Crop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information

Accurate monitoring of croplands helps in making decisions (for insurance claims, crop management and contingency plans) at the macro-level, especially in drylands where variability in cropping is very high owing to erratic weather conditions. Dryland cereals and grain legumes are key to ensuring the food and nutritional security of a large number of vulnerable populations living in the drylands. Reliable information on area cultivated to such crops forms part of the national accounting of food production and supply in many Asian countries, many of which are employing remote sensing tools to improve the accuracy of assessments of cultivated areas. This paper assesses the capabilities and limitations of mapping cultivated areas in the Rabi (winter) season and corresponding cropping patterns in three districts characterized by small-plot agriculture. The study used Sentinel-2 Normalized Difference Vegetation Index (NDVI) 15-day time-series at 10m resolution by employing a Spectral Matching Technique (SMT) approach. The use of SMT is based on the well-studied relationship between temporal NDVI signatures and crop phenology. The rabi season in India, dominated by non-rainy days, is best suited for the application of this method, as persistent cloud cover will hamper the availability of images necessary to generate clearly differentiating temporal signatures. Our study showed that the temporal signatures of wheat, chickpea and mustard are easily distinguishable, enabling an overall accuracy of 84%, with wheat and mustard achieving 86% and 94% accuracies, respectively. The most significant misclassifications were in irrigated areas for mustard and wheat, in small-plot mustard fields covered by trees and in fragmented chickpea areas. A comparison of district-wise national crop statistics and those obtained from this study revealed a correlation of 96%

ON THE APPLICATION OF REMOTE SENSING TIME SERIES ANALYSIS FOR LAND COVER MAPPING: SPECTRAL INDICES FOR CROPS CLASSIFICATION

Abstract. This study aims to introduce a semi-automatic classification workflow for the production of a land use/land cover (LULC) map of the island of Sardinia (Italy) following the CORINE legend schema, and a ground spatial resolution compatible with a scale of 1:25.000. The classification is based on free high-resolution satellite imagery from Sentinel-1 and Sentinel-2 collected in 2020, ancillary data derived from Sardinian Geoportal, Joint Research Centre (JRC) and OpenStreetMap. The LULC map production includes three steps: 1) pixel-based classification, realized with two different approaches, that use i) information derived from existing thematic maps eventually re-coded in case of incoherencies observed between datasets and/or satellite data products, and ii) spectral indices and parameter thresholds defined on the basis of multitemporal analysis; 2) segmentation of Sentinel-1 and 2 annual composites, and pre-labelling of segments with the pixel-based classified map, obtaining the preliminary map; 3) visual inspection procedure in order to confirm, or re-assign, classes to polygons. The accuracy of the preliminary map was tested in a sample area and on specific class of non-irrigated crops through ground truth data collected from a detailed photo-interpretation, estimating 97% of overall accuracy. The results show a great improvement from existing thematic maps in terms of detail, with the possibility of a yearly updating of the map via automatic processes. However, some limitations were found, due to the high fragmentation of Sardinian landscape and the high variety of crop types and agricultural practices, that could affect the efficiency of the classifier

Use and Improvement of Remote Sensing and Geospatial Technologies in Support of Crop Area and Yield Estimations in the West African Sahel

In arid and semi-arid West Africa, agricultural production and regional food security depend largely on small-scale subsistence farming and rainfed crops, both of which are vulnerable to climate variability and drought. Efforts made to improve crop monitoring and our ability to estimate crop production (areas planted and yield estimations by crop type) in the major agricultural zones of the region are critical paths for minimizing climate risks and to support food security planning. The main objective of this dissertation research was to contribute to these efforts using remote sensing technologies. In this regard, the first analysis documented the low reliability of existing land cover products for cropland area estimation (Chapter 2). Then two satellite remote sensing-based datasets were developed that 1) accurately map cropland areas in the five countries of Sahelian West Africa (Senegal, Mauritania, Mali, Burkina Faso and Niger; Chapter 3), and 2) focus on the country of Mali to identify the location and prevalence of the major subsistence crops (millet, sorghum, maize and non-irrigated rice; Chapter 4). The regional cropland area product is distributed as the West African Sahel Cropland area at 30 m (WASC30). The development of the new dataset involved high density training data (380,000 samples) developed by USGS in collaboration with CILSS for training about 200 locally optimized random forest (RF) classifiers using Landsat 8 surface reflectances and vegetation indices and the Google Earth Engine platform. WASC30 greatly improves earlier estimates through inclusion of cropland information for both rainfed and irrigated areas mapped with a class-specific accuracy of 79% across the West Africa Sahel. Used as a mask in crop monitoring systems, the new cropland area data could bring critical insights by reducing uncertainties in xv identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security. Keywords: Agricultural identification of croplands as crop growth condition metrics are extracted. WASC30 allowed us to derive detailed statistics on cultivated areas in the Sahel, at country and agroclimatic scales. Intensive agricultural zones were highlighted as well. The second dataset, mapping crop types for the country of Mali, is meant to separate signals of different crop types for improved crop yield estimation. The crop type map was used to derive detailed agricultural statistics (e.g. acreage by crop types, spatial distribution) at finer administrative scales than has previously been possible. The crop fraction information by crop type extracted from the map, gives additional details on farmers preferences by regions, and the natural adaptability of different crop types. The final analysis of this dissertation explores the use of ensemble machine learning techniques to predict maize yield in Mali (Chapter 5). Climate data (precipitation and temperature), and vegetation indices (Normalized Difference Vegetation Index, NDVI, the Enhanced Vegetation Index, EVI, and the Normalized Difference Water Index, NDWI) are used as predictors, while actual yields collected in 2017 by the Malian Ministry of Agriculture are the reference data. Random forest presented better predictive performance as compared to boosted regression trees (BRT). Results showed that climate variables have more predictive power for maize yield compared to vegetation indices. Among vegetation indices, the NDWI appeared to be the most influential predictor, maybe because of water requirement of maize and the sensitivity of this index to water in semi-arid regions. Tested with two different independent datasets, one constituted by 20% of the reference information, and another including observed yields for year 2018 (a one-year-left analysis), maize yield predictions were promising for year 2017 (RMSE = 362 kg/ha), but showed higher error for 2018 (RMSE = 707 kg/ha). That is, the fitted model may not capture accurately year to year variabilities in predicted maize yield. In this analysis, predictions were limited to field samples (~600 fields) across the country of Mali. It would be valuable in the future to predict maize yield for each pixel of the new developed crop type map. That will lead to a detailed spatial analysis of maize yield, allowing identification of low yielding regions for targeted interventions which could improve food security

Small-Scale Irrigation Mapping (SSIM) as a tool for improving and validating irrigated area maps: contextual approach and lessons learnt in Burkina Faso

Recent rapid expansion of private small-scale irrigation provides an opportunity to improve livelihoods and food security, but requires knowledge of where it is happening, in order to sustainably manage water use. Concerns are rising regarding the negative impacts of unchecked expansion of irrigation on downstream water quality and availability, particularly when using sub-optimal practices (de Fraiture et al. 2014; Domenech and Ringler 2013; Shah 2007). Therefore, for informed planning of potential sustainable irrigation expansion, policy makers and resource managers at the national level are interested in maps of the current extent of small-scale irrigation. Although several maps of irrigated areas have been produced for Burkina Faso, these maps, often of 250 meter (m), 300 m or 1 kilometer (km) resolution, are of too low resolution to account for scattered irrigation on areas smaller than 1 hae. Small-scale irrigation in Burkina Faso is typically carried out on individual plots of less than a quarter of hectare, with a small proportion on groups of fields no larger than one hectare, implying that existing maps are not reliably capturing the true extent and distribution of small-scale irrigation in the country

Comparative geospatial approach for agricultural crops identification in interfluvial plain - A case study of Sahiwal district, Pakistan

Agricultural crop cover identification is a major issue and time-consuming effort to verify the crop type through surveys of the individual field or using prehistoric methods. To establish the scenario of crop identification, the stage of crop provides diverse spatial information about the variety of crops due to its spectral changes. The main aim of this study was to the identify the crop types and their behavior using remote sensing and geographical information system-based approach. Moreover, two main methods were applied to the Sentinel-2 satellite data in which one is random forest based supervised classification and another was Normalize Difference Vegetation Index (NDVI) density estimation method through the google earth engine to procure the data in time-efficient way. This study also established the comparison between classified and vegetation index based seasonal compositional datasets for wheat, cotton, maize, and fodder crops. Study discussed the best fit technique for crops identification in the light of observed methods. Furthermore, the vegetation index ranges by the zonal statistics of the field samples were established according to crop precision. Results showed that -22.94, -43.72, 20.61, and 32.49 % dissimilarities existed in wheat, fodder, cotton, and maize results respectively, after comparison of both techniques. Although, the accuracy assessment was performed on the classified dataset for validation of results by confusion matrix accuracy assessment process using field sample data. Moreover, the vegetation index was used to evaluate crop land surface temperature to estimate the crop growth stage valuation that revealed noticeably enthralling outcomes. The results determined that the classified accuracies of wheat, cotton, maize and fodder were 84, 80, 81 and 71 % respectively. This study also revealed that the random forest classifier has used more features and information potentially during the classifier trainings but vegetation index just implies the limited number of features such as crop growing status

Monitoring crops water needs at high spatio-temporal resolution by synergy of optical/thermal and radar observations

L'optimisation de la gestion de l'eau en agriculture est essentielle dans les zones semi-arides afin de préserver les ressources en eau qui sont déjà faibles et erratiques dues à des actions humaines et au changement climatique. Cette thèse vise à utiliser la synergie des observations de télédétection multispectrales (données radar, optiques et thermiques) pour un suivi à haute résolution spatio-temporelle des besoins en eau des cultures. Dans ce contexte, différentes approches utilisant divers capteurs (Landsat-7/8, Sentinel-1 et MODIS) ont été developpées pour apporter une information sur l'humidité du sol (SM) et le stress hydrique des cultures à une échelle spatio-temporelle pertinente pour la gestion de l'irrigation. Ce travail va parfaitement dans le sens des objectifs du projet REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) qui visent à estimer l'humidité du sol dans la zone racinaire (RZSM) afin d'optimiser la gestion de l'eau d'irrigation. Des approches innovantes et prometteuses sont mises en place pour estimer l'évapotranspiration (ET), RZSM, la température de surface du sol (LST) et le stress hydrique de la végétation à travers des indices de SM dérivés des observations multispectrales à haute résolution spatio-temporelle. Les méthodologies proposées reposent sur des méthodes basées sur l'imagerie, la modélisation du transfert radiatif et la modélisation du bilan hydrique et d'énergie et sont appliquées dans une région à climat semi-aride (centre du Maroc). Dans le cadre de ma thèse, trois axes ont été explorés. Dans le premier axe, un indice de RZSM dérivé de LST-Landsat est utilisé pour estimer l'ET sur des parcelles de blé et des sols nus. L'estimation par modélisation de ET a été explorée en utilisant l'équation de Penman-monteith modifiée obtenue en introduisant une relation empirique simple entre la résistance de surface (rc) et l'indice de RZSM. Ce dernier est estimé à partir de la température de surface (LST) dérivée de Landsat, combinée avec les températures extrêmes (en conditions humides et sèches) simulée par un modèle de bilan d'énergie de surface piloté par le forçage météorologique et la fraction de couverture végétale dérivée de Landsat. La méthode utilisée est calibrée et validée sur deux parcelles de blé situées dans la même zone près de Marrakech au Maroc. Dans l'axe suivant, une méthode permettant de récupérer la SM de la surface (0-5 cm) à une résolution spatiale et temporelle élevée est développée à partir d'une synergie entre données radar (Sentinel-1) et thermique (Landsat) et en utilisant un modèle de bilan d'énergie du sol. L'approche développée a été validée sur des parcelles agricoles en sol nu et elle donne une estimation précise de la SM avec une différence quadratique moyenne en comparant à la SM in situ, égale à 0,03 m3 m-3. Dans le dernier axe, une nouvelle méthode est développée pour désagréger la MODIS LST de 1 km à 100 m de résolution en intégrant le SM proche de la surface dérivée des données radar Sentinel-1 et l'indice de végétation optique dérivé des observations Landsat. Le nouvel algorithme, qui inclut la rétrodiffusion S-1 en tant qu'entrée dans la désagrégation, produit des résultats plus stables et robustes au cours de l'année sélectionnée. Dont, 3,35 °C était le RMSE le plus bas et 0,75 le coefficient de corrélation le plus élevé évalués en utilisant le nouvel algorithme.Optimizing water management in agriculture is essential over semi-arid areas in order to preserve water resources which are already low and erratic due to human actions and climate change. This thesis aims to use the synergy of multispectral remote sensing observations (radar, optical and thermal data) for high spatio-temporal resolution monitoring of crops water needs. In this context, different approaches using various sensors (Landsat-7/8, Sentinel-1 and MODIS) have been developed to provide information on the crop Soil Moisture (SM) and water stress at a spatio-temporal scale relevant to irrigation management. This work fits well the REC "Root zone soil moisture Estimates at the daily and agricultural parcel scales for Crop irrigation management and water use impact: a multi-sensor remote sensing approach" (http://rec.isardsat.com/) project objectives, which aim to estimate the Root Zone Soil Moisture (RZSM) for optimizing the management of irrigation water. Innovative and promising approaches are set up to estimate evapotranspiration (ET), RZSM, land surface temperature (LST) and vegetation water stress through SM indices derived from multispectral observations with high spatio-temporal resolution. The proposed methodologies rely on image-based methods, radiative transfer modelling and water and energy balance modelling and are applied in a semi-arid climate region (central Morocco). In the frame of my PhD thesis, three axes have been investigated. In the first axis, a Landsat LST-derived RZSM index is used to estimate the ET over wheat parcels and bare soil. The ET modelling estimation is explored using a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (rc) and a RZSM index. The later is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The investigated method is calibrated and validated over two wheat parcels located in the same area near Marrakech City in Morocco. In the next axis, a method to retrieve near surface (0-5 cm) SM at high spatial and temporal resolution is developed from a synergy between radar (Sentinel-1) and thermal (Landsat) data and by using a soil energy balance model. The developed approach is validated over bare soil agricultural fields and gives an accurate estimates of near surface SM with a root mean square difference compared to in situ SM equal to 0.03 m3 m-3. In the final axis a new method is developed to disaggregate the 1 km resolution MODIS LST at 100 m resolution by integrating the near surface SM derived from Sentinel-1 radar data and the optical-vegetation index derived from Landsat observations. The new algorithm including the S-1 backscatter as input to the disaggregation, produces more stable and robust results during the selected year. Where, 3.35 °C and 0.75 were the lowest RMSE and the highest correlation coefficient assessed using the new algorithm