Wheat and barley discrimination using sar and optical satellite images. Case study: Coronel Rosales department (Argentina)

El partido de Coronel Rosales (Buenos Aires, Argentina) se halla localizado dentro de la región pampeana austral, una de las de mayor relevancia agro productiva del país. En este contexto, el conocimiento de la superficie cultivada adquiere significativa importancia para la posterior planificación agrícola y económica. En tal sentido, la discriminación de cultivos mediante teledetección se dificulta cuando se trata de los de ciclo fenológico muy similar, como el trigo y la cebada. En este estudio se realizó una discriminación de dichos cultivos empleando imágenes de Radar de Apertura Sintética (SAR) Sentinel-1A SLC, imágenes ópticas Sentinel-2 y una combinación de ambos tipos de datos. Se incorporaron medidas de coherencia, textura e intensidad de retrodispersión extraídas de los datos SAR durante el ciclo fenológico completo. Sobre cada escena Sentinel-2 se obtuvo el Índice de Diferencia Normalizada de Vegetación (Normalized Difference Vegetation Index - NDVI). Se emplearon tres algoritmos de clasificación: Máxima Verosimilitud (Maximum Likelihood - MLC), Máquinas de Soporte Vectorial (Support Vector Machines - SVM) y Random Forest (RF). Los mejores resultados se obtuvieron al combinar imágenes ópticas y SAR empleando el clasificador RF. La combinación de las retrodispersiones VV y VH junto a la coherencia y la textura de las imágenes SAR, sumada al apilado de NDVI de imágenes ópticas, arrojó los máximos valores de precisión de la clasificación. El valor de F1 fue de 87.27% para el trigo y de 89.20% para la cebada.TIn Argentina, the farming industry is considered one of the main economic resources in terms of income and domestic market supply. Thus, the study, inventory, and knowledge of the cultivated surface area are key cornerstones for agricultural and economic planning. Agriculture focuses mainly on cereals such as wheat, barley, maize, oat, and sor-ghum, as well as on oilseeds such as soybeans, sunflower, and peanuts. The most important productive areas of Argentina include the Pampean region, where the Coronel Rosales Department is located (Buenos Aires, Argentina). In this context, the knowledge of the cultivated surface area is particularly important to support agricultural and economic planning. In this regard, crop discrimination based on remote sensing is difficult for crops with highly similar phenological cycles, as is the case of wheat and barley. To address this issue, the standard satellite image classification methods have been based on the spectral response of each individual pixel using optical images. Crops are also monitored using Synthetic Aperture Ra-dar (SAR) images; these have several advantages over optical imagery because radio waves are unaffected by the presence of clouds. This provides the benefit of re-cording satellite data throughout the whole phenological cycle.EEA BordenaveFil: Marini, Mario Fabián. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bordenave. Agencia Extensión Rural Bahía Blanca; Argentina

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world

Below the radar: Data, narratives and the politics of irrigation in sub-Saharan Africa

Emerging narratives call for recognising and engaging constructively with small-scale farmers who have a leading role in shaping the current irrigation dynamics in sub-Saharan Africa. This paper explores whether new irrigation data can usefully inform these narratives. It argues that, for a variety of reasons, official irrigation data in sub-Saharan Africa fail to capture the full extent and diverse nature of irrigation and its rapid distributed growth over the last two decades. The paper investigates recent trends in the use of remote sensing methods to generate irrigation data; it examines the associated expectation that these techniques enable a better understanding of current irrigation developments and small-scale farmers' roles. It reports on a pilot study that uses radar-based imagery and analysis to provide new insights into the extent of rice irrigated agriculture in three regions of Tanzania. We further stress that such mapping exercises remain grounded in a binary logic that separates 'irrigation' from other 'non-irrigated' landscape features. They can stem from, and reinforce, a conventional understanding of irrigation that is still influenced by colonial legacies of engineering design and agricultural modernisation. As farmers' initiatives question this dominant view of irrigation, and in a policy context that is dominated by narratives of water scarcity, this means that new data may improve the visibility of water use by small-scale irrigators but may † Jean-Philippe Venot and Sam Bowers are joint first authors. General comments and other correspondence should be addressed to Jean-Philippe Venot. Correspondence on remote sensing radar analysis should be addressed to Sam Bowers. Water Alternatives-2021 Volume 14 | Issue 2 Venot et al.: The politics of irrigation data in Sub-Saharan Africa 547 also leave them more exposed to restrictions favouring more powerful water users. The paper thus calls for moving away from a narrow debate on irrigation data and monitoring, and towards a holistic discussion of the nature of irrigation development in sub-Saharan Africa. This discussion is necessary to support a constructive engagement with farmer-led irrigation development; it is also challenging in that it involves facing entrenched vested interests and requires changes in development practices

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen Wellenlängenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementären Fernerkundungssystemen zählen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. Für die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie Wellenlänge, Einfallswinkel, Radarrückstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementäre Beitrag der Radarfernerkundung zur Wachstumsüberwachung bewertet. Hierzu wurden zwei Testgebiete, eines für Winterweizen in der Nordchinesischen Tiefebene und eines für Reis im Nordosten Chinas ausgewählt. In beiden Gebieten wurden während der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern während der Satellitenüberflüge oder zeitnah dazu durchgeführt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die Sensitivität hyperspektraler Reflexion und Radarrückstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. Für die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band Rückstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementären Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten für die Parameterableitung kombiniert eingesetzt. Das Hauptziel für das Reisanbaugebiet im Nordosten Chinas ist das Verständnis über die kohärente Dualpolarimetrische X-Band Rückstreuung zu verschiedenen phänologischen Wachstumsstadien. Hierfür werden die gleichpolarisierte TerraSAR-X Rückstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementären Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von Heterogenitäten in den Beständen ermöglichen. Die Synergien diesbezüglich müssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur Verfügung stehen werden und das Umweltmanagement weiter verbessern können

Télédétection radar appliquée au suivi des rizières. Méthodes utilisant le rapport des intensités de rétrodiffusion.

Because of the importance of rice in global food security and of the role of rice paddies in methane emissions, a large-scale near-real-time monitoring system of rice fields appears to be particularly useful. The objective of this work is to develop methods aiming at an effective use of remote sensing data from past and future satellites for rice fields monitoring. Radar imagery is preferred to optical imagery, due to its all-weather ability. Two methods are considered, both involving a C-band SAR intensity ratio as a classification feature: the HH/VV polarization ratio and the co-polarized temporal change HHdate2/HHdate1. First, a statistical study of intensity ratios is done, leading to the development of an error model that estimates the performance of the classification methods. The error model is also used to assess the impact of SAR system parameters (calibration, ambiguity ratio, revisit frequency) on the classification accuracy. Then, these classification methods are applied to two datasets acquired by the ASAR instrument onboard ENVISAT over the Mekong Delta, Vietnam, in order to map rice fields at two scales. The first approach relies on the use of the HH/VV polarization ratio calculated from the Alternating Polarization mode of ASAR, and is applied to produce a rice map covering one province in the delta. The second approach uses the HH temporal change of Wide-Swath mode images from ASAR, and allows mapping rice fields over the whole delta. Both methods are validated with success through the use of the cultivated areas reported in national statistics.En raison de l'importance du riz dans l'alimentation mondiale et du rôle des rizières dans les émissions de méthane, un suivi à grande échelle et en temps quasi-réel des surfaces cultivées en riz semble particulièrement utile. L'objectif de cette thèse est de développer des méthodes permettant une utilisation effective des données de télédétection des satellites présents et futurs pour le suivi des rizières. L'imagerie radar est privilégiée car elle permet des acquisitions sous toutes les conditions météorologiques, contrairement à l'imagerie optique. Deux méthodes sont retenues qui font intervenir un rapport d'intensité de deux images SAR en bande C : le rapport de polarisation HH/VV ou le changement temporel en co-polarisation HHdate2/HHdate1. Dans un premier temps, une étude statistique des rapports d'intensité de rétrodiffusion est effectuée, qui conduit au développement d'un modèle d'erreur permettant d'estimer la performance des méthodes de classification. Ce modèle d'erreur est également utilisé pour évaluer l'impact des paramètres des systèmes SAR (Synthetic Aperture Radar) sur la performance de la classification. Il s'agit des paramètres concernant l'étalonnage, l'ambiguïté, la fréquence de revisite. Dans un second temps, les méthodes de classification ainsi développées sont appliquées à deux jeux de données de l'instrument ASAR du satellite ENVISAT sur le delta du Mékong au Vietnam, pour faire la cartographie des rizières à deux échelles différentes. La première méthode repose sur l'utilisation du rapport HH/VV à partir de données du mode Alternating Polarization d'ASAR, qui permet de produire une carte de rizières couvrant une province du delta. La seconde méthode tire parti du changement temporel de HH sur des images du mode Wide-Swath d'ASAR, et est utilisée pour cartographier les rizières de l'ensemble du delta. Les deux méthodes sont validées avec succès en utilisant les surfaces cultivées données par les statistiques nationales