Using satellite remote sensing and hydrologic modeling to improve understanding of crop management and agricultural water use at regional to global scales.

Thesis (Ph. D.)--Boston UniversityCroplands are essential to human welfare. In the coming decades , croplands will experience substantial stress from climate change, population growth, changing diets, urban expansion, and increased demand for biofuels. Food security in many parts of the world therefore requires informed crop management and adaptation strategies. In this dissertation, I explore two key dimensions of crop management with significant potential to improve adaptation pathways: irrigation and crop calendars. Irrigation, which is widely used to boost crop yields, is a key strategy for adapting to changes in drought frequency and duration. However, irrigation competes with household, industrial, and environmental needs for freshwa t er r esources. Accurate information regarding irrigation patterns is therefore required to develop strategies that reduce unsustainable water use. To address this need, I fused information from remote sensing, climate datasets, and crop inventories to develop a new global database of rain-fed, irrigated, and paddy croplands. This database describes global agricultural water management with good realism and at higher spatial resolution than existing maps. Crop calendar management helps farmers to limit crop damage from heat and moisture stress. However, global crop calendar information currently lacks spatial and temporal detail. In the second part of my dissertation I used remote sensing to characterize global cropping patterns annually, from 2001-2010, at 0.08 degree spatial resolution. Comparison of this new dataset with existing sources of crop calendar data indicates that remote sensing is able to correct substantial deficiencies in available data sources. More importantly, the database provides previously unavailable information related to year-to-year variability in cropping patterns. Asia, home to roughly one half of the Earth's population, is expected to experience significant food insecurity in coming decades. In the final part of my dissertation, I used a water balance model in combination with the data sets described above to characterize the sensitivity of agricultural water use in Asia to crop management. Results indicate that water use in Asia depends strongly on both irrigation and crop management, and that previous studies underestimate agricultural water use in this region. These results support policy development focused on improving the resilience of agricultural systems in Asia

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

Assessment of Time-Series MODIS Data for Cropland Mapping in the U.S. Central Great Plains

The goal of this study was to further investigate the potential of MODIS NDVI 250-m data for crop spectral characterization, discrimination, and mapping in the Great Plains of the USA using various exploratory approaches. GIS operations, and reference data refinement using clustering and visual assessment of each crop's NDVI cluster profiles in Nebraska, demonstrated that it is possible to devise an alternative reference data set and refinement plan that redresses the unexpected loss of training and validation data. A pixel-level analysis of the time-series MODIS 250-m NDVI for 1,288 field sites representing each of the eight cover types under investigation across Nebraska found that each crop type had a distinctive MODIS 250-m NDVI profile corresponding to the crop calendar. A visual and statistical comparison of the average NDVI profiles showed that the crop types were separable at different times of the growing season based on their phenology-driven spectral-temporal differences. In Kansas, an initial investigation revealed that there was near-complete agreement between the winter wheat crop profiles but that there were some minor differences in the crop profiles for alfalfa and summer crops between 2001 and 2005. However, the profiles of summer crops - corn, grain sorghum, and soybeans - displayed a shift to the right by at least 1 composite date, indicative of possible late crop planting and emergence. Alfalfa and summer crops, seem to suggest that time series NDVI response curves for crops over a growing period for one year of valid ground reference data may not be used to map crops for a different year without taking into account the climatic and/or environmental conditions of each year

PhenoRice:A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis

Developing Earth Observations Requirements for Global Agricultural Monitoring: Toward a Multi-Mission Data Acquisition Strategy

Global food supply and our understanding of it have never been more important than in today's changing world. For several decades, Earth observations (EO) have been employed to monitor agriculture, including crop area, type, condition, and yield forecasting processes, at multiple scales. However, the EO data requirements to consistently derive these informational products had not been well defined. Responding to this dearth, I have articulated spatially explicit EO requirements with a focus on moderate resolution (10-70m) active and passive remote sensors, and evaluate current and near-term missions' capabilities to meet these EO requirements. To accomplish this, periods requiring monitoring have been identified through the development of agricultural growing season calendars (GSCs) at 0.5 degrees from MODIS surface reflectance. Second, a global analysis of cloud presence probability and extent using MOD09 daily cloud flags over 2000-2012 has shown that the early-to-mid agricultural growing season (AGS) - an important period for monitoring - is more persistently and pervasively occluded by clouds than is the late and non-AGS. Third, spectral, spatial, and temporal resolution data requirements have been developed through collaboration with international agricultural monitoring experts. These requirements have been spatialized through the incorporation of the GSCs and cloud cover information, establishing the revisit frequency required to yield reasonably clear views within 8 or 16 days. A comparison of these requirements with hypothetical constellations formed from current/planned moderate resolution optical EO missions shows that to yield a scene at least 70% clear within 8 or 16 days, 46-55% or 10-32% of areas, respectively, need a revisit more frequent than Landsat 7 & 8 combined can deliver. Supplementing Landsat 7 & 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. No single mission/observatory can consistently meet requirements throughout the year, and the only way to meet a majority (77-94% for &#8805;70% clear; 47-73% for 100% clear) of 8 day requirements is through coordination of multiple missions. Still, gaps exist in persistently cloudy regions and periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring

Fingerprint of rice paddies in spatial-temporal dynamics of atmospheric methane concentration in monsoon Asia.

Agriculture (e.g., rice paddies) has been considered one of the main emission sources responsible for the sudden rise of atmospheric methane concentration (XCH4) since 2007, but remains debated. Here we use satellite-based rice paddy and XCH4 data to investigate the spatial-temporal relationships between rice paddy area, rice plant growth, and XCH4 in monsoon Asia, which accounts for ~87% of the global rice area. We find strong spatial consistencies between rice paddy area and XCH4 and seasonal consistencies between rice plant growth and XCH4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since 2007, which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial-temporal dynamics of XCH4 in monsoon Asia

Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale

Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between -1.1 and 0.99 and -1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions

Analysis of MODIS 250 m NDVI Using Different Time-Series Data for Crop Type Separability

The primary objectives of this research were to: (1) investigate the use of different compositing periods of NDVI values of time-series MODIS 250 m data for distinguishing major crop types on the central Great Plains of the U.S. and (2) analyze collection 5 versus collection 4 time-series MODIS 250 m NDVI data to separate crop types. NDVI profiles extracted from different compositing periods for 2001 and 2005 were analyzed to see whether 8-day (and dual-8-day) composited NDVI data, as compared to 16-day composited NDVI data, would show finer scale spectral-temporal variability that would result in improved crop separability. NDVI value profiles were also extracted from different collection versions (4 and 5) for 2001 and 2005 (collection 5 only). Phenological curves for all crops and all datasets were created and visually inspected and JM distance statistical analysis was performed to compare separability of the crops for both the compositing period analysis and the collection version analysis. Major conclusions and findings for the compositing period analysis include: (1) there are statistical differences among the different compositing period datasets, (2) time-series data that have shorter compositing periods are more effective in separating crop types, and (3) any observed differences should be interpreted with care and in the context of variations in environmental conditions for a given growing season. For the collection version analysis the major finding was that, contrary to expectations, the most recent version of time-series MODIS 250 m data (version 5) was inferior to version 4 in terms of crop separability; however the analysis did not suggest reasons for the outcome. As a result of this research, it is tentatively recommended (subject to further research) that MODIS NDVI data (a) from a shorter 8-day compositing period and (b) from collection 4 should be used where possible for crop-type mapping in the study region