SOYBEAN CROP YIELD PREDICTION BY INTEGRATION OF REMOTE SENSING AND WEATHER OBSERVATIONS Article Swipe
Jayantrao Mohite
,
Suryakant Sawant
,
Ankur Pandit
,
Rishabh Agrawal
,
Srinivasu Pappula
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-197-2023
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-197-2023
The main objective of this study is the in-season forecasting of soybean crop yield using the integration of satellite remote sensing and weather observations. The study was carried out in the Paran´a state of Brazil. The soybean crop in the study region is sown during Oct.–Nov. month and harvested between Feb.–Mar. of the next year. Municipality-level soybean yield data for 15 municipalities was obtained from the AGROLINK portal of Brazil, from the 2005–06 season to the 2020–21 season. The crop yield data constituted yearly municipality-wise yield in kg/ha. Remote sensing-based indicators such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST), and Rainfall data from CHIRPS was considered in the study. Regression modelling was carried out between municipality-level yield as the dependent variable and features generated from remote sensing and weather observations as independent variables. Performance evaluation of tuned random forest regression (RFR) and tuned support vector regression (SVR) were performed against multiple linear regression (MLR). A comparison of results in terms of algorithms shows that RFR performed better than SVR and MLR. Further, a rootmean- square-error (RMSE) of 414 kg/ha and an R2 value of 0.748 were achieved by the best RFR model. Validation of developed RFR model was performed on the data from the new soybean season, i.e., 2020–21. We have achieved an R2 value of 0.693 with a RMSE of 585 kg/ha. Although the model performance on the data of 2020-21 season is slightly reduced, R2 and RMSE are in good agreement with test results. This study showed that, integration of remote sensing and weather observations would be useful for in-season yield forecasting of soybean at municipality level.
Related Topics
Concepts
Normalized Difference Vegetation Index
Vegetation (pathology)
Linear regression
Yield (engineering)
Random forest
Regression analysis
Growing season
Crop
Crop yield
Mean squared error
Regression
Mathematics
Support vector machine
Environmental science
Geography
Remote sensing
Statistics
Agronomy
Leaf area index
Forestry
Machine learning
Computer science
Biology
Medicine
Metallurgy
Materials science
Pathology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-197-2023
- https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/197/2023/isprs-archives-XLVIII-M-1-2023-197-2023.pdf
- OA Status
- diamond
- Cited By
- 7
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366775282
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4366775282Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-197-2023Digital Object Identifier
- Title
-
SOYBEAN CROP YIELD PREDICTION BY INTEGRATION OF REMOTE SENSING AND WEATHER OBSERVATIONSWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-21Full publication date if available
- Authors
-
Jayantrao Mohite, Suryakant Sawant, Ankur Pandit, Rishabh Agrawal, Srinivasu PappulaList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-197-2023Publisher landing page
- PDF URL
-
https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/197/2023/isprs-archives-XLVIII-M-1-2023-197-2023.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/197/2023/isprs-archives-XLVIII-M-1-2023-197-2023.pdfDirect OA link when available
- Concepts
-
Normalized Difference Vegetation Index, Vegetation (pathology), Linear regression, Yield (engineering), Random forest, Regression analysis, Growing season, Crop, Crop yield, Mean squared error, Regression, Mathematics, Support vector machine, Environmental science, Geography, Remote sensing, Statistics, Agronomy, Leaf area index, Forestry, Machine learning, Computer science, Biology, Medicine, Metallurgy, Materials science, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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