Increases in crop production show increasing trends in agricultural intensification. These trends are predominant in the corn and soybean crop production of the United States' Midwest region. The increase of production and irrigation in the past 20 years is spatially diverse, with some regions in the Midwest intensifying agriculture at faster rates. Measurement of vegetation through the use of satellite remote sensing methods has gained popularity in recent years, with the refinement of existing retrieval algorithms and the development of new ones. Many different indices can be used to study the vegetation, including optical products such as the Normalized Difference Vegetation Index (NDVI) and the Leaf Area Index (LAI). However, the use of microwave remote sensing has some advantages over near optical methods as it is unaffected by weather conditions and can therefore collect data more regularly. Microwave remote sensing methods can retrieve the vegetation optical depth (VOD), a parameter which is related to the intensity of microwave signal extinction by vegetation and can be derived through the use of both passive and active microwave observations. In this study, these microwave remote sensing methods are used to look for evidence of agricultural intensification trends in the Midwest. The spatial distribution and intensity of trends is compared with trends in yield data and LAI. Trend analysis of soybean and corn yield in the Midwest show statistically significant increasing trends in the Western states of South Dakota, Nebraska, and parts of North Dakota. Similar spatial distributions were picked up in LAI and passive microwave data.

Droughts are a serious threat to the planet. Especially in agriculture, the consequences are devastating leading to reduced yields or crop failure with further implications for economics, politics and society. Being able to monitor and predict agricultural droughts is an important step to reduce agricultural vulnerability and secure livelihoods. However, few drought indicators measure the actual crop response towards the hydrological conditions. Remote sensing bears great potential for this challenge due to its worldwide continuous coverage and relatively low costs. Vegetation Optical Depth (VOD), a remote sensing method, is a measure of above-ground vegetation water content based on passive microwaves, which are not affected by atmospheric distortion. The present research aims at analysing the potential of VOD as an agricultural drought indicator. A case study on pepper fields in Indonesia was conducted using daily VOD data between 2012 and 2018 at a downscaled spatial resolution of 100 x 100 m. Correlations between VOD anomalies and anomalies of other meteorological drought indicators were calculated. VOD was found to be correlated with the Standardized Precipitation Evaporation Index (SPEI) by 0.47, with the El Niño Southern Oscillation (ENSO) by -0.46 and with the Indian Ocean Dipole (IOD) by -0.46. These correlations indicate that other factors influence the VOD, which should be considered as an independent dataset. This study provides evidence that VOD is capable of capturing agricultural droughts. A cross-comparison was conducted with the Normalized Difference Vegetation Index (NDVI), which is often compared to VOD as an alternative measure. Results in the investigated area suggest that, although VOD has a lower spatial resolution, it performs better than NDVI. Finally, a roadmap was proposed towards developing a drought indicator based on the VOD anomalies. This roadmap aims at modelling future VOD based on the relation between SPEI and VOD by using the added value of VOD that this study reveals. This study is especially relevant for other tropical areas with a high cloud density.

The growing world population and climate change have resulted in increasing stress on the Earth’s ecosystems. Especially the overexploitation of agricultural land, overgrazing and extreme droughts have resulted in land degradation, often called desertification. This leads to the disappearing of natural vegetation, the loss of soil quality, lower production capacity in agriculture and increased water scarcity, which is most severe on drylands like the African continent. Globally, organizations such as the United Nations Convention to Combat Desertification (UNCCD) are fighting land degradation by implementing sustainable farming practices and supporting re-vegetation projects such as the Green Wall Initiative in Africa, often using ancient water harvesting techniques. Justdiggit, a Dutch NGO, is currently working on land restoration projects and dug over 72,000 water-retaining semi-circular bumps, or ’bunds’, in Kenya. By retaining rainwater in these bunds, water is given the time to infiltrate into the soil, erosion rates by overland flow are reduced, while vegetation recovers and on the long termcan take over the function of the bunds. Although the first results of the bunds at small scale are promising, the impact of the projects of Justdiggit has never been quantified on a large scale in terms of the amount of water that’s retained, increase of vegetation and decrease of surface temperature.

As dense in-situ networks are expensive and have difficulties in capturing the large spatial variability of parameters such as soil moisture and temperature, often remote sensing (satellite observation) is used, which does well in measuring spatial variability on large scales. However, as optical remote sensing is often affected by cloud cover, data availability is limited. Furthermore, it is difficult to translate optical vegetation parameters such as the Normalized Difference Vegetation Index (NDVI) to vegetation biomass, as these only observe the surface of the canopy. Passive microwave remote sensing, which is measured in the microwave part of the electromagnetic spectrum, has proven to be very accurate in determining parameters such as soil moisture content, surface temperature and vegetation optical depth (VOD, related to vegetation thickness and water content). The advantage of using microwaves is that it can ’see’ through clouds and can therefore be used under almost all atmospheric circumstances. However, due to the low intensity of passive microwaves, resolutions are low (¼ 50x30km). VanderSat, a Dutch remote sensing company, downscales the low-resolution passive microwave observations to 100x100m field-scale resolution. The goal of this research is therefore to test if the project areas of Justdiggit can be monitored using these downscaled passive microwave remote sensing soil moisture, surface temperature (night-time) and vegetation optical depth products of VanderSat.

Using in-situ data from sensors that were installed inside and outside the bunds during a fieldwork in Kenya in March 2018, the hydrological behaviour of the bunds was investigated. Furthermore, a cross-validation was carried out to compare the downscaled passive microwave soil moisture, surface temperature (nighttime) and VOD products to the in-situ sensors, thermal derived surface temperatures (day- and night-time) from MODIS and NDVI fromSentinel-2 and MODIS. After these cross-comparisons, the differences between the project areas of Justdiggit and the reference area were analysed using the downscaled passive microwave products. Finally, using the Tau-Omega model, the passive microwave signal from the bunds was simulated.

All in all, the analysis of the results have shown that the bunds have a significant effect on the soil moisture content, especially at the deeper soil layers, based on the in-situ sensors, while results of NDVI have shown a clear contrast in vegetation ’greenness’ between the project areas and the reference area. Due to the short measuring period, the correlations between the in-situ sensors and the downscaled soilmoisture and surface temperature products were still low. Furthermore, highest correlations were found between the downscaled surface temperature product and the night-time surface temperatures of MODIS. As it turned out, no significant differences were found between the project areas and the reference area based on the downscaled passive microwave products. This, in spite of the fact that the soil moisture, surface temperature and VOD products have shown to be consistent. Finally, modelling of the microwave signal of the project areas, has shown that the effect of the bunds is still too small in terms of emissivity and is therefore not being measured yet. Increasing the current percentage of bunds from10 to roughly 20%, or waiting five years after implementation of the bunds in 2016, should give significant differences in terms of soil moisture, surface temperature and vegetation optical depth, as a result of vegetation development and land restoration in the project areas.