Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series

Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).We found strong agreement between SOS estimates from logistic and spline models (r_EVI = 0.86; r_NDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSD_EVI = 3.07, RMSD_NDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (r_EVI = 0.51, r_NDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.     

Extracting Regional Pattern of Wheat Sowing Dates Using Multispectral and High Temporal Observations from Indian Geostationary Satellite

Monitoring of Agricultural crops using remote sensing data is an emerging tool in recent years. Spatial determination of sowing date is an important input of any crop model. Geostationary satellite has the capability to provide data at high temporal interval to monitor vegetation throughout the entire growth period. A study was conducted to estimate the sowing date of wheat crop in major wheat growing states viz. Punjab, Haryana, Uttar Pradesh (UP), Madhya Pradesh (MP), Rajasthan and Bihar. Data acquired by Charged Couple Detector (CCD) onboard Indian geostationary satellite INSAT 3A have continental (Asia) coverage at 1 km?×?1 km spatial resolution in optical spectral bands with high temporal frequency. Daily operational Normalized Difference Vegetation Index (NDVI) product from INSAT 3A CCD available through Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) was used to estimate sowing date of wheat crop in selected six states. Daily NDVI data acquired from September 1, 2010 to December 31, 2010 were used in this study. A composite of 7 days was prepared for further analysis of temporal profile of NDVI. Spatial wheat crop map derived from AWiFS (56 m) were re-sampled at INSAT 3A CCD parent resolution and applied over each 7 day composite. The characteristic temporal profiles of 7 day NDVI composite was used to determine sowing date. NDVI profile showed decreasing trend during maturity of kharif crop, minimum value after harvest and increasing trend after emergence of wheat crop. A mathematical model was made to capture the persistent positive slope of NDVI profile after an inflection point. The change in behavior of NDVI profile was detected on the basis of change in NDVI threshold of 0.3 and sowing date was estimated for wheat crop in six states. Seven days has been deducted after it reached to threshold value with persistent positive slope to get sowing date. The clear distinction between early sowing and late sowing regions was observed in study area. Variation of sowing date was observed ranging from November 1 to December 20. The estimated sowing date was validated with the reported sowing date for the known wheat crop regions. The RMSD of 3.2 (n?=?45) has been observed for wheat sowing date. This methodology can also be applied over different crops with the availability of crop maps.     

Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS

Crop yield estimation has an important role on economy development and its accuracy and speed influence yield price and helps in deciding the excess or deficit production conditions. The water productivity evaluates the irrigation command through water use efficiency (WUE). Remote sensing (RS) and geographical information system (GIS) techniques were used for crop yield and water productivity estimation of wheat crop (Triticum aestivum) grown in Tarafeni South Main Canal (TSMC) irrigation command of West Bengal State in India. One IRS P6 image and four wide field sensor (WiFS) images for different months of winter season were used to determine the Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) for area under wheat crop. The temporally and spatially distributed spectral growth profile and AREASUM of NDVI (A_NDVI) and SAVI (A_SAVI) with time after sowing of wheat crop were developed and correlated with actual crop yield of wheat (Y_act). The developed relationships between A_SAVI and Y_act resulted high correlation in comparison to that of A_NDVI. Using the developed model the RS based wheat yield (Y_RS) predicted from A_SAVI varied on entire TSMC irrigation command from 22.67 to 33.13 q ha⁻¹ respectively, which gave an average yield of 26.50 q ha⁻¹. The RS generated yield based water use efficiency (WUE_YRS) for water supplied from canal of TSMC irrigation command was found to be 6.69 kg ha⁻¹ mm⁻¹.     

Estimating inter-annual variability in winter wheat sowing dates from satellite time series in Camargue,France

Crop simulation models are commonly used to forecast the performance of cropping systems under different hypotheses of change. Their use on a regional scale is generally constrained, however, by a lack of information on the spatial and temporal variability of environment-related input variables (e.g., soil) and agricultural practices (e.g., sowing dates) that influence crop yields. Satellite remote sensing data can shed light on such variability by providing timely information on crop dynamics and conditions over large areas. This paper proposes a method for analyzing time series of MODIS satellite data in order to estimate the inter-annual variability of winter wheat sowing dates. A rule-based method was developed to automatically identify a reliable sample of winter wheat field time series, and to infer the corresponding sowing dates. The method was designed for a case study in the Camargue region (France), where winter wheat is characterized by vernalization, as in other temperate regions. The detection criteria were chosen on the grounds of agronomic expertise and by analyzing high-confidence time-series vegetation index profiles for winter wheat. This automatic method identified the target crop on more than 56% (four-year average) of the cultivated areas, with low commission errors (11%). It also captured the seasonal variability in sowing dates with errors of ±8 and ±16 days in 46% and 66% of cases, respectively. Extending the analysis to the years 2002–2012 showed that sowing in the Camargue was usually done on or around November 1st (±4 days). Comparing inter-annual sowing date variability with the main local agro-climatic drivers showed that the type of preceding crop and the weather conditions during the summer season before the wheat sowing had a prominent role in influencing winter wheat sowing dates.     

Spectral wheat growth profile in Punjab using IRS WiFS data

A functional form of crop spectral profile suggested by Badhwar was applied to district-wise wheat Normalised Difference Vegetation Index (NDVI) values relatively normalised by Pseudo-Invariant Feature (urban and built-up) NDVI values, derived from Wide Field Sensor (WiFS) onboard Indian Remote Sensing Satellites (IRS) for 17 dates during 1999–2000 rabi season. The goodness of overall profile fitting and the three basic parameters i.e., crop emergence date (T_o), and crop specific parameters (a and P) was found to be statistically significant. While a corresponds to profile progressive growth rate, β corresponds to profile decay rate. A comparison with earlier studies in Punjab using NOAA-AVHRR indicated improvement in relation between peak NDVI and wheat yield. The estimated time of spectral emergence and profile-derived peak NDVI follow the observed behaviour of shortened crop pre-anthesis period with delayed sowing.     

Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain

Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat–maize and winter wheat–cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement with in situ observations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region.     

自动化样本生成策略用于冬季作物制图——以兰陵县为例

准确地获取作物空间分布是作物生长监测和产量预测的前提。目前,遥感图像处理需要足够的人工采集的训练样本,因此,大规模作物分布的自动获取仍然是一个挑战。以高效、经济的方式获得足够的训练样本成为作物制图的关键因素之一。因此,本文结合冬季作物物候特征与Sentinel-2时间序列影像,提出了一种自动化样本生成策略用于冬季作物制图。首先,利用归一化植被指数（NDVI）时间序列曲线进行冬季作物的判别;然后,通过时间序列曲线相似性度量的方法,判断样本点与标准的绿色叶绿素植被指数（GCVI）时间序列曲线的差距,从而为未知样本赋予正确的标签;最后,利用获取的样本训练随机森林模型,实现研究区域的冬季作物提取。最终精度评定结果：总体精度（OA）为98.46%,Kappa为0.973,表明该方法对于快速实现冬季作物自动制图的有效性。     

Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVI_v) and bare soil (NDVI_s), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVI_v and NDVI_s at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVI_s and NDVI_v can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVI_v and NDVI_s. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVI_v and NDVI_s maps for FVC estimation on regional and global scales.     

Local-scale agricultural drought monitoring with satellite-based multi-sensor time-series

ABSTRACT

Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel 1) data. Several indicators were derived such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), Tasseled cap indices and Sentinel-1 based backscattering intensity and relative surface moisture. We used logistic regression to evaluate the drought-induced variability of remotely sensed parameters estimated for different phases of crop growth. The parameters with the highest prediction rate were further used to estimate thresholds for drought/non-drought classification. The models were evaluated using the area under the receiver operating characteristic curve and validated with in-situ data. The results revealed that not all remotely sensed variables respond in the same manner to drought conditions. Growing season maximum NDVI and NDMI (70–75%) and SAR derived metrics (60%) reflect specifically the impact of agricultural drought. These metrics also depict stress affected areas with a larger spatial extent. LST was a useful indicator of crop condition especially for maize and sunflower with prediction rates of 86% and 71%, respectively. The developed approach can be further used to assess crop condition and to support decision-making in areas which are more susceptible and vulnerable to drought.     

A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia,Canada)

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r² = 0.56–0.77 AIC = 0.16–0.30 for SR models; r² = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVI_MODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVI_MSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVI_MSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVI_MODIS data (r² = 0.68 for SC for non-grazed systems, modeled with SR based on NDVI_MODIS data; r² = 0.77 for SC for non-grazed systems, modeled with SR based on NDVI_MSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.     

Assessing the suitability of data from Sentinel-1A and 2A for crop classification

Sentinel-1A C-SAR and Sentinel-2A MultiSpectral Instrument (MSI) provide data applicable to the remote identification of crop type. In this study, six crop types (beans, beetroot, grass, maize, potato, and winter wheat) were identified using five C-SAR images and one MSI image acquired during the 2016 growing season. To assess the potential for accurate crop classification with existing supervised learning models, the four different approaches namely kernel-based extreme learning machine (KELM), multilayer feedforward neural networks, random forests, and support vector machine were compared. Algorithm hyperparameters were tuned using Bayesian optimization. Overall, KELM yielded the highest performance, achieving an overall classification accuracy of 96.8%. Evaluation of the sensitivity of classification models and relative importance of data types using data-based sensitivity analysis showed that the set of VV polarization data acquired on 24 July (Sentinel-1A) and band 4 data (Sentinel-2A) had the greatest potential for use in crop classification.     

Evaluating the relationship between biomass,percent groundcover and remote sensing indices across six winter cover crop fields in Maryland,United States

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r² = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r² = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.     

区域作物生长过程的遥感提取方法

提出利用时序NDVI数据提取作物生长过程方法。遥感数据在采集过程中受云、大气因子的影响 ,以及混合像元问题 ,造成时序植被指数值变得没有规律 ,对比性不强。采用基于最小二次方拟合的谐函数分析方法 ,依据作物轮作规律和生长周期性特征 ,用主要频率的正弦、余弦谐函数重建时序图像 ,去除了影像中云污染的影响。以中国的旱地为例 ,考虑到像元内旱地对NDVI值的贡献率 ,计算区域内旱地像元加权平均值来反映其作物生长过程。同时与区域所有像元的平均值、旱地平均值等统计方法的结果进行对比分析 ,表明区域内旱地的加权平均值能够削弱旱地比例和地域间的差异 ,突出耕地上作物的生长过程特征。通过与地面实测数据分析 ,平滑前后的作物生长过程与叶面积指数相关性增加 5 %— 11% ,采用区域加权平均的方法得到的作物生长过程 ,比旱地平均和NDVI平均的结果与叶面积指数的相关性增加 14 %— 17%。     

On spectral indices as a function of soil variability in safflower crop

Spectral indices as an indicator of physiological traits affecting safflower yield in relation to soil variability were evaluated in a two year experiment (1997–1999). Reflectance, biometric and phonological data were collected. Two indices namely normalized differential vegetation index (NDVI) and ratio of spectral reflectance in infrared region to red region (1R/R) were derived from radiometric observation. Yield data indicated significant difference in different soils. Temporal NDVI behaviour as a function of soil type was not prominent especially in early stages of crop growth. However NDVI at 75 days after sowing (DAS) was found to be relatively better indicator of plant status and yield. IR/R was relatively less effective in indicating the differential response of crop to soil types. Effect of soil and crop interaction on spectral indices was significant at 75 and 90 DAS, which was attributed to attainment of maximum leaf area and leaf area at these stages of growth. Regression analysis showed strong positive relationship between NDVI and leaf area, dry matter and yield. IR/R and leaf area had the strongest and consistent relationship (r = 0.96). A single regression equation accounted for yield variability in the dataset. Thus possible transformation of NDVI maps (satellite data) to LAI units and consequently applications like yield forecasting was indicated. Utility of spectra-temporal data as a pointer of plant development status and yield was also demonstrated.     

Is it possible to discern Striga weed (Striga hermonthica) infestation levels in maize agro-ecological systems using in-situ spectroscopy?

The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum (EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis: LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga (Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 waveband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 using published spectral response functions. We sampled and detected seven Striga infestation classes based on three flowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture of maize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to select the most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2 multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algorithms was compared using classification accuracy assessment metrics. We were able to positively discriminate Striga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurring weeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampled Sentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highest overall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands, respectively, across the four different discriminant models tested in this study. The class with the highest detection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurring weeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 most relevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectral wavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectral wavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hyperspectral features (i.e. wavebands and VIs) significantly (p ≤ 0.05) increased the overall classification accuracy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Our results show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learning discriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems.     

Derivation of crop phenological parameters using multi-date SPOT-VGT-NDVI data: A case study for Punjab

Crop phenological parameters, such as the start and end time of the crop growth, the total length of the growing season, time of peak vegetation and rate of greening and senescence are important for planning crop management and crop diversification/intensification. Multi-temporal remote sensing data provides opportunity to characterize the crop phenology at regional level. This study was conducted during the kharif season of the year 2001–02 for Punjab. The ten-day Normalised Difference Vegetation Index (NDVI) composite products, with 1 km spatial resolution, available from the Vegetation sensor onboard SPOT4 were used for the study. Twenty-one temporal datasets from May 1, 2001 to November 21, 2001 were used. Logical modelling approach was followed to compute the minimum and maximum NDVI, the amplitude of NDVI, the threshold NDVI during sowing and harvest, the crop duration, integrated NDVI and skewness of profile. The analysis showed that before July beginning, in the whole of Punjab, sowing/planting was over. It was found that the crop emergence in the eastern part of the state started earlier than the western region. The maximum NDVI, which represented peak vegetative stage, was above 0.7 and occurred mostly during August. The duration of crops ranged between 90–140 days, with majority between 110–120 days. Total integrated NDVI in Punjab was generally above 60. Using principal component analysis and divergence analysis seven best metrics were selected for crop discrimination.     

Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data

Improved rice crop and water management practices that make the sustainable use of resources more efficient are important interventions towards a more food secure future. A remote sensing-based detection of different rice crop management practices, such as crop establishment method (transplanting or direct seeding), can provide timely and cost-effective information on which practices are used as well as their spread and change over time as different management practices are adopted. Establishment method cannot be easily observed since it is a rapid event, but it can be inferred from resulting observable differences in land surface characteristics (i.e. field condition) and crop development (i.e. delayed or prolonged stages) that take place over a longer time. To examine this, we used temporal information from Synthetic Aperture Radar (SAR) backscatter to detect differences in field condition and rice growth, then related those to crop establishment practices in Nueva Ecija (Philippines). Specifically, multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Farmer surveys and field observations were conducted in four selected municipalities across the study area in 2017, providing information on field boundaries and crop management practices for 61 fields. Mean backscatter values were generated per rice field per SAR acquisition date. We matched the SAR acquisition dates with the reported dates for land management activities and with the estimated dates for when the crop growth stages occurred. The Mann-Whitney U test was used to identify significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences in cross-polarised, co-polarised and band ratio backscatter values were observed in the early growing season, specifically during land preparation, crop establishment, rice tillering and stem elongation. These findings indicate the possibility to discriminate crop establishment methods by SAR at those stages, suggesting that there is more opportunity for discrimination than has been presented in previous studies. Further testing in a wider range of environments, seasons, and management practices should be done to determine how reliably rice establishment methods can be detected. The increased use of dry and wet direct seeding has implications for many remote sensing-based rice detection methods that rely on a strong water signal (typical of transplanting) during the early season.     

Multispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data

The use of multispectral satellite sensors for generation of hyperspectral indices is restricted because of their coarse spectral resolutions. In this study, we attempted to synthesize a few of these hyperspectral indices, viz. RedEdge Normalized Difference Vegetation Index (NDVI₇₀₅), Plant Senescence Reflectance Index (PSRI) and Normalized-Difference-Infrared-Index (NDII), for crop stress monitoring at regional scale using multispectral images, simulated from Hyperion data. The Hyperion data were resampled and simulated to corresponding spatial and spectral resolutions of AWiFS, OCM-2 and MODIS sensors using their respective filter function. Different possible combinations of two bands (i.e. simple difference, simple ratio and normalized difference) were computed using synthetic spectral bands of each sensor, and were regressed with NDVI₇₀₅, PSRI and NDII. Models with highest correlation were selected and inverted on Hyperion data of another date to synthesize respective multispectral indices. Synthetic broad band indices of multispectral sensors with their respective narrow band indices of Hyperion were found to be in good agreement.     

Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions

ABSTRACT

Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technologies that can somehow support them in quantifying and mapping risks. Remotely sensed data, with special focus on free ones, can certainly provide the most of information they need, making possible to better calibrate insurance fees in space and time. In this work, a prototype of service based on free remotely sensed data is proposed with the aim of supporting insurance companies’ strategies. The service is thought to calibrate annual insurance rates, longing for their reduction at such level that new customers could be attracted. The study moves from the entire Piemonte region (NW Italy), to specifically focus onto the Cuneo province (Southern Piemonte), which is mainly devoted to agriculture. MODIS MOD13Q1-v6 and Sentinel-2 L2A image time series were jointly used. NDVI maps from MODIS data were useful to describe the midterm phenological trends of main crops at regional level in the period 2000–2018; differently, Sentinel-2 data permitted to map local crop differences at field level in 2016 and 2017 years. With reference to MODIS data, the average phenological behavior of main crop classes in the area, obtained from the CORINE Land Cover map Level 3, was considered using a time series decomposition approach. Trend analyses showed that the most of the crop classes alternated three phases (about 7 years) suggesting that, presently, this is probably the time horizon to be considered to tune mid-term algorithms for risk estimates in the agricultural context. Crop classes trends were consequently split into three phases and each of them modeled by a first-order polynomial function used to update correspondent insurance risk rate. Sentinel-2 data were used to map phenological anomalies at field level for the 2016 and 2017 growing seasons; shifts from class average behavior were considered to locally and temporarily tune insurance premium around its average trend as described at the previous step. Synthesizing, one can say that this approach, integrating MODIS and Sentnel-2 data, makes possible to locally and temporarily calibrate premiums of indexed insurance policies by describing the average trends of crop performance (NDVI) at regional level by MODIS data and refining it at field and specific crop level by Sentinel-2 data.     

A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time

Within-season forecasting of crop yields is of great economic, geo-strategic and humanitarian interest. Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio-temporal information to assist such yield forecasts. This study explores different configurations of remote sensing time series to estimate of winter wheat yield using either spatially finer but temporally sparser time series (5daily at 100 m spatial resolution) or spatially coarser but denser (300 m and 1 km at daily frequency) time series. Furthermore, we hypothesised that better yield estimations could be made using thermal time, which is closer to the crop physiological development. Time series of NDVI from the PROBA-V instrument, which has delivered images at a spatial resolution of 100 m, 300 m and 1 km since 2013, were extracted for 39 fields for field and 56 fields for regional level analysis across Northern France during the growing season 2014-2015. An asymmetric double sigmoid model was fitted on the NDVI series of the central pixel of the field. The fitted model was subsequently integrated either over thermal time or over calendar time, using different baseline NDVI thresholds to mark the start and end of the cropping season. These integrated values were used as a predictor for yield using a simple linear regression and yield observations at field level. The dependency of this relationship on the spatial pixel purity was analysed for the 100 m, 300 m and 1 km spatial resolution. At field level, depending on the spatial resolution and the NDVI threshold, the adjusted R² ranged from 0.20 to 0.74; jackknifed – leave-one-field-out cross validation – RMSE ranged from 0.6 to 1.07 t/ha and MAE ranged between 0.46 and 0.90 t/ha for thermal time analysis. The best results for yield estimation (adjusted R² = 0.74, RMSE =0.6 t/ha and MAE =0.46 t/ha) were obtained from the integration over thermal time of 100 m pixel resolution using a baseline NDVI threshold of 0.2 and without any selection based on pixel purity. The field scale yield estimation was aggregated to the regional scale using 56 fields. At the regional level, there was a difference of 0.0012 t/ha between thermal and calendar time for average yield estimations. The standard error of mean results showed that the error was larger for a higher spatial resolution with no pixel purity and smaller when purity increased. These results suggest that, for winter wheat, a finer spatial resolution rather than a higher revisit frequency and an increasing pixel purity enable more accurate yield estimations when integrated over thermal time at the field scale and at the regional scale only if higher pixel purity levels are considered. This method can be extended to larger regions, other crops, and other regions in the world, although site and crop-specific adjustments will have to include other threshold temperatures to reflect the boundaries of phenological activity. In general, however, this methodological approach should be applicable to yield estimation at the parcel and regional scales across the world.