Enhanced processing of 1-km spatial resolution fAPAR time series for sugarcane yield forecasting and monitoring

A processing of remotely-sensed Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) time series at 1-km spatial resolution is established to estimate sugarcane yield over the state of São Paulo, Brazil. It includes selecting adequate time series according to the signal spatial purity, using thermal time instead of calendar time and smoothing temporally the irregularly sampled observations. A systematic construction of various metrics and their capacity to predict yield is explored to identify the best performance, and see how timely the yield forecast can be made. The resulting dataset not only reveals a strong spatio-temporal structure, but is also capable of detecting both absolute changes in biomass accumulation and changes in its inter-annual variability. Sugarcane yield can thus be estimated with a RMSE of 1.5 t/ha (or 2%) without taking into account the strong linear trend in yield increase witnessed in the past decade. Including the trend reduces the error to 0.6 t/ha, correctly predicting whether the yield in a given year is above or below the trend in 90% of cases. The methodological framework presented here could be applied beyond the specific case of sugarcane in São Paulo, namely to other crops in other agro-ecological landscapes, to enhance current systems for monitoring agriculture or forecasting yield using remote sensing.JRC.H.4-Monitoring Agricultural Resource

Remote Sensing Based Yield Estimation in a Stochastic Framework – Case Study of Durum Wheat in Tunisia

Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by the information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument.JRC.H.4-Monitoring Agricultural Resource

Monitoring the Sustainable Intensification of Arable Agriculture:the Potential Role of Earth Observation

Sustainable intensification (SI) has been proposed as a possible solution to the conflicting problems of meeting projected increases in food demand and preserving environmental quality. SI would provide necessary production increases while simultaneously reducing or eliminating environmental degradation, without taking land from competing demands. An important component of achieving these aims is the development of suitable methods for assessing the temporal variability of both the intensification and sustainability of agriculture. Current assessments rely on traditional data collection methods that produce data of limited spatial and temporal resolution. Earth Observation (EO) provides a readily accessible, long-term dataset with global coverage at various spatial and temporal resolutions. In this paper we demonstrate how EO could significantly contribute to SI assessments, providing opportunities to quantify agricultural intensity and environmental sustainability. We review an extensive body of research on EO-based methods to assess multiple indicators of both agricultural intensity and environmental sustainability. To date these techniques have not been combined to assess SI; here we identify the opportunities and initial steps required to achieve this. In this context, we propose the development of a set of essential sustainable intensification variables (ESIVs) that could be derived from EO data

A Review of Vegetation Phenological Metrics Extraction Using Time-Series, Multispectral Satellite Data

Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes, and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phenology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness, and growing season length) often termed “land surface phenology,” as well as more advanced methods that estimate species-specific phenological stages (e.g., silking stage of maize). Common data-processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phenology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multiscale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams, and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization

Analisis Usia Tebu Terhadap Pola Nilai GNDVI (Green Normalized Difference Vegetation Index) Berdasarkan Data Citra Landsat-8

Tebu adalah salah satu tanaman yang dapat memproduksi gula serta bioenergi bagi lingkungan yang masa tanamnya selama ±12 bulan. Kondisi pertumbuhan tanaman tebu berdasarkan usia tanamnya dapat dipantau dengan menggunakan teknologi penginderaan jauh. Penelitian ini bertujuan untuk mengetahui pola reflektansi spektra yang dinyatakan melalui indeks vegetasi GNDVI. Penelitian ini menghubungkan nilai GNDVI terhadap usia tanaman tebu dengan menggunakan data citra Landsat 8 tahun 2019-2020 di Kabupaten Jember yang terbagi menjadi 11 lokasi lahan. Tahap awal yang dilakukan yaitu mengumpulkan data citra lalu dikoreksi radiometrik ToA. Selanjutnya menghitung nilai GNDVI pada lokasi lahan tebu yang ditinjau. Nilai GNDVI dari setiap lokasi lahan ini dihitung nilai mediannya dan dibuat grafik hubungan antara nilai GNDVI terhadap usia tanam tebu. Dari hasil penelitian didapatkan pola nilai GNDVI terhadap usia tebu berbentuk kurva parabolik. Hasil dari pola tersebut menunjukkan bahwa nilai GNDVI berada pada maksimum di usia sekitar 8, 9, dan 10, dimana nilai GNDVInya dalam rentang 0.43 sampai 0.54. Secara rata-rata hubungan usia tebu dengan nilai GNDVI dinyatakan dengan persamaan   dimana X dan Y berturut-turut adalah usia tebu dan nilai GNDVI dengan puncak nilai GNDVI berada pada bulan ke 9. Secara umum dapat dikatakan bahwa semakin besar usia tebu maka semakin besar nilai GNDVI hingga pada keadaan maksimum dan kembali menurun

Estimation de la biomasse de canne par modélisation et télédétection. Application à la Réunion

In the context of an increasing demand for sugar, the estimation of sugarcane biomass in smallholding farming countries (of which Reunion Island is an example) is an optimization lever of production and thus of sustainability for the sugar industry facing giants such as Brazil, India of China. The objective of this thesis is to explore the contribution of remote sensing for the estimation of sugarcane yields at field scale on Reunion Island. We organized our work in two main approaches: first, a methodological approach, where we explore the coupling (recalibration and forcing) between remote sensing data and modeling, and second, an operational approach where we compare three methods of yield estimation based on remote sensing : (1) empirical relationships between yield and vegetation indices computed from remote sensing data, (2) the efficiency models, with a low number of parameters and thus easily adaptable to different types of crops and (3) forcing a sugarcane crop growth model with data derived from remote sensing. The MOSICAS sugarcane dedicated crop model, which is adapted to the cropping conditions of Reunion Island, was used. Our tests were made on sixty three fields located on two contrasted in-farm sites, and on seven plots located on an experimental site. Our dataset was composed of remote sensing data (SPOT4 & 5 images and thermal infrared data), yield data, climatic data, soil data and cropping practices data (irrigation schedules and harvest dates). Concerning the methodological approach, obtained results showed that remote sensing data, through a better inclusion of the actual state of development of the crop or an optimized parameterization of the model, results in a significant enhancement of the estimation of the yield by the MOSICAS model. In particular, we showed that forcing the model resulted in a gain of accuracy of 2.6 t ha-1. We also recalibrated the radiation use efficiency parameter for each studied cultivar. Finally, we determined an optimized value of the rooting depth parameter using recalibration and the water stress index CWSI as an adjustment variable. Concerning the application approach, our results also showed that the more complex methods of yield estimation do not provide the best results when considering the precision. We therefore recommend using the simple empirical relationship between yield and vegetation indices for the estimation of the sugarcane biomass on Reunion Island. These results offer several prospects: firstly, a better inclusion of the heterogeneity of cultivars used on Reunion Island by recalibrating the key parameters of the yield computation for each of these cultivars in order to test various scenarios of cultivar implantation as a function of climatic zones of the island. The estimation method selected here should also be exported to other sugarcane smallholder countries, particularly with introduction of the Sentinel-2 system to provide open access and high spatial resolution images.Dans un contexte de demande mondiale en sucre sans cesse croissante, l'estimation de la biomasse de canne à sucre dans les pays petits producteurs (dont la Réunion est un exemple) est un levier d'optimisation de la production et donc de pérennisation de la filière sucrière face à des géants tels le Brésil, l'Inde ou encore la Chine. L'objectif de cette thèse est d’'explorer l'apport de la télédétection à l'estimation des rendements à l'échelle de la parcelle de canne à sucre à la Réunion. Nous avons organisé notre travail suivant deux grandes approches : d'une part une approche méthodologique, où nous explorons le couplage (réétalonnage et forçage) entre données de télédétection et modèles et d'autre part une approche opérationnelle où nous comparons trois méthodes d'estimation du rendement basées sur la télédétection : (1) les relations empiriques entre rendement et indices de végétation calculés à partir de données de télédétection, (2) les modèles d'efficience, faiblement paramétrés et donc aisément adaptables à différentes types de cultures et (3) le forçage d'un modèle de croissance de la canne à sucre avec des données issues de télédétection. Le modèle de croissance utilisé est MOSICAS, adapté aux conditions de culture de la canne à sucre à la Réunion. Nos tests ont été réalisés sur soixante-trois parcelles situées sur deux exploitations agricoles présentant des conditions de croissance contrastées, ainsi que sur sept placettes expérimentales. Notre jeu de données était composé de données obtenues par télédétection (images SPOT4 & 5, données de capteurs infrarouges thermiques), ainsi que des données de rendement, des données climatiques, pédologiques et d'itinéraire technique (calendriers d'irrigation et dates de coupe). L'approche méthodologique a montré que les données de télédétection apportaient une amélioration significative de l'estimation des rendements par le modèle MOSICAS au travers de la prise en compte de l'état réel de développement de la culture ou encore de l'amélioration des valeurs de paramètres du modèle. Nous avons notamment montré que le forçage apportait un gain de précision de 2.6 t ha-1 aux rendements estimés par le modèle. En outre, nous avons réétalonné le paramètre d'efficience de conversion du rayonnement en biomasse pour chaque variété étudiée. Enfin, nous avons déterminé une valeur optimisée de la réserve utile via la profondeur d'enracinement par réétalonnage en utilisant l'indice de stress hydrique CWSI comme variable d'ajustement. Concernant l'approche applicative, nos résultats ont montré que les méthodes les plus complexes d'estimation du rendement n'offraient pas les meilleurs résultats en termes de précision. Nous recommandons l'utilisation de la méthode reposant sur une simple relation empirique entre le NDVI et le rendement pour l'élaboration d'un système opérationnel d'estimation de la production de la biomasse de canne à la Réunion. Ces résultats offrent plusieurs perspectives : d'une part, une meilleure prise en compte de l'hétérogénéité des variétés cultivées à la Réunion en réétalonnant les paramètres clés du calcul de rendement pour chacune de ces variétés et tester différents scénarios d'implantation variétale selon les zones climatiques de l'île. De plus, la méthode d'estimation de la biomasse proposée ici peut être exportée à d'autres pays petits producteurs de canne à sucre, notamment avec la mise en place du système Sentinel-2 devant apporter, à terme, des images en accès libre à haute résolution spatiale.