Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Joint retrieval of growing season corn canopy LAI and leaf chlorophyll content by fusing Sentinel-2 and MODIS images

Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R2) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1-0.2 and 0.0-0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R2 value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data

Characterizing Spatiotemporal Patterns of White Mold in Soybean across South Dakota Using Remote Sensing

Soybean is among the most important crops, cultivated primarily for beans, which are used for food, feed, and biofuel. According to FAO, the United States was the biggest soybeans producer in 2016. The main soybean producing regions in the United States are the Corn Belt and the lower Mississippi Valley. Despite its importance, soybean production is reduced by several diseases, among which Sclerotinia stem rot, also known as white mold, a fungal disease that is caused by the fungus Sclerotinia sclerotiorum is among the top 10 soybean diseases. The disease may attack several plants and considerably reduce yield. According to previous reports, environmental conditions corresponding to high yield potential are most conducive for white mold development. These conditions include cool temperature (12-24 °C), continued wet and moist conditions (70-120 h) generally resulting from rain, but the disease development requires the presence of a susceptible soybean variety. To better understand white mold development in the field, there is a need to investigate its spatiotemoral characteristics and provide accurate estimates of the damages that white mold may cause. Current and accurate data about white mold are scarce, especially at county or larger scale. Studies that explored the characteristics of white mold were generally field oriented and local in scale, and when the spectral characteristics were investigated, the authors used spectroradiometers that are not accessible to farmers and to the general public and are mostly used for experimental modeling. This study employed free remote sensing Landsat 8 images to quantify white mold in South Dakota. Images acquired in May and July were used to map the land cover and extract the soybean mask, while an image acquired in August was used to map and quantify white mold using the random forest algorithm. The land cover map was produced with an overall accuracy of 95% while white mold was mapped with an overall accuracy of 99%. White mold area estimates were respectively 132 km2, 88 km2, and 190 km2, representing 31%, 22% and 29% of the total soybean area for Marshall, Codington and Day counties. This study also explored the spatial characteristics of white mold in soybean fields and its impact on yield. The yield distribution exhibited a significant positive spatial autocorrelation (Moran’s I = 0.38, p-value \u3c 0.001 for Moody field, Moran’s I = 0.45, p-value \u3c 0.001, for Marshall field) as an evidence of clustering. Significant clusters could be observed in white mold areas (low-low clusters) or in healthy soybeans (high-high clusters). The yield loss caused by the worst white mold was estimated at 36% and 56% respectively for the Moody and the Marshall fields, with the most accurate loss estimation occurring between late August and early September. Finally, this study modeled the temporal evolution of white mold using a logistic regression analysis in which the white mold was modeled as a function of the NDVI. The model was successful, but further improved by the inclusion of the Day of the Year (DOY). The respective areas under the curves (AUC) were 0.95 for NDVI and 0.99 for NDVI+DOY models. A comparison of the NDVI temporal change between different sites showed that white mold temporal development was affected by the site location, which could be influenced by many local parameters such as the soil properties, the local elevation, management practices, or weather parameters. This study showed the importance of freely available remotely sensed satellite images in the estimation of crop disease areas and in the characterization of the spatial and temporal patterns of crop disease; this could help in timely disease damage assessment

The integration of freely available medium resolution optical sensors with Synthetic Aperture Radar (SAR) imagery capabilities for American bramble (Rubus cuneifolius) invasion detection and mapping.

Doctoral Degree. University of KwaZulu- Natal, Pietermaritzburg.The emergence of American bramble (Rubus cuneifolius) across South Africa has caused severe ecological and economic damage. To date, most of the efforts to mitigate its effects have been largely unsuccessful due to its prolific growth and widespread distribution. Accurate and timeous detection and mapping of Bramble is therefore critical to the development of effective eradication management plans. Hence, this study sought to determine the potential of freely available, new generation medium spatial resolution satellite imagery for the detection and mapping of American Bramble infestations within the UNESCO world heritage site of the uKhahlamba Drakensberg Park (UDP). The first part of the thesis determined the potential of conventional freely available remote sensing imagery for the detection and mapping of Bramble. Utilizing the Support Vector Machine (SVM) learning algorithm, it was established that Bramble could be detected with limited users (45%) and reasonable producers (80%) accuracies. Much of the confusion occurred between the grassland land cover class and Bramble. The second part of the study focused on fusing the new age optical imagery and Synthetic Aperture Radar (SAR) imagery for Bramble detection and mapping. The synergistic potential of fused imagery was evaluated using multiclass SVM classification algorithm. Feature level image fusion of optical imagery and SAR resulted in an overall classification accuracy of 76%, with increased users and producers’ accuracies for Bramble. These positive results offered an opportunity to explore the polarization variables associated with SAR imagery for improved classification accuracies. The final section of the study dwelt on the use of Vegetation Indices (VIs) derived from new age satellite imagery, in concert with SAR to improve Bramble classification accuracies. Whereas improvement in classification accuracies were minimal, the potential of stand-alone VIs to detect and map Bramble (80%) was noteworthy. Lastly, dual-polarized SAR was fused with new age optical imagery to determine the synergistic potential of dual-polarized SAR to increase Bramble mapping accuracies. Results indicated a marked increase in overall Bramble classification accuracy (85%), suggesting improved potential of dual-polarized SAR and optical imagery in invasive species detection and mapping. Overall, this study provides sufficient evidence of the complimentary and synergistic potential of active and passive remote sensing imagery for invasive alien species detection and mapping. Results of this study are important for supporting contemporary decision making relating to invasive species management and eradication in order to safeguard ecological biodiversity and pristine status of nationally protected areas

Assessing Interactions between Estuary Water Quality and Terrestrial Land Cover in Hurricane Events with Multi-sensor Remote Sensing

Estuaries are environmentally, ecologically and environmentally important places as they act as a meeting place for land, freshwater and marine ecosystems. They are also called nurseries of the sea as they often provide nesting and feeding habitats for many aquatic plants and animals. These estuaries also withstand the worst of some natural disasters, especially hurricanes. The estuaries as well as the harbored ecosystems undergo significant changes in terms of water quality, vegetation cover etc. and these components are interrelated. When hurricane makes landfall it is necessary to assess the damages as quickly as possible as restoration and recovery processes are time-sensitive. However, assessment of physical damages through inspection and survey and assessment of chemical and nutrient component changes by laboratory testing are time-consuming processes. This is where remote sensing comes into play. With the help of remote sensing images and regression analysis, it is possible to reconstruct water quality maps of the estuary affected. The damage sustained by the vegetation cover of the adjacent coastal watershed can be assessed using Normalized Difference Vegetation Index (NDVI) The water quality maps together with NDVI maps help observe a dynamic sea-land interaction due to hurricane landfall. The observation of hurricane impacts on a coastal watershed can be further enhanced by use of tasseled cap transformation (TCT). TCT plots provide information on a host of land cover conditions with respect to soil moisture, canopy and vegetation cover. The before and after TCT plots help assess the damage sustained in a hurricane event and also see the progress of recovery. Finally, the use of synthetic images obtained by use of data fusion will help close the gap of low temporal resolution of Landsat satellite and this will create a more robust monitoring system

Analyzing the phenologic dynamics of kudzu (Pueraria montana) infestations using remote sensing and the normalized difference vegetation index.

Non-native invasive species are one of the major threats to worldwide ecosystems. Kudzu (Pueraria montana) is a fast-growing vine native to Asia that has invaded regions in the United States making management of this species an important issue. Estimated normalized difference vegetation index (NDVI) values for the years 2000 to 2015 were calculated using data collected by Landsat and MODIS platforms for three infestation sites in Kentucky. The STARFM image-fusing algorithm was used to combine Landsat- and MODIS-derived NDVI into time series with a 30 m spatial resolution and 16 day temporal resolution. The fused time series was decomposed using the Breaks for Additive Season and Trend (BFAST) algorithm. Results showed that fused NDVI could be estimated for the three sites but could not detect changes over time. Combining this method with field data collection and other types of analyses may be useful for kudzu monitoring and management

Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively