Waterlogging is the accumulation of excess water in the rootzone. When soils are waterlogged the rootzone is either saturated or submerged with water. As a result, waterlogging prevents aeration of the rootzone which has adverse effects on crop growth. Waterlogging is an issue in both irrigated and rain-fed agriculture and crop productivity issues related to waterlogging are reported around the globe. As most terrestrial crops are within days susceptible to the negative effects of waterlogging, timely information on waterlogging and its presence spatially is beneficial. In that regard, to understand the impact of waterlogging on agriculture and combat consequential negative effect, satellite remote sensing can play a role. However, in the field of satellite Remote Sensing of agriculture, waterlogging has so far received little attention. Previous related research focused on remote sensing of inundation or monitoring surface water, but waterlogging in agriculture is an overlooked subject. Little is known about how waterlogging is present in (irrigated) agriculture and what the ability of different remote sensing techniques is to detect and monitor waterlogging. Therefore, this thesis aimed to extend knowledge on how waterlogging influences agricultural monitoring with satellite remote sensing. Ultimately, to set footsteps towards monitoring waterlogging with satellite remote sensing. The results presented evolve around a sugarcane plantation in Xinavane, Mozambique. The plantation served as a case study to demonstrate different satellite remote sensing observations in the context of waterlogging. First, the case study is presented and a describption is provided on the ground data collected. By providing remote sensing evaporation estimates, the high demand of irrigation water is illustrated. Vast quantities of water is needed to sustain sugarcane crop growth in the semi-arid environment of the plantation. To continue, with a thorough literature review and the case study it is demonstrated waterlogging is a major issue burdening crop productivity. By assessing different remote sensing evaporation algorithms the results showed currently available evaporation estimates interpret waterlogging stress as a need to irrigate. This implies, before evaporation estimates from satellite data can play a role in optimizing field-scale water use in irrigated areas, evaporation algorithms must be able to identify water stress only in the case of water deficit in the root-zone. Throughout the chapter the presence of waterlogging or crop response to waterlogging is illustrated in different satellite remote sensing observations. In sum, the results imply a need to integrate observations of multiple sensors and potentially ancillary data (e.g. DEMs) to unravel how to monitor waterlogging with satellite remote sensing. In search for the influence of waterlogging on agricultural monitoring, the research continued by comparing optical vegetation indices, radar vegetation indices, and sugarcane yield over the growing season in the plantation. The analysis gave an interesting and unexpected result. Contrary to the expectation the results showed a negative correlation between the Cross Ratio (CR) and sugarcane yield over the growing season. A modeling study proved the negative correlation results from a change in the sugarcane's internal composition which affects the dielectric constant of sugarcane canopies observed. The chemical composition of plant water in sugarcane changes over the growing season. As a consequence of sucrose accumulation in the stalk, water is increasingly bound to sucrose and this process lowers the dielectric constant. %The results predominantly show a decrease in observed vegetation water content, as a result of a change in chemical composition due to an increase in sucrose accumulation, lowers the backscattered signal. To follow up, active and passive microwave observations, optical vegetation indices, and production data are evaluated in different seasons. In addition to a temporal change of sucrose and moisture, the results showed vertically the sucrose-moisture distribution changes as well over the growing season. Therefore, the vertical distribution of sucrose - plant moisture influences the dielectric constant and, hence, the backscattered signal. The results highlight the VV backscatter responds to the stalk biomass, which is also the reservoir of sucrose in the sugarcane crop. Finally, the influence of waterlogging on Sentinel-1 backscatter was detected through benchmarking with passive microwave observations, optical vegetation indices, and production data in a period where waterlogging was reported. Despite a thick sugarcane canopy, an increase in VH and VV polarizations was observed as a result of waterlogging. The increase was present at all stages during the growing season. The difference in backscatter as a result of waterlogging was highest in the VH backscatter. Also, the effect of waterlogging is translated through to the CR, which proves CR can play a role in the discrimination of waterlogging. The results presented in this thesis help to further understand the influence of waterlogging in agricultural monitoring. Also, this work shows to correctly interpret irrigation estimates and crop development, waterlogging and sucrose development need to be flagged or otherwise considered during the growing season. Especially radar observations from Sentinel-1 backscatter appeared to be useful in monitoring waterlogging and sucrose development.@en

Sentinel-1 observes the whole globe every 12 days (6 days when both satellites were operational) and provides a wealth of data relevant to agriculture. Sugarcane cultivators could potentially benefit from these data by using them to assist operational and management practices. However, first, thorough understanding is needed of Sentinel-1 backscatter and its behavior over sugarcane canopies. In this study, we aimed to improve understanding of how Sentinel-1 backscatter responds to sugarcane yield variability and waterlogging. In order to do so we focused on an irrigated sugarcane plantation in Xinavane, Mozambique. In the analysis presented, we assessed different polarizations, their ratio, and benchmarked them against optical indices and passive microwave observations in different seasons. With the help of a large sugarcane yield dataset, we analyzed how backscatter relates to sucrose yield variability in different seasons. We found VV backscatter related to the stalk development, the most important reservoir for sucrose accumulation. In addition, in a season with reported waterlogging, optical and radar observations showed a delay in sugarcane crop development. Further analysis showed the presence of water underneath the canopy caused an increase in all polarizations and the cross ratio (CR). The results imply that Sentinel-1 backscatter contains information on both waterlogging under the canopy as well as sucrose development in the stalk. By isolating and quantifying the impact of waterlogging on backscatter, it will be possible to further quantify sucrose development with backscatter observations and identify waterlogging simultaneously.

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Satellite sensors have been promoted widely as a technology to monitor crop canopies to encourage sustainable agriculture. Different remote sensing technologies are able to detect crop stress and water shortages, with a special emphasis on water stress. Improving water and crop productivity goes hand in hand. Moreover, crop stress resulting from waterlogging leads to suboptimal crop productivity. However, this has so far received little attention in literature and, consequently, technological development. This is surprising because approximately twenty percent of the global agricultural land suffers from the consequences of waterlogging and secondary soil salinization. In this study we focus on an irrigated sugarcane plantation in southern Mozambique burdened by waterlogging. We show how Sentinel-1 backscatter and Planet NDVI can be used to monitor sugarcane development. Our results demonstrate Sentinel-1 backscatter is able to monitor sucrose development and, in addition, how waterlogging influences the radar signals in different growth stages. Consequently, we show the potential to monitor sugarcane development and waterlogging simultaneously.@en

The growing competition for finite land and water resources and the need to feed an ever-growing population require new techniques to monitor the performance of irrigation schemes and improve land and water productivity. Datasets from FAO's portal to monitor Water Productivity through Open access Remotely sensed derived data (WaPOR) are increasingly applied as a cost-effective means to support irrigation performance assessment and identify possible pathways for improvement. This study presents a framework that applies WaPOR data to assess irrigation performance indicators, including uniformity, equity, adequacy, and land and water productivity differentiated by irrigation method (furrow, sprinkler, and centre pivot) at the Xinavane sugarcane estate, Mozambique. The WaPOR data on water, land, and climate are in near-real time and spatially distributed, with the finest spatial resolution in the area of 100gm. The WaPOR data were first validated agronomically by examining the biomass response to water, and then the data were used to systematically analyse seasonal indicators for the period 2015 to 2018 on g1/48000gha. The WaPOR-based yield estimates were found to be comparable to the estate-measured yields with ±20g% difference, a root mean square error of 19±2.5gtgha-1 and a mean absolute error of 15±1.6gtgha-1. A climate normalization factor that enables the spatial and temporal comparison of performance indicators are applied. The assessment highlights that in Xinavane no single irrigation method performs the best across all performance indicators. Centre pivot compared to sprinkler and furrow irrigation shows higher adequacy, equity, and land productivity but lower water productivity. The three irrigation methods have excellent uniformity (g1/494g%) in the four seasons and acceptable adequacy for most periods of the season except in 2016, when a drought was observed. While this study is done for sugarcane in one irrigation scheme, the approach can be broadened to compare other crops across fields or irrigation schemes across Africa with diverse management units in the different agroclimatic zones within FAO WaPOR coverage. We conclude that the framework is useful for assessing irrigation performance using the WaPOR dataset.

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Efficient irrigation water management for an 18,000 ha sugarcane plantation in Xinavane in southern Mozambique is a challenge. Sugarcane is an irrigation intensive crop and its productivity is sensitive to water stress. Options to adopt field water management best practices and proper irrigation scheduling are limited due to the lack of plot-level information on the actual crop water use and stress levels throughout the growing season. Due to heterogeneity in cropping calendar within the sugarcane plantation, at a certain point of time, different plots are at different growth stages. This makes scheme level irrigation scheduling complex and calls for frequent crop water use information. To fill this gap, this study presents a novel approach where a combination of satellite imagery with local weather data is used to provide daily evaporation rates. The Priestley–Taylor equation is applied to quantify evaporation (soil evaporation + transpiration) using radiation and temperature data from a meteorological station and spatial albedo estimates derived from the Sentinel-2 satellites. The results show 20 meter resolution maximum crop evaporation estimates can be derived with the proposed methodology. Additionally, the results show NDVI in the last two crop stages is able to distinguish between poor and good performing fields. Therefore, NDVI can be a useful index to estimate actual evaporation. First, the evaporation estimates were corrected for the crop stage using NDVI proxies and an additional stress indicator was used to calculate the actual evaporation flux spatially. The spatial evaporation estimates provide the water manager with information on actual crop water use and biomass development, which is relevant to both crop monitoring and irrigation management water management when drought-related stress is filtered.

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Waterlogging is an increasingly important issue in irrigated agriculture that has a detri-mental impact on crop productivity. The above-ground effect of waterlogging on crops is hard to distinguish from water deficit stress with remote sensing, as responses such as stomatal closure and leaf wilting occur in both situations. Currently, waterlogging as a source of crop stress is not considered in remote sensing-based evaporation algorithms and this may therefore lead to erroneous interpretation for irrigation scheduling. Monitoring waterlogging can improve evaporation models to assist irrigation management. In addition, frequent spatial information on waterlogging will provide agriculturalists information on land trafficability, assist drainage design, and crop choice. This article provides a scientific perspective on the topic of waterlogging by consulting literature in the disciplines of agronomy, hydrology, and remote sensing. We find the solution to monitor waterlogging lies in a multi-sensor approach. Future scientific routes should focus on monitoring waterlogging by combining remote sensing and ancillary data. Here, drainage parameters deduced from high spatial resolution Digital Elevation Models (DEMs) can play a crucial role. The proposed approaches may provide a solution to monitor and prevent waterlogging in irrigated agriculture.

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In this study the impact of sucrose accumulation in Sentinel-1 backscatter observations is presented and compared to Planet optical observations. Sugarcane yield data from a sugarcane plantation in Xinavane, Mozambique are used for this study. The database contains sugarcane yield of 387 fields over two seasons (2018-2019 and 2019-2020). The relation between sugarcane yield and Sentinel-1 VV and VH backscatter observation is analyzed by using the Normalized Difference Vegetation Index (NDVI) data as derived from Planet Scope optical imagery as a benchmark. The different satellite observations were compared over time to sugarcane yield to understand how the relation between the observations and yield evolves during the growing season. A negative correlation between yield and Cross Ratio (CR) from Sentinel-1 backscatter was found while a positive correlation between yield and Planet NDVI was observed. An additional modeling study on the dielectric properties of the crop revealed how the CR could be affected by sucrose accumulation during the growing season and supported the opposite correlations. The results shows CR contains information on sucrose content in the sugarcane plant. This sets a basis for further development of sucrose monitoring and prediction using a combination of radar and optical imagery.@en

Irrigation requirements are mostly determined by estimating the atmospheric evaporative demand, in combination with precipitation data to estimate the irrigation need per field. However, in case of a high groundwater table, the contribution of capillary rise is often not taken into account. Nevertheless, this flux can contribute significantly to the actual evaporation. Ignoring this flux in irrigation practices might lead to over-irrigation and reduced yields. The significance of the groundwater flux in a furrow irrigated sugarcane plantation in Mozambique with a shallow groundwater table is presented here. Groundwater levels in a sugarcane plantation in Xinavane in Mozambique were recorded in several fields for a duration of six months. The groundwater recordings, potential evaporation estimates from satellite remote sensing, and field data were combined in the Vegetation-AtMosPhere-Soil water model (VAMPS) that was set up to understand the effect of groundwater contribution on the actual evaporation of a sugarcane field. With the hydrological field representations set up, we analyzed whether the current furrow irrigation requirement in the plantation, of 1350 mm/year for furrow irrigation, is efficient. The results show that groundwater contribution to the transpiration flux reduces the need for irrigation in the study area. As such, we conclude that the current irrigation requirement is leading to over-irrigation. The incorporation of the groundwater contribution is needed to provide adequate estimations for irrigation. A reduction in irrigation for these fields will lead to a higher water productivity in the study area.

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Maharashtra is one of the States in India that has witnessed one of the highest rates of farmer suicides as proportion of total number of suicides. Most of the farmer suicides in Maharashtra are from semi-arid divisions such as Marathwada where cotton has been historically grown. Other dominant crops produced include cereals, pulses, oilseeds and sugarcane. Cotton (fibers), oilseeds and sugarcane providing highest value addition per unit cultivated area and cereals and pulses the least. Hence it is not surprising that smallholders take risks growing high value crops without “visualising” the risks it entails such as those corresponding to price and weather shocks. We deploy recently developed smallholder socio-hydrology modelling framework to understand the underlying dynamics of the crisis. It couples the dynamics of six main variables that are most relevant at the scale of a smallholder: water storage capacity (root zone storage and other ways of water storage), capital, livestock, soil fertility and fodder biomass. The hydroclimatic variability is accounted for at sub-annual scale and influences the socio-hydrology at annual scale. The model incorporates rule-based adaptation mechanisms (e.g., adjusting expenditures on food and fertilizers, selling livestocks) of smallholders when they face adverse conditions, such as high variability in rainfall or in agricultural prices. The model is applied to two adjoining divisions of Maharashtra: Marathwada and Desh. The former is the division with relatively higher farmer suicide rates than the latter. Diverse spatial data sets of precipitation, potential evaporation, soil, agricultural census based farm inputs, cropping pattern and prices are used to understand the dynamics of small farmers in these divisions, and to attribute farmer distress rates to soil types, hydroclimatic variability and crops grown. Comparative socio-hydrologic assessment across the two regions confirms existing narratives: low (soil) water storage capacities, no irrigation and poor access to alternative sources of incomes are to blame for the crisis, suggesting that smart indigenous solutions such as rain-water harvesting and better integration of smallholder systems to efficient agricultural supply chains are needed to tackle this development challenge.

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