accuracy than LR models, and the models were able to explain up to 83% within-field yield variability. Model based yield maps demonstrated within-field yield differences better than yield monitor data. Spatial variability of crop yield was generally lower than temporal variability. Topographic properties were found to play significant role in within-field yield differences. Areas with lower slope were found to have higher yield suggesting the need to consider topographic variabilities in implementation of agricultural practices. Improved understanding on processes underlying spatial and temporal variability of crop yield can help develop management practices for optimal productivity with improved environmental quality.

Timely and reliable agricultural production forecasts are critical to make informed food policy decisions and enable rapid responses to emerging food shortfalls. Sub-Saharan Africa is subject to highly variable yield, production and consumption, occasioned by high climate variability, rapidly increasing populations, and limited financial capacity. This review examines the current status of the remote sensing (RS) tools, products, methodologies and data that can help to improve agricultural crop production forecasting systems.

Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones’ geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. Presents a well-integrated collection of chapters, with quality, consistency and continuity Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented Covers geospatial data, with codes available through shared links

Farmers and researchers are aware of spatial variation in grain yield within farms or fields. Fertilizer management may be improved if techniques can be developed to identify grain yield variations in wheat fields. Aerial color infrared (CIR) photography was used to identify winter wheat (Triticum aestivum L.) canopy biomass variability in the Spring of the growing season. Low yielding areas identified from CIR photography were associated with shallow soil profiles consistent with soil forming factors of the region, and were significantly different from average and high yielding areas. The high yielding areas were located within a few meters of a drainage way, and were not significantly different than the average yielding areas except in one field with a deep soil profile and low variance. Fields with heterogeneous CIR photographs had high variances because of many dissimilar inclusions. CIR photography, although useful to distinguish vegetational differences, requires complex timing, ground verification, and correction to estimate yield variability. A geographic information system (GIS) was used to overlay photo interpreted biomass and soil map units. The overlay analysis allowed construction of a higher (first) order soil map indicating inclusions. Area calculation of the inclusions and map units using a GIS function combined with estimated yield (no variance estimates or confidence intervals associated with the estimated yield) data suggests fertilizer management with a first order soil map to increase fertilizer efficiency by up to six percent. Future research combining remotely-sensed subsidiary variables correlated with moisture supply capacity estimates from soil survey methods may assess, using relatively new spatially dependent interpolation methods, the local and regional variation in wheat grain yield.

Dynamic monitoring of crop phenotypic traits (e.g., LAI, plant height, biomass, nitrogen, yield et al.) is essential for exploring crop growth patterns, breeding new varieties, and determining optimized strategies for crop management. Traditional methods for determining crop phenotypic traits are mainly based on field sampling, handheld instrument measurement, and mechanized high-throughput platforms, which are time-consuming, and have low efficiency and incomplete spatial coverage. The development of crop science requires more rapid and accurate access to field-based crop phenotypes. Remote sensing provides a novel solution to quantify crop structural and functional traits in a timely, rapid, non-invasive and efficient manner. With the development of burgeoning remote sensing sensors and diversified algorithms, a range of crop phenotypic traits have been determined, including morphological parameters, spectral and textural characteristics, physiological traits, and responses to abiotic/biotic stresses in different environments. In addition, research advances in varying disciplines beyond agricultural sciences, such as engineering, computer science, molecular biology, and bioinformatics, have brought new opportunities for further development of remote sensing-based methods and technologies to gain more quantitative information on crop structure and function in complex environments

In light of global environmental change, population pressure, and food production demands, there is considerable value in mapping biogeographic crop niche and characterizing crop productivity at multiple scales to enhance the impact of agricultural improvement across Africa. Crop system research has advanced sustainable strategies for intensifying food production; however, questions regarding where to implement innovative technologies are largely unresolved.This dissertation focuses on four geographic questions: (1) Where is the fundamental climate niche of maize, pigeonpea, and sorghum across Africa? (2) Where are marginal lands in Malawi and what are the underlying drivers of marginality? (3) Based on the drivers of marginal maize production, what are geographic scaling options for integration of pigeonpea into maize-based cropping systems? (4) What spatial resolutions are effective for conducting precision agriculture at the farm scale in smallholder systems? Overarching themes within the geographic discipline such as the modifiable areal unit problem and ecological fallacy problem underpin this research. Marginal areas for maize are highlighted at the Africa and Malawi scales and overlain with the optimal climate niche for crops such as sorghum and pigeonpea that offer multiple ecosystem services (e.g., soil rehabilitation through nitrogen fixation). Crop productivity is evaluated at scales relative to policy making delineations in Malawi (i.e., country, district, and extension planning area) to disentangle heterogeneity at local scales that may appear homogeneous at broader scales. At the Malawi farm scale, this research included the use of a small unmanned aerial system (sUAS), national government satellites (e.g., Sentinel-2), and commercial satellites (e.g., SPOT 6). Spectral measurements of crop status were evaluated at multiple spatial resolutions (ranging from 0.07-20-m) to determine what spatial resolutions and what spectral indices are most effective for estimating crop yields and crop chlorophyll.Results of this research include high spatial resolution maps of maize, pigeonpea, and sorghum suitability across Africa, indicating that pigeonpea and sorghum occupy unique agroecological zones throughout the continent (e.g., sorghum in the Sahel region). Similarly, pigeonpea suitability in Malawi occupies a greater land area than the extent to which it is currently cultivated, demonstrating that integration into maize-based cropping systems, particularly where soil is marginal, can have beneficial scaling outcomes. For the smallholder farm scale, problems of clouds and satellite revisit rates have not yet been overcome for precision agriculture. In this regard, sUAS are a promising option for relating spectral signals to on-farm measurements of crop status. Evidence from drone flights conducted at two experimental farms in the central region of Malawi (Nyambi and Ntubwi) suggest that spatial resolutions closer to the plant scale (i.e., 14-27-cm) are most effective for relating spectral imagery to crop status. Moreover, the green normalized difference vegetation index (GNDVI) and green soil adjusted vegetation index (GSAVI) were consistently correlated with crop chlorophyll and yield, illustrating that a broad range of indices should be evaluated for precision agriculture.

Crop models and remote sensing techniques have been combined and applied in agriculture and crop estimation on local and regional scales, or worldwide, based on the simultaneous development of crop models and remote sensing. The literature shows that many new remote sensing sensors and valuable methods have been developed for the retrieval of canopy state variables and soil properties from remote sensing data for assimilating the retrieved variables into crop models. At the same time, remote sensing has been used in a staggering number of applications for agriculture. This book sets the context for remote sensing and modelling for agricultural systems as a mean to minimize the environmental impact, while increasing production and productivity. The eighteen papers published in this Special Issue, although not representative of all the work carried out in the field of Remote Sensing for agriculture and crop modeling, provide insight into the diversity and the complexity of developments of RS applications in agriculture. Five thematic focuses have emerged from the published papers: yield estimation, land cover mapping, soil nutrient balance, time-specific management zone delineation and the use of UAV as agricultural aerial sprayers. All contributions exploited the use of remote sensing data from different platforms (UAV, Sentinel, Landsat, QuickBird, CBERS, MODIS, WorldView), their assimilation into crop models (DSSAT, AQUACROP, EPIC, DELPHI) or on the synergy of Remote Sensing and modeling, applied to cardamom, wheat, tomato, sorghum, rice, sugarcane and olive. The intended audience is researchers and postgraduate students, as well as those outside academia in policy and practice.