Quantifying Irrigated Winter Wheat LAI in Argentina Using Multiple Sentinel-1 Incidence Angles

Synthetic aperture radar (SAR) data provides an appealing opportunity for all-weather day or night Earth surface monitoring. The European constellation Sentinel-1 (S1) consisting of S1-A and S1-B satellites offers a suitable revisit time and spatial resolution for the observation of croplands from space. The C-band radar backscatter is sensitive to vegetation structure changes and phenology as well as soil moisture and roughness. It also varies depending on the local incidence angle (LIA) of the SAR acquisition’s geometry. The LIA backscatter dependency could therefore be exploited to improve the retrieval of the crop biophysical variables. The availability of S1 radar time-series data at distinct observation angles holds the feasibility to retrieve leaf area index (LAI) evolution considering spatiotemporal coverage of intensively cultivated areas. Accordingly, this research presents a workflow merging multi-date S1 smoothed data acquired at distinct LIA with a Gaussian processes regression (GPR) and a cross-validation (CV) strategy to estimate cropland LAI of irrigated winter wheat. The GPR-S1-LAI model was tested against in situ data of the 2020 winter wheat campaign in the irrigated valley of Colorador river, South of Buenos Aires Province, Argentina. We achieved adequate validation results for LAI with R2CV = 0.67 and RMSECV = 0.88 m2 m−2. The trained model was further applied to a series of S1 stacked images, generating temporal LAI maps that well reflect the crop growth cycle. The robustness of the retrieval workflow is supported by the associated uncertainties along with the obtained maps. We found that processing S1 smoothed imagery with distinct acquisition geometries permits accurate radar-based LAI modeling throughout large irrigated areas and in consequence can support agricultural management practices in cloud-prone agri-environments.EEA Hilario AscasubiFil: Caballero, Gabriel. Technological University of Uruguay (UTEC). Agri-Environmental Engineering; UruguayFil: Caballero, Gabriel. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Sanchez Angonova, Paolo Andres. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Orden, Luciano. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Orden, Luciano. Universidad Miguel Hernández. Centro de Investigación e Innovación Agroalimentaria y Agroambiental. GIAAMA Reseach Group; EspañaFil: Berger, Katja. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Berger, Katja. Mantle Labs GmbH; AustriaFil: Verrelst, Jochem. University of Valencia. Image Processing Laboratory (IPL); EspañaFil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); Españ

Cartografía de alta resolución de la cubierta del suelo y clasificación de los cultivos en la cuenca del Loukkos (norte de Marruecos): Un enfoque que utiliza las series temporales de SAR Sentinel-1

[EN] Remote  sensing  has  become  more  and  more  a  reliable  tool  for  mapping  land  cover  and  monitoring  cropland. Much of the work done in this field uses optical remote sensing data. In Morocco, active remote sensing data remain under-exploited despite their importance in monitoring spatial and temporal dynamics of land cover and crops even during cloudy weather. This study aims to explore the potential of C-band Sentinel-1 data in the production of a high-resolution land cover mapping and crop classification within the irrigated Loukkos watershed agricultural landscape in northern Morocco. The work was achieved by using 33 dual-polarized images in vertical-vertical  (VV)  and  vertical-horizontal  (VH)  polarizations.  The  images  were  acquired  in  ascending  orbits  between  April 16 and October 25, 2020, with the purpose to track the backscattering behavior of the main crops and other land  cover  classes  in  the  study  area.  The  results  showed  that  the  backscatter  increased  with  the  phenological  development  of  the  monitored  crops  (rice,  watermelon,  peanuts,  and  winter  crops),  strongly  for  the  VH  and  VV  bands, and slightly for the VH/VV ratio. The other classes (water, built-up, forest, fruit trees, permanent vegetation, greenhouses, and bare lands) did not show significant variation during this period. Based on the backscattering analysis and the field data, a supervised classification was carried out, using the Random Forest Classifier (RF) algorithm.  Results  showed  that  radiometric  characteristics  and  6  days  time  resolution  covered  by  Sentinel-1  constellation gave a high classification accuracy by dual-polarization with Radar Ratio (VH/VV) or Radar Vegetation Index and textural features (between 74.07% and 75.19%). Accordingly, this study proves that the Sentinel-1 data provide useful information and a high potential for multi-temporal analyses of crop monitoring, and reliable land cover mapping which could be a practical source of information for various purposes in order to undertake food security issues.[ES] La teledetección se ha convertido en una herramienta cada vez más fiable para cartografiar la cubierta vegetal y controlar las tierras de cultivo. Gran parte de los trabajos realizados en este campo utilizan datos ópticos de teledetección. Además, en Marruecos, los datos de teledetección activa siguen estando infrautilizados, a pesar de su importancia para el seguimiento de la dinámica espacial y temporal de la cubierta vegetal y de los cultivos, incluso con tiempo nublado. Este estudio tiene como objetivo explorar el potencial de los datos de la banda C de Sentinel-1 en la producción de una cartografía de alta resolución de la cubierta del suelo y la clasificación de los cultivos dentro del paisaje agrícola de la cuenca del Loukkos de regadío en el norte de Marruecos. Este trabajo se ha realizado utilizando 33 imágenes de doble polarización vertical-vertical (VV) y vertical-horizontal (VH). Las imágenes fueron adquiridas en órbitas ascendentes entre el 16 de abril y el 25 de octubre de 2020, con el propósito de rastrear el comportamiento de retrodispersión de los principales cultivos y otras clases de cobertura del suelo en el área de estudio. Los gráficos obtenidos muestran que la retrodispersión aumenta con el desarrollo fenológico de los tres cultivos monitorizados (arroz, sandía, cacahuetes, cultivos de invierno), fuertemente para las bandas VH y VV, y ligeramente para el ratio VH/VV. Las otras clases (agua, edificado, bosque, árboles frutales, vegetación permanente, invernaderos y tierras desnudas) no muestran una variación significativa durante este periodo. A partir del análisis de retrodispersión y de los datos de campo, se llevó a cabo una clasificación supervisada, utilizando el  algoritmo  Random Forest Classifier (RF). Los resultados muestran que las características radiométricas y la resolución temporal para los 6 días cubiertos por la constelación Sentinel-1 dan una alta precisión de clasificación por polarización dual con Ratio de Radar (VH/VV) o Índice de Vegetación de Radar y características de la textura (entre  74,07%  y  75,17%).  En  consecuencia,  este  estudio  demuestra  que  los  datos  de  Sentinel-1  proporcionan  información útil y un alto potencial para los análisis multitemporales de seguimiento de los cultivos, así como una cartografía fiable de la cubierta terrestre que debería ser una fuente de información práctica para para varios propósitos a fin de acometer cuestiones de seguridad alimentaria.Nizar, EM.; Wahbi, M.; Ait Kazzi, M.; Yazidi Alaoui, O.; Boulaassal, H.; Maatouk, M.; Zaghloul, MN.... (2022). High Resolution Land Cover Mapping and Crop Classification in the Loukkos Watershed (Northern Morocco): An Approach Using SAR Sentinel-1 Time Series. Revista de Teledetección. (60):47-69. https://doi.org/10.4995/raet.2022.17426OJS47696

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

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

From cropland to cropped field: A robust algorithm for national-scale mapping by fusing time series of Sentinel-1 and Sentinel-2

Detailed and updated maps of actively cropped fields on a national scale are vital for global food security. Unfortunately, this information is not provided in existing land cover datasets, especially lacking in smallholder farmer systems. Mapping national-scale cropped fields remains challenging due to the spectral confusion with abandoned vegetated land, and their high heterogeneity over large areas. This study proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were proposed and highlighted cropped fields by consistently higher values due to cropping activities. The proposed VSPS algorithm was exploited for national-scale mapping in China without regional adjustments using Sentinel-2 and Sentinel-1 images. Agriculture in China illustrated great heterogeneity and has experienced tremendous changes such as non-grain orientation and cropland abandonment. Yet, little is known about the locations and extents of cropped fields cultivated with field crops on a national scale. Here, we produced the first national-scale 20 m updated map of cropped and fallow/abandoned land in China and found that 77 % of national cropland (151.23 million hectares) was actively cropped in 2020. We found that fallow/abandoned cropland in mountainous and hilly regions were far more than we expected, which was significantly underestimated by the commonly applied VImax-based approach based on the MODIS images. The VSPS method illustrates robust generalization capabilities, which obtained an overall accuracy of 94 % based on 4,934 widely spread reference sites. The proposed mapping framework is capable of detecting cropped fields with a full consideration of a high diversity of cropping systems and complexity of fallow/abandoned cropland. The processing codes on Google Earth Engine were provided and hoped to stimulate operational agricultural mapping on cropped fields with finer resolution from the national to the global scale

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery

Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45º), and also to configurations where several polarimetric features (Pauli and Cloude–Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45º bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational