Irrigated lands assessment for water management: Technique test

A procedure for estimating irrigated land using full frame LANDSAT imagery was demonstrated. Relatively inexpensive interpretation of multidate LANDSAT photographic enlargements was used to produce a map of irrigated land in California. The LANDSAT and ground maps were then linked by regression equations to enable precise estimation of irrigated land area by county, basin, and statewide. Land irrigated at least once in California in 1979 was estimated to be 9.86 million acres, with an expected error of less than 1.75% at the 99% level of confidence. To achieve the same level of error with a ground-only sample would have required 3 to 5 times as many ground sample units statewide. A procedure for relatively inexpensive computer classification of LANDSAT digital data to irrigated land categories was also developed. This procedure is based on ratios of MSS band 7 and 5, and gave good results for several counties in the Central Valley

Remote sensing research for agricultural applications

Materials and methods used to characterize selected soil properties and agricultural crops in San Joaquin County, California are described. Results show that: (1) the location and widths of TM bands are suitable for detecting differences in selected soil properties; (2) the number of TM spectral bands allows the quantification of soil spectral curve form and magnitude; and (3) the spatial and geometric quality of TM data allows for the discrimination and quantification of within field variability of soil properties. The design of the LANDSAT based multiple crop acreage estimation experiment for the Idaho Department of Water Resources is described including the use of U.C. Berkeley's Survey Modeling Planning Model. Progress made on Peditor software development on MIDAS, and cooperative computing using local and remote systems is reported as well as development of MIDAS microcomputer systems

ACCURACIES, ERRORS, AND UNCERTAINTIES OF GLOBAL CROPLAND PRODUCTS

ABSTRACT ACCURACIES, ERRORS, AND UNCERTAINTIES OF GLOBAL CROPLAND PRODUCTS By Kamini Yadav University of New Hampshire, May 2019 Global cropland products are continuously being produced at different spatial resolutions using remotely sensed satellite imagery. Recently, with our increased accessibility to higher computing processing, three different cropland extent maps have been developed as a part of Global Food Security-Support Analysis Data (GFSAD) project at three spatial resolutions (i.e., GFSAD1km, GFSAD250m, and GFSAD30m). All cropland maps should be assessed for their accuracy, errors, and uncertainty for various agriculture monitoring applications. However, in previous assessment efforts appropriate assessment strategies have not always been applied and many have reported only a single accuracy measure for the entire world. This research was divided into four components to provide more attention and focus on the accuracy assessment of large area cropland products. First, a valid assessment of cropland extent maps was performed addressing different strategies, issues, and constraints depending upon various conditions related to the cropland distribution, proportion, and pattern present in each continent. This research focused on dealing with some specific issues encountered when assessing the cropland extent of North America (confined to the United States), Africa and Australia. Continent-specific sampling strategies and accuracy assessments were performed within homogenous regions (i.e., strata) of different continents to ensure that an appropriate reference data set was collected to generate rigorous and valid accuracy results indicative of the actual cropland proportion. Second, all the three different GFSAD cropland extent maps were assessed using appropriate sampling and collection of a cropland reference data based on the cropland distribution and proportion for different regions in the entire world. In addition to the accuracy assessment, the cropland extent maps developed at the three spatial resolutions were compared to investigate the differences among them and provide guidance for users to select the appropriate resolution given different agriculture field sizes. The comparison of three different GFSAD cropland extent maps was performed based on the similarity of the cropland area proportion (CAP) and landscape clumping at different spatial resolutions to provide specific recommendations for when to apply these maps in different agriculture field sizes. Third, an issue was discovered with the accuracy assessment of 30m global cropland extent map (i.e., GFSAD30m) in that insufficient samples were collected resulting in an ineffective assessment when the cropland map class was rare as occurred in some regions around the world. This research evaluated the sampling designs for different cropland regions to achieve sufficient samples and effective accuracy of rare cropland map class by comparing the distribution, allocation of samples and accuracy measures. The evaluation of sampling designs demonstrated that the cropland regions of \u3c15% CAP must be sampled with an appropriate stratified sampling combined with a predetermined minimum sample size for each map class. Finally, the accuracy assessment of all thematic maps (e.g., crop type maps) needs sufficient reference data to conduct a valid assessment. The availability of reference data is a severely limiting factor over large geographic region because of the time, effort, cost, and accessibility in different parts of the world. The objectives of this research were to augment and extend the limited availability of crop type reference data using non-ground-based sources of crop type information for creating and assessing large area crop type maps. There is the potential to either interpret the photographs available from Google Street View (GSV) or classify High Resolution Imagery (HRI) using a phenology-based classification approach to generate additional reference data within similar agriculture ecological zones (AEZs) based on the crop characteristics, their types, and growing season. These two methods of augmenting and extending crop type reference data were developed for the United States (US) where high-quality crop type reference data already exist so that the methods could be effectively and efficiently tested. This research described a tale of three continents providing recommendations to adapt accuracy assessment strategies and methodologies for assessing global cropland extent maps. Based on these results, the assessment and comparison of different resolution GFSAD cropland extent maps were performed to provide specific recommendations for when to apply each of the maps for agriculture monitoring based on the agriculture field sizes. When assessing the cropland extent maps, different sampling strategies perform differently in the various cropland proportion regions and therefore, must be selected according to the cropland extent maps to be assessed. Finally, this research concluded that the limited crop type reference data can be effectively extended using a phenology-based classification approach and is more efficient than the interpretation of photographs collected from GSV

Bias of area counted from sub-pixel map:Origin and correction

With the increasingly widespread use of sub-pixel mapping techniques in land cover/use mapping, more accurate area information is often required for a specific land cover type in a particular study region. However, the bias of area counted from sub-pixel maps (called area bias below), and the inadequate understanding of the area bias's origin and influential factors pose a challenge to using this information accurately. Traditional model-assisted estimators combining the map and the reference sample showed unreliable performances in the case of small sample sizes collected in target regions. This work presented a theoretical analysis of the origin of area bias. It then proposed a novel bias-adjusted estimator which can effectively deal with the small sample sizes. The theoretical analysis illustrated that area bias mainly originates from two terms, i.e., the abundance-dependent error and the probability distribution of abundances. We next developed a stratified bias-adjusted area estimator named the two-term method (TTM) by incorporating the sub-pixel map and a reference sample obtained from both target and external regions. We validated the effects of different sub-pixel mapping methods, different spatial resolutions, the varying spatial structures of statistical units on area bias, and the performance of TTM in correcting the biased areas in multiple cases. The results showed that area bias varied from zero to approximately 20% with the variation of three influential factors. TTM effectively corrected the biased area values to nearly the true values, showing approximate equivalence with the traditional stratified regression estimator (STRE) when adequate reference samples are collected sorely inside target regions. However, in cases of small samples from target regions, TTM showed significant superiority over STRE in reducing the variance and MSE due to the incorporation of external reference samples. We conclude that the theoretical analysis resulted in a better understanding of area bias counted from sub-pixel maps and an improved area estimator for dealing with the cases of small sample sizes inside target regions.</p

Vegetation change detection and soil erosion risk assessment modelling in the Man River basin, Central India

Land use change directly increased soil erosion risk, which is a very sensitive environmental issue in Central India. To evaluate the response of land use changes on soil erosion risk, research was implemented using remote sensing techniques, coupled with ground information, to develop an integrated modelling approach to study the factors driving land use changes in the Man River basin, Central India. Results were used to assess the impact of land use change on soil erosion risk. First, a series of sub methods were applied to monitor and verify land use land cover change in the study area which included pre-processing, classification and assessment of land use transaction from 1971 to 2013 using Landsat time series imagery. Additionally, an independent spatial assessment of deforestation, forest degradation and responsible drivers for the period 2009-2013 was conducted to enable a deeper analysis of forestry activates using the GIS based direct interpretation approach. The research also developed a robust accuracy assessment method to check the quality of the 2009 and 2013 classification maps using good quality Google Earth TM imagery and a field measured GPS dataset. These approaches were largely based on the GOFC- GOLD (2010) and IPCC good recommendations for land use land cover mapping and verification. The information obtained from an accuracy assessment was also used to estimate deforestation area and construct confidence intervals that reflect the uncertainty of the area estimates obtained. Such analysis is rarely applied in current published verification assessments. In the second phase of the study, a Geo-spatial interface for process-based Water Erosion Prediction Project (GeoWEPP) was implemented, to estimate the response of land use and land cover change on soil erosion risk in several scenarios derived from both ground and satellite based precipitation, DEMs and vegetation change. GeoWEPP was used at the hillslope scale in three selected watersheds within the Man River basin using Landsat, LISSIII, Cartosat-1, ASTER, SRTM, TRMM and ground based datasets. The results highlight that the study developed a realistic approach using remote sensing techniques to understand the pattern and process of landscape change in the Man River basin and its response on soil erosion risk. Over the last four decades, forest and agriculture areas were found to be the most dynamic land use /land cover categories. During the last four decades, around 54200 ha (33.7 %) forest area has been decreased due to the expansion of agriculture, forest harvesting and infrastructure development. The direct interpretation approach estimated similar patterns of deforestation and forest degradation associated with iii drivers for the 2009 to 2013 time period, but this approach also provided more accurate and location specific information than automatic analysis. The overall correspondence between the map and reference data are a good measure for 2009 and 2013; 94.03 % and 92.8 % respectively. User‘s and producer‘s accuracies of individual classes range from 75 % to 99 %. Using the accuracy assessment data and a simple set of equations, an error-adjusted estimate of the area of deforestation was obtained (± 95% confidence interval) of 23382 ± 550 ha. The estimated average annual soil loss for all three watersheds is 21 T/ha which was found to be comparable to similar studies carried out in the study region. The highest soil loss rates occurred in areas of agriculture (301 T. /ha /yr) and fallow land (158 T/ha/yr), while the lowest rates were recorded in forest land (33.45 T/ha/yr). Agriculture extension (316.5 ha) due to forest harvesting (234 ha) in the last four decades is one of the significant drivers to speed up soil erosion (7.37 T/ha/yr.) in all three watersheds. The spatial pattern of erosion risk indicates that areas with forest cover have minimum rates of soil erosion, while areas with extensive human intervention such as agriculture and fallow land, have high estimated rates of soil erosion. The different DEMs generated varied topographic and hydrologic attributes, which in turn led to significantly different erosion simulations. GeoWEPP using Cartosat-1 (30 m) and SRTM (90 m) produced the most accurate estimation of soil loss which was close to similar already published studies in the area. TRMM rainfall data has good to use as a rainfall parameter for soil erosion risk mapping in study area. Overall, the integrated approach using remote sensing and GIS allowed a clear understanding of the factors that drive land use/land cover change to be developed and enabled the impact of this change on soil erosion risk in the Man River basin, Central India to be assessed

Earth Resources Laboratory research and technology

The accomplishments of the Earth Resources Laboratory's research and technology program are reported. Sensors and data systems, the AGRISTARS project, applied research and data analysis, joint research projects, test and evaluation studies, and space station support activities are addressed

Development of Landsat-based Technology for Crop Inventories: Appendices

There are no author-identified significant results in this report

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R2 = 0.90, NRMSE = 0.12), followed by RFR (R2 = 0.90 NRMSE = 0.15), and SVR (R2 = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage

Crop growth and yield monitoring in smallholder agricultural systems:a multi-sensor data fusion approach

Smallholder agricultural systems are highly vulnerable to production risks posed by the intensification of extreme weather events such as drought and flooding, soil degradation, pests, lack of access to agricultural inputs, and political instability. Monitoring the spatial and temporal variability of crop growth and yield is crucial for farm management, national-level food security assessments, and famine early warning. However, agricultural monitoring is difficult in fragmented agricultural landscapes because of scarcity and uncertainty of data to capture small crop fields. Traditional pre- and post-harvest crop monitoring and yield estimation based on fieldwork is costly, slow, and can be unrepresentative of heterogeneous agricultural landscapes as found in smallholder systems in sub-Saharan Africa. Devising accurate and timely crop phenology detection and yield estimation methods can improve our understanding of the status of crop production and food security in these regions.Satellite-based Earth observation (EO) data plays a key role in monitoring the spatial and temporal variability of crop growth and yield over large areas. The small field sizes and variability in management practices in fragmented landscapes requires high spatial and high temporal resolution EO data. This thesis develops and demonstrates methods to investigate the spatiotemporal variability of crop phenology detection and yield estimation using Landsat and MODIS data fusion in smallholder agricultural systems in the Lake Tana sub-basin of Ethiopia. The overall aim is to further broaden the application of multi-sensor EO data for crop growth monitoring in smallholder agricultural systems.The thesis addressed two important aspects of crop monitoring applications of EO data: phenology detection and yield estimation. First, the ESTARFM data fusion workflow was modified based on local knowledge of crop calendars and land cover to improve crop phenology monitoring in fragmented agricultural landscapes. The approach minimized data fusion uncertainties in predicting temporal reflectance change of crops during the growing season and the reflectance value of fused data was comparable to the original Landsat image reserved for validation. The main sources of uncertainty in data fusion are the small field size and abrupt crop growth changes between the base andviiprediction dates due to flooding, weeding, fertiliser application, and harvesting. The improved data fusion approach allowed us to determine crop phenology and estimate LAI more accurately than both the standard ESTARFM data fusion method and when using MODIS data without fusion. We also calibrated and validated a dynamic threshold phenology detection method using maize and rice crop sowing and harvest date information. Crop-specific phenology determined from data fusion minimized the mismatch between EO-derived phenometrics and the actual crop calendar. The study concluded that accurate phenology detection and LAI estimation from Landsat–MODIS data fusion demonstrates the feasibility of crop growth monitoring using multi-sensor data fusion in fragmented and persistently cloudy agricultural landscapes.Subsequently, the validated data fusion and phenology detection methods were implemented to understand crop phenology trends from 2000 to 2020. These trends are often less understood in smallholder agricultural systems due to the lack of high spatial resolution data to distinguish crops from the surrounding natural vegetation. Trends based on Landsat–MODIS fusion were compared with those detected using MODIS alone to assess the contribution of data fusion to discern crop phenometric change. Landsat and MODIS fusion discerned crop and environment-specific trends in the magnitude and direction of crop phenology change. The results underlined the importance of high spatial and temporal resolution EO data to capture environment-specific crop phenology change, which has implications in designing adaptation and crop management practices in these regions.The second important aspect of the crop monitoring problem addressed in this thesis is improving crop yield estimation in smallholder agricultural systems. The large input requirements of crop models and lack of spatial information about the heterogeneous crop-growing environment and agronomic management practices are major challenges to the accurate estimation of crop yield. We assimilated leaf area index (LAI) and phenology information from Landsat–MODIS fusion in a crop model (simple algorithm for yield estimation: SAFY) to obtain reasonably reliable crop yield estimates. The SAFY model is sensitive to the spatial and temporal resolution of the calibration input LAI, phenology information, and the effective light use efficiency (ELUE) parameter, which needs accurate field level inputs during modelviiioptimization. Assimilating fused EO-based phenology information minimized model uncertainty and captured the large management and environmental variation in smallholder agricultural systems.In the final research chapter of the thesis, we analysed the contribution of assimilating LAI at different phenological stages. The frequency and timing of LAI observations influences the retrieval accuracy of the assimilating LAI in crop growth simulation models. The use of (optical) EO data to estimate LAI is constrained by limited repeat frequency and cloud cover, which can reduce yield estimation accuracy. We evaluated the relative contribution of EO observations at different crop growth stages for accurate calibration of crop model parameters. We found that LAI between jointing and grain filling has the highest contribution to SAFY yield estimation and that the distribution of LAI during the key development stages was more useful than the frequency of LAI to improve yield estimation. This information on the optimal timing of EO data assimilation is important to develop better in-season crop yield forecasting in smallholder systems

Key issues in rigorous accuracy assessment of land cover products

© 2019 Accuracy assessment and land cover mapping have been inexorably linked throughout the first 50 years of publication of Remote Sensing of Environment. The earliest developers of land-cover maps recognized the importance of evaluating the quality of their maps, and the methods and reporting format of these early accuracy assessments included features that would be familiar to practitioners today. Specifically, practitioners have consistently recognized the importance of obtaining high quality reference data to which the map is compared, the need for sampling to collect these reference data, and the role of an error matrix and accuracy measures derived from the error matrix to summarize the accuracy information. Over the past half century these techniques have undergone refinements to place accuracy assessment on a more scientifically credible footing. We describe the current status of accuracy assessment that has emerged from nearly 50 years of practice and identify opportunities for future advances. The article is organized by the three major components of accuracy assessment, the sampling design, response design, and analysis, focusing on good practice methodology that contributes to a rigorous, informative, and honest assessment. The long history of research and applications underlying the current practice of accuracy assessment has advanced the field to a mature state. However, documentation of accuracy assessment methods needs to be improved to enhance reproducibility and transparency, and improved methods are required to address new challenges created by advanced technology that has expanded the capacity to map land cover extensively in space and intensively in time