A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning
The West African Sahel Cropland map (WASC30) is a new 30-m cropland extent product for the nominal year of 2015. We used the computing resources provided by Google Earth Engine (GEE) to fit and apply Random Forest models for cropland detection in each of 189 grid cells (composed of 100 km<sup>...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/9/1436 |
| _version_ | 1797569147221049344 |
|---|---|
| author | Kaboro Samasse Niall P. Hanan Julius Y. Anchang Yacouba Diallo |
| author_facet | Kaboro Samasse Niall P. Hanan Julius Y. Anchang Yacouba Diallo |
| author_sort | Kaboro Samasse |
| collection | DOAJ |
| description | The West African Sahel Cropland map (WASC30) is a new 30-m cropland extent product for the nominal year of 2015. We used the computing resources provided by Google Earth Engine (GEE) to fit and apply Random Forest models for cropland detection in each of 189 grid cells (composed of 100 km<sup>2</sup>, hence a total of ~1.9 × 10<sup>6</sup> km<sup>2</sup>) across five countries of the West African Sahel (Burkina Faso, Mauritania, Mali, Niger, and Senegal). Landsat-8 surface reflectance (Bands 2–7) and vegetation indices (NDVI, EVI, SAVI, and MSAVI), organized to include dry-season and growing-season band reflectances and vegetation indices for the years 2013–2015, were used as predictors. Training data were derived from an independent, high-resolution, visually interpreted sample dataset that classifies sample points across West Africa using a 2-km grid (~380,000 points were used in this study, with 50% used for model training and 50% used for model validation). Analysis of the new cropland dataset indicates a summed cropland area of ~316 × 10<sup>3</sup> km<sup>2</sup> across the 5 countries, primarily in rainfed cropland (309 × 10<sup>3</sup> km<sup>2</sup>), with irrigated cropland area (7 × 10<sup>3</sup> km<sup>2</sup>) representing 2% of the total cropland area. At regional scale, the cropland dataset has an overall accuracy of 90.1% and a cropland class (rainfed and irrigated) user’s accuracy of 79%. At bioclimatic zones scale, results show that land proportion occupied by rainfed agriculture increases with annual precipitation up to 1000 mm. The Sudanian zone (600–1200 mm) has the highest proportion of land in agriculture (24%), followed by the Sahelian (200–600 mm) and the Guinean (1200 +) zones for 15% and 4%, respectively. The new West African Sahel dataset is made freely available for applications requiring improved cropland area information for agricultural monitoring and food security applications. |
| first_indexed | 2024-03-10T20:06:46Z |
| format | Article |
| id | doaj.art-b0dbee5589bb4c6da5a9c8dfc579a55e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:06:46Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b0dbee5589bb4c6da5a9c8dfc579a55e2023-11-19T23:15:35ZengMDPI AGRemote Sensing2072-42922020-05-01129143610.3390/rs12091436A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine LearningKaboro Samasse0Niall P. Hanan1Julius Y. Anchang2Yacouba Diallo3Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USAPlant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003, USAPlant and Environmental Sciences, New Mexico State University, Las Cruces, NM 88003, USAIPR/IFRA, BP 06 Koulikoro, MaliThe West African Sahel Cropland map (WASC30) is a new 30-m cropland extent product for the nominal year of 2015. We used the computing resources provided by Google Earth Engine (GEE) to fit and apply Random Forest models for cropland detection in each of 189 grid cells (composed of 100 km<sup>2</sup>, hence a total of ~1.9 × 10<sup>6</sup> km<sup>2</sup>) across five countries of the West African Sahel (Burkina Faso, Mauritania, Mali, Niger, and Senegal). Landsat-8 surface reflectance (Bands 2–7) and vegetation indices (NDVI, EVI, SAVI, and MSAVI), organized to include dry-season and growing-season band reflectances and vegetation indices for the years 2013–2015, were used as predictors. Training data were derived from an independent, high-resolution, visually interpreted sample dataset that classifies sample points across West Africa using a 2-km grid (~380,000 points were used in this study, with 50% used for model training and 50% used for model validation). Analysis of the new cropland dataset indicates a summed cropland area of ~316 × 10<sup>3</sup> km<sup>2</sup> across the 5 countries, primarily in rainfed cropland (309 × 10<sup>3</sup> km<sup>2</sup>), with irrigated cropland area (7 × 10<sup>3</sup> km<sup>2</sup>) representing 2% of the total cropland area. At regional scale, the cropland dataset has an overall accuracy of 90.1% and a cropland class (rainfed and irrigated) user’s accuracy of 79%. At bioclimatic zones scale, results show that land proportion occupied by rainfed agriculture increases with annual precipitation up to 1000 mm. The Sudanian zone (600–1200 mm) has the highest proportion of land in agriculture (24%), followed by the Sahelian (200–600 mm) and the Guinean (1200 +) zones for 15% and 4%, respectively. The new West African Sahel dataset is made freely available for applications requiring improved cropland area information for agricultural monitoring and food security applications.https://www.mdpi.com/2072-4292/12/9/1436agricultural land areaSahelWest Africamachine learningEarth Engine |
| spellingShingle | Kaboro Samasse Niall P. Hanan Julius Y. Anchang Yacouba Diallo A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning Remote Sensing agricultural land area Sahel West Africa machine learning Earth Engine |
| title | A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning |
| title_full | A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning |
| title_fullStr | A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning |
| title_full_unstemmed | A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning |
| title_short | A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning |
| title_sort | high resolution cropland map for the west african sahel based on high density training data google earth engine and locally optimized machine learning |
| topic | agricultural land area Sahel West Africa machine learning Earth Engine |
| url | https://www.mdpi.com/2072-4292/12/9/1436 |
| work_keys_str_mv | AT kaborosamasse ahighresolutioncroplandmapforthewestafricansahelbasedonhighdensitytrainingdatagoogleearthengineandlocallyoptimizedmachinelearning AT niallphanan ahighresolutioncroplandmapforthewestafricansahelbasedonhighdensitytrainingdatagoogleearthengineandlocallyoptimizedmachinelearning AT juliusyanchang ahighresolutioncroplandmapforthewestafricansahelbasedonhighdensitytrainingdatagoogleearthengineandlocallyoptimizedmachinelearning AT yacoubadiallo ahighresolutioncroplandmapforthewestafricansahelbasedonhighdensitytrainingdatagoogleearthengineandlocallyoptimizedmachinelearning AT kaborosamasse highresolutioncroplandmapforthewestafricansahelbasedonhighdensitytrainingdatagoogleearthengineandlocallyoptimizedmachinelearning AT niallphanan highresolutioncroplandmapforthewestafricansahelbasedonhighdensitytrainingdatagoogleearthengineandlocallyoptimizedmachinelearning AT juliusyanchang highresolutioncroplandmapforthewestafricansahelbasedonhighdensitytrainingdatagoogleearthengineandlocallyoptimizedmachinelearning AT yacoubadiallo highresolutioncroplandmapforthewestafricansahelbasedonhighdensitytrainingdatagoogleearthengineandlocallyoptimizedmachinelearning |