the coefficient of determination (R2) and the relative root-mean-square error (RRMSE). The feature combinations and network architecture are optimized via a series of experiments. Overall, intercomparison with several routinely analyzed OHC products shows that the OPEN OHC has an R2 larger than 0.95 and an RRMSE of <0.20 and presents notably accurate trends and variabilities. The OPEN product can therefore provide a valuable complement for studies of global climate changes.

scholarly journals Rapid primary productivity changes in one of the last coastal rainforests: the case of Kahua, Solomon Islands

2009 ◽  
Vol 36 (3) ◽  
pp. 253-260 ◽  
Author(s):  
IRENE GARONNA ◽  
IOAN FAZEY ◽  
MOLLY E. BROWN ◽  
NATHALIE PETTORELLI
Keyword(s):  
Remote Sensing ◽  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Solomon Islands ◽  
Remote Sensing Data ◽  
Human Populations ◽  
Sensing Data ◽  
Relative Contribution ◽  
Indirect Impacts

SUMMARYThe growth of human populations has many direct and indirect impacts on tropical forest ecosystems both locally and globally. This is particularly true in the Solomon Islands, where coastal rainforest cover still remains, but where climate change and a growing human population is putting increasing pressure on ecosystems. This study assessed recent primary productivity changes in the Kahua region (Makira, Solomon Islands) using remote sensing data (normalized difference vegetation index, NDVI). In this area, there has been no commercial logging and there is no existing information about the state of the forests. Results indicate that primary productivity has been decreasing in recent years, and that the recent changes are more marked near villages. Multiple factors may explain the reported pattern in primary productivity. The study highlights the need to (1) assess how accurately remote sensing data-based results match field data on the ground; (2) identify the relative contribution of the climatic, socioeconomic and political drivers of such changes; and (3) evaluate how primary productivity changes affect biodiversity level, ecosystem functioning and human livelihoods.

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