Field-scale crop monitoring is essential for agricultural management and policy making for food security and sustainability. Automating crop classification process while elaborating a workflow is a key step for reliable and precise crop mapping. This study aims to develop an approach for crop classification in the state of Rio Grande do Sul, Brazil, following the specific goals of i) evaluating spatial satellite-based features to guide crop data collection; ii) testing transfer learning model with subsequent growing season data; iii) examining accuracy in early-season prediction model; and lastly, iv) developing a crop classification model for estimating large scale crop area. As main data inputs, Sentinel-2, Sentinel-1, and Shuttle Radar Topographic Mission (SRTM) Digital Elevation data were used to extract features to input in the Random Forest classifier. Spatial variability of satellite features was evaluated using Moran’s I Index and cluster k-means. Crop area prediction data were obtained at municipality level to compare with census data (standard method). A crop summer map layer was generated for three major crops: soybeans (Glycine max L.), corn (Zea mays L.), and rice (Oryza sativa L.) in the state of Rio Grande do Sul, Brazil. The crop classification model achieved an overall accuracy of 0.95. Model performance was influenced by sample size and spatial variability of the samples. The random forest model was transferred to the next growing season with 0.89 and 0.91 overall accuracy for 250 and 750 samples, respectively. However, overall accuracy increased from 0.93 to 0.95 when 50 to 250 samples of same-year data was aggregated to the model. Similar accuracy was obtained for predictions done with data until March relative to when the entire season was considered, until May. When data for more growing seasons were aggregated, the model produced more accurate early season predictions (January and February). Soybean prediction area obtained the highest performance (R2 = 0.94), relative to rice (R2 = 0.90) and corn (R2 = 0.37). The rice prediction area presented a high precision, but the crop area was overestimated due to errors with wetland target relative to other class. Lastly, this study presents the first crop map layer of the three major field crops for the state of Rio Grande do Sul, Brazil, serving as a foundation for the creation of crop type maps for other states in the country and around the globe.

Satellite-based data fusion crop type classification and mapping in Rio Grande do Sul, Brazil

Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops—maize and potato—and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize–cereals and potato–maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize–legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services.

Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery

Sensing and measuring meteorological and physiological parameters of humans, animals, and plants are necessary to understand the complex interactions that occur between atmospheric processes and the health of the living organisms. Advanced sensing technologies have provided both meteorological and biological data across increasingly vast spatial, spectral, temporal, and thematic scales. Information and communication technologies have reduced barriers to data dissemination, enabling the circulation of information across different jurisdictions and disciplines. Due to the advancement and rapid dissemination of these technologies, a review of the opportunities for sensing the health effects of weather and climate change is necessary. This paper provides such an overview by focusing on existing and emerging technologies and their opportunities and challenges for studying the health effects of weather and climate change on humans, animals, and plants.

Technological opportunities for sensing of the health effects of weather and climate change: a state-of-the-art-review

Lagos, Nigeria’s economic hub and one of Africa’s fastest-growing cities, has experienced remarkable urban growth due to rapid urbanization. The city has recently faced persistent urban flooding du...

Analyzing urban growth and land cover change scenario in Lagos, Nigeria using multi-temporal remote sensing data and GIS to mitigate flooding

Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapping spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit trees mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well suited for accurate smallholder fruit plantation mapping.

https://www.mdpi.com/2072-4292/14/11/2621/pdf?version=1653986675

Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes

A timely and accurate crop type mapping is very significant, and a prerequisite for agricultural regions and ensuring global food security. The combination of remotely sensed optical and radar datasets presents an opportunity for acquiring crop information at relative spatial resolution and temporal resolution adequately to capture the growth profiles of various crop species. In this paper, we employed Sentinel-1A (S-1) and Sentinel-2A (S-2) data acquired between the end of June and early September 2016, on a semi-arid area in northern Nigeria. A different set of (VV and VH) SAR and optical (SI and SB) images, illustrating crop phenological development stage, were employed as inputs to the two machines learning Random Forest (RF) and Support Vector Machine (SVM) algorithms to automatically map maize fields. Significant increases in overall classification were shown when the multi-temporal spectral indices (SI) and spectral band (SB) datasets were added with the different integration of SAR datasets (i.e., VV and VH). The best overall accuracy (OA) for maize (96.93%) was derived by using RF classification algorithms with SI-SB-SAR datasets, although the SI datasets for RF and SB datasets for SVM also produced high overall maize classification accuracies, of 97.04% and 97.44%. The outcomes indicate the robustness of the RF or SVM methods to produce high-resolution maps of maize for subsequent application from agronomists, policy planners, and the government, because such information is lacking in our study area.

Kamal Abdelrahim Mohamed Shuka

Papers

Mapping Maize Fields by Using Multi-Temporal Sentinel-1A and Sentinel-2A Images in Makarfi, Northern Nigeria, Africa