scholarly journals Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre, Spain

2020 ◽  
Vol 12 (14) ◽  
pp. 2278
Author(s):  
Joel Segarra ◽  
Jon González-Torralba ◽  
Íker Aranjuelo ◽  
Jose Luis Araus ◽  
Shawn C. Kefauver
Keyword(s):  
Grain Yield ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Suitable Model ◽  
Wheat Grain ◽  
Northern Spain ◽  
Spatial Factors ◽  
Topographic Features ◽  
Field Level ◽  
Sentinel 2

Reliable methods for estimating wheat grain yield before harvest could help improve farm management and, if applied on a regional level, also help identify spatial factors that influence yield. Regional grain yield can be estimated using conventional methods, but the typical process is complex and labor-intensive. Here we describe the development of a streamlined approach using publicly accessible agricultural data, field-level yield, and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield. We validated our method on wheat croplands in Navarre in northern Spain, which features heterogeneous topography and rainfall. First, this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices, which were measured at various phenological stages in order to determine the optimal timings. Second, the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images. We used a supervised pixel-based random forest classification and the estimates were compared to government-published post-harvest yield statistics. When tested, the model achieved an R2 of 0.83 in predicting grain yield at field level. The wheat parcels were mapped with an accuracy close to 86% for both overall accuracy and compared to official statistics. Third, the validated model was used to explore potential relationships of the mapped per-parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions. Topographic features and rainfall together accounted for an average for 11 to 20% of the observed spatial variation in grain yield in Navarre. These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale.

scholarly journals Regional Scale Mapping of Grassland Mowing Frequency with Sentinel-2 Time Series

2018 ◽  
Vol 10 (8) ◽  
pp. 1221 ◽  
Author(s):  
Natalia Kolecka ◽  
Christian Ginzler ◽  
Robert Pazur ◽  
Bronwyn Price ◽  
Peter Verburg
Keyword(s):  
Time Series ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Grassland Management ◽  
Management Activity ◽  
Field Campaigns ◽  
Grass Growth ◽  
Key Events ◽  
Global Carbon ◽  
Sentinel 2

Grassland use intensity is a topic of growing interest worldwide, as grasslands are integral in supporting biodiversity, food production, and regulating of the global carbon cycle. Data available for characterizing grasslands management are largely descriptive and collected from laborious field campaigns or questionnaires. The recent launch of the Sentinel-2 earth monitoring constellation provides new possibilities for high temporal and spatial resolution remote sensing data covering large areas. This study aims to evaluate the potential of a time series of Sentinel-2 data for mapping of mowing frequency in the region of Canton Aargau, Switzerland. We tested two cloud masking processes and three spatial mapping units (pixels, parcel polygons and shrunken parcel polygons), and investigated how missing data influence the ability to accurately detect and map grassland management activity. We found that more than 40% of the study area was mown before 15 June, while the remaining part was either mown later, or was not mown at all. The highest accuracy for detection of mowing events was achieved using additional clouds masking and size reduction of parcels, which allowed correct detection of 77% of mowing events. Additionally, we found that using only standard cloud masking leads to significant overestimation of mowing events, and that the detection based on sparse time series does not fully correspond to key events in the grass growth season.

scholarly journals Novel Vegetation Indices for Cotton Boll Opening Status Estimation Using Sentinel-2 Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1712 ◽  
Author(s):  
Yu Ren ◽  
Yanhua Meng ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Yuxing Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Great Influence ◽  
Remote Sensing Data ◽  
Area Ratio ◽  
Wide Field ◽  
Entire Area ◽  
Sentinel 2

The application of chemical harvest aids to defoliate leaves and ripen bolls plays a significant role in the once-over machine harvest of cotton (Gossypium hirsutum L.) fields. The boll opening rate (BOR) is a key indicator for the determination of harvest aid spraying times. However, the most commonly used method to determine BOR is manual investigation, which is subjective and cannot have a holistic judgment of the entire area. Remote sensing can be employed to overcome these limitations, due to a wide field of vision, acceptably spatial and temporal resolution, and rich spectral information beyond the perception of the human eye. The reflectance of open cotton bolls is relatively high in the visible and near-infrared bands. High reflectance of open bolls has a great influence on the reflectance of the mixed pixels on remote sensing imagery. Therefore, it is an effective method to detect boll opening status by constructing vegetation indices with the sensitive spectral bands of imagery. In this study, we proposed two new vegetation indices based on Sentinel-2 remote sensing data, namely, the boll area ratio index (BARI) and the boll opening rate index (BORI), in order to estimate the boll opening status on a regional scale. The proposed indices were strongly correlated with the boll area ratio (BAR) and BOR. In particular, BARI exhibited the most accurate and robust performance with BAR in the prediction (R2 = 0.754, RMSE = 2.56%) and validation (R2 = 0.706, RMSE = 5.00%) among all the indices, including published indices we chose. Furthermore, when comparing to all other indices, BORI demonstrated the best and satisfactory estimation with BOR in the prediction (R2 = 0.675, RMSE = 7.96%) and validation (R2 = 0.616, RMSE = 2.79%). Meanwhile, an exponential growth relationship between BOR and BAR was identified, and the underlying mechanisms behind this phenomenon were discussed. Overall, through our study, we provided convenient and accurate vegetation indices for the investigation of boll opening status in a cotton-producing area by accessible and free Sentinel-2 imagery.

scholarly journals New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

2021 ◽  
Vol 13 (15) ◽  
pp. 2892
Author(s):  
Zhongbing Chang ◽  
Sanaa Hobeichi ◽  
Ying-Ping Wang ◽  
Xuli Tang ◽  
Gab Abramowitz ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Aboveground Biomass ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Random Forest Regression ◽  
Multiple Datasets ◽  
Optimal Weighting ◽  
Two Hybrid ◽  
Forest Aboveground Biomass

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

Comparisons of sensors to predict spring Wheat grain yield and protein content

2021 ◽  
Author(s):  
Donald Veverka ◽  
Amitava Chatterjee ◽  
Melissa Carlson
Keyword(s):  
Grain Yield ◽  
Protein Content ◽  
Spring Wheat ◽  
Wheat Grain

Effect of weeds and nitrogen fertiliser on yield and grain protein concentration of wheat

1996 ◽  
Vol 36 (4) ◽  
pp. 443 ◽  
Author(s):  
MG Mason ◽  
RW Madin
Keyword(s):  
Grain Yield ◽  
Weed Control ◽  
Dry Matter ◽  
Protein Concentration ◽  
The Other ◽  
Grain Protein ◽  
Dry Matter Yield ◽  
Wheat Grain ◽  
Grain Protein Concentration ◽  
Nitrogen Fertiliser

Field trials at Beverley (19911, Salmon Gums (1991; 2 sites) and Merredin (1992; 2 sites), each with 5 rates of nitrogen (N) and 3 levels of weed control, were used to investigate the effect of weeds and N on wheat grain yield and protein concentration during 1991 and 1992. Weeds in the study were grasses (G) and broadleaf (BL). Weeds reduced both vegetative dry matter yield and grain yield of wheat at all sites except for dry matter at Merredin (BL). Nitrogen fertiliser increased wheat dry matter yield at all sites. Nitrogen increased wheat grain yield at Beverley and Merredin (BL), but decreased yield at both Salmon Gums sites in 1991. Nitrogen fertiliser increased grain protein concentration at all 5 sites-at all rates for 3 sites [Salmon Gums (G) and (BL) and Merredin (G)] and at rates of 69 kg N/ha or more at the other 2 sites [Beverley and Merredin (BL)]. However, the effect of weeds on grain protein varied across sites. At Merredin (G) protein concentration was higher where there was no weed control, possibly due to competition for soil moisture by the greater weed burden. At Salmon Gums (G), grain protein concentration was greater when weeds were controlled than in the presence of weeds, probably due to competition for N between crop and weeds. In the other 3 trials, there was no effect of weeds on grain protein. The effect of weeds on grain protein appears complex and depends on competition between crop and weeds for N and for water at the end of the season, and the interaction between the two.

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