An accuracy assessment of satellite-based cotton yield estimation using panel data regression
A case study of Uzbekistan
Bibliographic Data
| ID | 15112568 |
|---|---|
| Authors | Shovkat Khodjaev (0000-0002-8643-9502, Leibniz Institute of Agricultural Development in Transition Economies, corresponding author), Ihtiyor Bobojonov (0000-0003-2166-6234, Leibniz Institute of Agricultural Development in Transition Economies), Lena Kuhn (0000-0002-1453-0040, Leibniz Institute of Agricultural Development in Transition Economies), Thomas Glauben (0000-0003-0640-9387, Leibniz Institute of Agricultural Development in Transition Economies) |
| Year | 2024 |
| Volume | 28 |
| Issue | 3 |
| Pages | 6799-6830 |
| Publication date | 2024-07-27 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment Development and Sustainability (JOURNAL) |
| Journal identifiers | ISSN: 1387-585X • E-ISSN: 1573-2975 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s10668-024-05220-1 |
| OpenAlex | W4401046780 |
| Language | EN |
| References cited | 128 |
Satellite-based yield estimation is crucial for spotting potential deficits in crop yields at an early stage, supports farm-level decision-making and early-warning systems, and is a prerequisite for index insurance markets. Precise satellite-based yield estimations are already established for important food crops like maize and wheat. However, for many cash crops like cotton, the accuracy of satellite-based yield estimation has not been scientifically tested, mainly due to their low biomass-yield correlation. This paper contributes to exploring the suitability of multiple vegetation indices based on Sentinel-2 imagery to estimate farm-level yields for one of these cash crops, cotton. We estimated various vegetation indices conjugated with the cotton crop phenology for the selected study area and compared them with farm-level panel data (n = 232) for the years 2016–2018 obtained from a statistical agency in Uzbekistan. Overall, we tested the suitability of the Normalized Difference Vegetation Index, the Modified Soil Adjusted Vegetation Index 2, the Red-Edge Chlorophyll Index and the Normalized Difference Red-Edge Index (NDRE). Among these indices, the NDRE index shows the highest fit with the actual cotton yield data (R 2 up to 0.96, adj R 2 = 0.95 and RMSE = 0.21). These results indicate that the NDRE index is a powerful indicator for determining cotton yields. Based on this approach, farmers can monitor crop growth, which in turn avoids crop loss and thereby increases productivity. This research highlights that a satellite-based estimate of crop production can provide a unique perspective which should improve the possibility of identifying management priorities to improve agriculture productivity and mitigate climate impacts
Agricultural engineering · Agriculture · Agronomy · Cash crop · Crop yield · Economics · Estimation · Geography · Leaf area index · Normalized Difference Vegetation Index · Statistics · Computer Science · Engineering · Environmental Science · Land Use and Ecosystem Services · Leaf Properties and Growth Measurement · Mathematics · Remote Sensing in Agriculture
A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscape fragmentation
Landsat-8
High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform
Agricultural Decisions after Relaxing Credit and Risk Constraints
Informal Risk Sharing, Index Insurance, and Risk Taking in Developing Countries
Overview of the radiometric and biophysical performance of the Modis vegetation indices
Sentinel-2
A modified soil adjusted vegetation index
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter
Red and photographic infrared linear combinations for monitoring vegetation
Feasible crop insurance indexes for drought risk management in Northern Ethiopia
The Sustainable Development Goals Report 2019
Climate change impacts on crop productivity in Africa and South Asia
Spatial analysis of drought severity and magnitude using the standardized precipitation index and streamflow drought index over the Upper Indus Basin, Pakistan
Synergy of Sentinel-2A and Near-proximal sensor data for deriving biochemical parameters of paddy at different growth stages
Predicting land use allocation in France
What To Do (and Not to Do) with Time-Series Cross-Section Data
| Highly cited | No |
|---|